Coinrule MCP: Build AI Trading Agents Through Natural Language

Last updated July 22, 2026

AI is changing the way traders interact with financial markets. Instead of relying exclusively on dashboards, configuration panels, charts, and manually constructed automation, traders can increasingly use AI assistants as an intelligent interface between a trading idea and the infrastructure required to analyse, test, automate, and monitor it.

Coinrule MCP https://coinrule.com/mcp/ brings this conversational approach to automated trading.

By connecting a compatible AI assistant such as ChatGPT, Claude, Gemini, Grok, or another MCP-compatible client to a Coinrule account, traders can use natural-language instructions to inspect portfolios, analyse running strategies, explore trading ideas, perform historical backtests, create automated strategies, monitor signals, and manage trading agents.

The objective is not simply to add a chatbot to a trading platform. It is to create a more intelligent layer for interacting with algorithmic trading infrastructure.

Instead of navigating through multiple interfaces to answer questions such as:

Which of my strategies generated the latest signal?

or:

How is my portfolio currently allocated across connected venues?

a trader can ask an AI assistant directly.

More advanced workflows can go further:

Design a momentum strategy for BTC that only enters when the broader trend is positive, include defined risk controls, explain the logic, and backtest the configuration before I consider running it.

The AI assistant interprets the intention behind the request and uses the authorised Coinrule MCP tools required to perform the relevant operations. Coinrule continues to provide the strategy, execution, validation, portfolio, and market-connectivity infrastructure underneath the conversation.

This creates a new model for AI trading: the trader defines the objective, the AI helps translate that objective into structured actions, and Coinrule provides the controlled automation layer through which those actions are executed.

[!WARNING] A Read + Write connection can launch strategies that trade real funds. Start with Read-only access or paper trading, review every request, and make the venue and capital amount explicit.

Connect in under a minute

Choose a compatible client such as Claude, ChatGPT, Grok, Cursor, or VS Code.
Add this remote MCP server:
text
```
https://cloud.coinrule.com/mcp
```
Sign in to Coinrule when your client opens the OAuth window.

Choose Read-only or Read + Write, then try:

text

Show my Coinrule bots and how each one is doing.

You need a Coinrule Cloud account and an AI client that supports remote MCP connectors with OAuth. Connect an exchange or broker to trade live, or use paper trading without one.

Agent Plugin

This repository is packaged as an Agent Plugins 1.0.0 plugin for compatible clients and directories, including Cursor:

plugin.json contains portable plugin metadata.
mcp.json declares the hosted Coinrule MCP server using the Streamable HTTP transport.
server.json contains the official MCP Registry metadata.

Agent Plugins 1.0.0 intentionally leaves OAuth to the client. When the plugin connects to https://cloud.coinrule.com/mcp, the client discovers Coinrule's OAuth 2.1 endpoints and asks the user to authorize Read-only or Read + Write access.

From Trading Bots to AI Trading Agents

Traditional trading automation usually begins with configuration.

A trader chooses an asset, selects an indicator, defines an entry condition, configures an exit, chooses a position size, and decides where the strategy should run.

This approach can be effective, but it requires the trader to translate an investment or trading thesis into the structure expected by the software.

AI trading introduces a different interaction model.

Instead of beginning with a configuration screen, the process can begin with intent.

For example:

I want to investigate whether short-term weakness in Ethereum creates attractive entries when the longer-term trend remains bullish.

An AI assistant can help break this idea into questions:

How should short-term weakness be measured?
How should the broader trend be defined?
Which timeframe should be used?
What conditions invalidate the setup?
How much capital should be allocated?
How frequently should the strategy be allowed to trade?
What stop-loss or exit logic should apply?
How would the strategy have behaved historically?

The conversation can then move from an abstract idea toward a structured trading strategy.

With Coinrule MCP, the AI assistant can interact with authorised Coinrule functionality during this process. Depending on the permissions granted, that may include reviewing portfolio information, examining existing strategies, creating strategies, running backtests, launching baskets, or managing trading automation.

The important distinction is that the language model is not replacing the trading infrastructure.

The AI assistant acts as the conversational and reasoning interface. Coinrule remains the underlying system responsible for connecting strategies with supported exchanges, brokers, assets, portfolio data, and automation functionality.

What Is Coinrule MCP?

MCP stands for Model Context Protocol.

It is an open protocol designed to allow compatible AI applications to interact with external tools and services.

Coinrule operates an official remote MCP server that allows supported AI assistants to connect to Coinrule Cloud. Once the connection has been authorised, the assistant can use the Coinrule tools available within the permission scope selected by the user.

From the trader's perspective, the interaction remains conversational.

You might ask:

Show me my active Coinrule strategies and summarise their recent activity.

The AI assistant determines which authorised tools are required, requests the information through Coinrule MCP, and then presents the results in a conversational format.

You do not necessarily need to know the names of the underlying tools or understand Coinrule's internal API structure.

This abstraction is one of the most significant aspects of AI-assisted trading.

The trader can concentrate on what they want to understand or accomplish, while the AI assistant handles much of the translation between natural language and structured software operations.

Coinrule MCP is therefore not a separate exchange, broker, trading account, or custody solution. It is another interface through which authorised Coinrule functionality can be accessed.

Strategies created or managed through an AI assistant remain part of the broader Coinrule trading environment rather than existing in a disconnected AI-only system.

How AI Trading Through Coinrule MCP Works

A typical MCP interaction can be understood as a four-layer process.

1. The Trader Defines an Intention

You begin by describing a task in natural language.

For example:

Analyse my current strategies and identify which ones have produced signals during the last 24 hours.

Or:

Help me design a trend-following strategy for BTC using a moving-average filter and a volatility-based risk condition.

The prompt can be simple or highly detailed.

2. The AI Assistant Interprets the Request

The language model analyses the instruction and determines what information or actions may be necessary.

For an account-analysis request, it may need portfolio or strategy information.

For strategy development, it may need to construct and validate a configuration.

For historical analysis, it may need to run or retrieve a backtest.

3. Coinrule MCP Provides Authorised Tools

The assistant can call only the Coinrule functionality permitted by the user's connection.

A Read-only connection is intended for information retrieval and analysis.

A Read + Write connection provides additional capabilities for supported strategy creation and management actions.

4. Coinrule Handles the Underlying Trading Infrastructure

Coinrule remains the operational layer responsible for the relevant strategy, account, portfolio, backtesting, and market-connectivity functionality.

The AI assistant becomes an interface to that infrastructure rather than receiving unrestricted access to the trading environment.

This separation matters.

An AI model may be effective at interpreting objectives and helping users reason about strategies, but trading actions still need structured infrastructure, authentication, permissions, validation, and execution controls.

Coinrule MCP is designed to connect these two layers: AI reasoning and trading automation.

The AI Trading Lifecycle

One of the most useful ways to understand Coinrule MCP is to look at the complete lifecycle of an AI-assisted trading strategy.

Instead of viewing AI trading as a single prompt that immediately produces a trade, a more disciplined workflow can involve multiple stages.

1. Understand the Current Portfolio

Before creating another strategy, an AI assistant can help you understand what is already running.

With appropriate read permissions, you may be able to ask questions about balances, holdings, open positions, strategies, performance, and recent trading activity.

For example:

Summarise my current Coinrule portfolio and identify my largest asset exposures.

Or:

Show my active strategies, their current status, and the most recent signal generated by each one.

This turns the AI assistant into a conversational layer for portfolio intelligence.

Rather than manually moving between individual accounts and strategies, the trader can begin with a broader question and progressively investigate the areas that matter.

A conversation might evolve like this:

Which strategy currently has the largest open exposure?

Then:

What conditions caused that strategy to enter?

Then:

Show me its recent activity.

Then:

Compare its behaviour with my other momentum strategies.

This ability to move from a high-level portfolio question into increasingly detailed analysis is an important characteristic of conversational AI trading.

2. Express the Trading Thesis in Natural Language

Most trading strategies begin as ideas rather than software configurations.

Examples might include:

Buy temporary weakness during a long-term uptrend.
Reduce exposure when market volatility rises significantly.
Accumulate an asset periodically rather than attempting to time every entry.
Follow strong momentum but exit when the trend deteriorates.
Rebalance a portfolio when allocations move outside predefined ranges.

An AI assistant can help convert these ideas into more precise specifications.

For example:

I want to buy significant dips in BTC, but only when the longer-term trend remains positive.

The assistant can help identify the missing variables.

What constitutes a significant dip?

What defines the long-term trend?

How frequently can the strategy enter?

Should the strategy scale into positions?

Where should risk be limited?

When should profits be taken?

The advantage of this approach is that strategy creation can become an iterative conversation rather than a one-time configuration exercise.

The trader can refine the strategy until its logic is explicit enough to validate and test.

3. Translate the Idea Into Systematic Rules

AI trading becomes considerably more useful when an idea can be converted into deterministic rules.

A systematic strategy might include:

Market or asset selection
Entry conditions
Exit conditions
Technical indicators
Timeframes
Position sizing
Capital allocation
Maximum trading frequency
Cooldown periods
Stop-loss logic
Take-profit logic
Portfolio exposure limits

An AI assistant can act as a bridge between qualitative thinking and quantitative structure.

The goal is not to let the AI produce vague instructions such as:

Buy when the market looks good.

Instead, the conversation should move toward explicit logic.

For example:

Enter when RSI indicates short-term weakness, but only when price remains above a longer-term moving-average trend filter. Limit each position to a defined percentage of available capital and include an exit if the trend condition is no longer valid.

The more precise the strategy becomes, the easier it is to review, test, and monitor.

4. Validate the Strategy Before Execution

An AI-generated trading idea should not automatically be treated as a valid strategy.

AI systems can misunderstand ambiguous requests, select inappropriate assumptions, or generate logic that appears reasonable conversationally but behaves differently when translated into actual trading rules.

Validation is therefore a critical stage.

Before execution, the trader should review questions such as:

Does the resulting strategy match the original intention?
Are the entry conditions explicit?
Are the exit conditions complete?
Is position sizing defined?
Is maximum capital exposure limited?
Could multiple conditions trigger unexpectedly?
Does the strategy behave differently during high volatility?
What happens if the market gaps or moves rapidly?
What happens to existing positions when the strategy is stopped?

A sophisticated AI trading workflow should use the assistant not merely to create strategies, but also to explain them.

For example:

Explain this strategy back to me in plain English before doing anything else.

Or:

Identify the three largest risks or ambiguities in this configuration.

Or:

Describe exactly what would cause an entry, what would cause an exit, and what would happen if the strategy were stopped while a position remained open.

This review step creates a human-in-the-loop process in which AI accelerates strategy development without eliminating trader oversight.

5. Backtest the Trading Logic

Once the strategy has been clearly defined, historical testing can provide additional information.

Coinrule MCP includes functionality related to backtesting and allows compatible assistants to work with historical strategy simulations through authorised tools.

A trader might ask:

Backtest this strategy and summarise the results.

A more analytical request could be:

Compare several versions of this strategy using different entry thresholds and explain how the changes affect trade frequency, drawdown, and overall behaviour.

The purpose of backtesting is not to discover a configuration that appears perfect historically.

Excessive optimisation can produce strategies that fit historical data extremely well but fail under future market conditions.

Instead, backtesting can be used to investigate questions such as:

Did the strategy behave as expected?
How frequently did it trade?
Under what market conditions did it struggle?
Did small parameter changes dramatically alter the results?
Were losses concentrated in particular market regimes?
Did the risk controls behave as intended?

Historical results should be treated as evidence about past behaviour, not a guarantee of future profitability.

6. Consider Paper Trading or Controlled Testing

Before deploying AI-generated automation with real capital, a trader may prefer to test the workflow in a simulated environment.

This is particularly useful when learning how conversational strategy management works.

A controlled process could be:

Describe the strategy.
Ask the AI assistant to formalise the rules.
Review the resulting logic.
Validate the configuration.
Run historical tests.
Test the strategy using paper trading where available.
Monitor how the strategy responds to live market conditions.
Review signals and activity.
Refine the configuration.
Consider live deployment only after independent review.

This approach treats AI as a strategy-development and automation tool rather than assuming that every AI-generated idea should immediately control real capital.

7. Launch and Monitor the Trading Agent

Once a strategy has been reviewed and the appropriate permissions are available, Coinrule MCP can support workflows that extend beyond strategy creation.

The AI assistant can become an ongoing interface for monitoring the trading automation.

You might ask:

Which of my strategies generated signals today?

Or:

Summarise the recent activity across all my running strategies.

Or:

Compare the recent performance of my momentum and mean-reversion strategies.

The assistant can help transform raw trading activity into a more understandable summary.

This is where the concept of an AI trading agent becomes particularly relevant.

The conversational workflow can continue across the strategy's lifecycle:

Idea → Definition → Validation → Backtest → Deployment → Monitoring → Review → Adjustment

What Can an AI Trading Assistant Do With Coinrule MCP?

The precise functionality available depends on the authorised connection and supported Coinrule tools, but the MCP workflow can cover several important areas.

For the complete, scope-marked list of tools exposed by the hosted server, see the tools reference.

Portfolio Intelligence

A connected assistant can help retrieve and summarise supported account information.

This may allow traders to investigate:

Balances
Holdings
Portfolio exposure
Open positions
Connected accounts
Recent strategy activity
Signals
Performance information

Instead of treating portfolio monitoring as a collection of dashboards, traders can investigate their accounts conversationally.

For example:

Give me a high-level summary of my portfolio.

Then:

Which positions contribute the most exposure?

Then:

Which automated strategies are responsible for those positions?

This creates a more exploratory approach to portfolio analysis.

Strategy Analysis

An AI assistant can also act as a conversational interface for understanding existing trading automation.

Questions might include:

Which strategies are currently active?

Which strategy most recently opened a position?

Summarise the activity of my strategies over the last day.

Which strategies have not generated signals recently?

This can be particularly useful for traders operating multiple automated systems simultaneously.

Rather than reviewing each system independently, the AI assistant can help organise the information around the trader's question.

AI-Assisted Strategy Creation

With the required permissions, natural-language descriptions can become the starting point for new automated strategies.

For example:

Create a conservative DCA concept that increases purchases during larger market declines but limits total portfolio exposure.

Or:

Design a trend-following strategy that requires confirmation from both momentum and volume before entering.

The trader can then continue refining the strategy conversationally.

Add a cooldown period.

Reduce the maximum capital allocation.

Explain when the strategy would stop entering new positions.

Backtest the revised version.

The important advantage is iteration.

Strategy development becomes less about completing a configuration form correctly on the first attempt and more about progressively improving the specification through dialogue.

Backtest Analysis and Comparison

AI assistants can also help make historical testing more conversational.

Instead of simply generating a result, the assistant can be asked to interpret differences between scenarios.

For example:

Compare a conservative and aggressive version of this strategy.

Or:

Test three different entry thresholds and explain how each changes the number of trades.

Or:

Which parameter appears most sensitive to small changes?

The AI layer can help traders reason about backtest results rather than merely viewing them.

However, any interpretation remains subject to the limitations of historical simulation.

Backtests can be affected by market regime, data quality, parameter selection, execution assumptions, and overfitting.

They should therefore be treated as analytical tools rather than predictions.

Strategy Templates and Starting Points

Not every trading strategy needs to begin from a blank page.

Coinrule's ecosystem includes predefined trading strategies and templates that can be used as starting points.

An AI-assisted workflow could therefore begin with exploration.

For example:

Find a strategy template related to dollar-cost averaging and explain its basic logic.

Or:

Show me approaches that could be relevant to momentum trading, then compare their assumptions.

The trader can select a starting point and use conversation to modify it.

This can reduce the distance between discovering a strategy concept and adapting it to a specific objective.

Multi-Asset Trading and Baskets

AI trading is not limited to individual buy and sell decisions.

Coinrule MCP can also support workflows involving diversified groups of assets and target allocations.

A trader might describe an allocation concept in natural language and then refine it.

For example:

Create a diversified basket concept using several assets with defined target weights.

Then:

Explain the concentration risk.

Then:

Show how the allocation changes if I reduce the largest position.

This makes AI useful not only for individual trading signals but also for portfolio construction and systematic allocation workflows.

AI Trading Requires Risk Controls

The phrase AI trading agent can imply autonomy, but responsible automation requires boundaries.

An effective trading agent should operate within clearly defined rules and permissions.

That means separating several concepts:

Analysis

The AI can inspect information and help the trader understand what is happening.

Recommendation or Strategy Design

The AI can help formulate, explain, or compare potential trading logic.

Validation

The strategy can be reviewed and tested before execution.

Execution

The system can perform authorised actions when the appropriate permissions are available.

These stages should not automatically be treated as interchangeable.

A trader may be comfortable using AI for portfolio analysis without granting the ability to create or manage strategies.

Another trader may use AI for strategy creation but still manually review every configuration before deployment.

The appropriate level of automation depends on the user's objectives and risk tolerance.

Read-Only vs Read + Write Access

Coinrule MCP uses OAuth 2.1 scopes to control what a connected assistant can do.

Access level	OAuth scope	Your assistant can do
Read-only	`coinrule:read`	Inspect strategies, balances, holdings, trades, signals, PnL, and saved backtests. It cannot place or change trades.
Read + Write	`coinrule:read` + `coinrule:write`	Everything in Read-only, plus create, launch, update, start, and stop strategies; launch baskets; and run backtests.

With a Read-only connection, the assistant can access supported information without receiving the write capabilities required to create or modify trading automation. This is often the appropriate starting point for users who primarily want conversational portfolio analysis or who are exploring MCP functionality.

With Read + Write access, supported management and trading-automation tools become available. This creates substantially greater capability, but also greater responsibility. When write permissions are connected to live trading infrastructure, instructions may ultimately affect real capital.

Users should therefore verify the requested action before allowing consequential changes.

Security and the Architecture of AI Trading

Connecting an AI assistant to a trading platform raises an important question:

What does the AI actually have access to?

Coinrule MCP uses an OAuth-based permission model in which the AI assistant receives authorised, scoped access rather than direct access to the user's Coinrule password or exchange API keys.

Conceptually, the architecture can be understood as:

Trader

↓

AI Assistant

↓

Authorised MCP Tools

↓

Coinrule Trading Infrastructure

↓

Connected Markets and Accounts

This separation is important because an AI trading system should not rely on giving a language model unrestricted access to sensitive account credentials.

Instead, the assistant operates through defined tools and permissions.

Users should still regularly review their connected applications and revoke access that is no longer required.

How to Connect Coinrule MCP

The exact interface varies depending on the AI assistant, but the connection uses the same official remote server.

Before You Begin

You will generally need:

A Coinrule Cloud account
A compatible AI assistant or MCP client
Support for remote MCP connections and OAuth authentication
An exchange or broker connection when live execution is required

Add Coinrule to your client

Client	How to connect	Help
Cursor	Use the button above, or add the remote server from `examples/cursor-mcp.json`.	Cursor configuration
VS Code	Use the button above, or add the server to `.vscode/mcp.json` from `examples/vscode-mcp.json`.	VS Code configuration
Claude	In Claude's connector settings, add a remote MCP connector and paste the server URL.	Connect Claude
ChatGPT	Go to Settings → Apps / Connectors, add a custom connector, and paste the server URL.	Connect ChatGPT
Grok	In a Grok client with remote MCP support, add a custom tool or remote MCP server and paste the URL.	Connect Grok
Another MCP client	Use the client’s remote-MCP configuration and adapt `examples/mcp-generic.json` if it uses an `mcpServers` map.	General connection guide

OAuth starts when the client first connects. Do not add a Coinrule password or exchange API key to an MCP configuration file.

ChatGPT invalid_client? Remove the Coinrule connector and add it again from scratch. This refreshes the client registration. See the ChatGPT guide or troubleshooting.

Connect manually

Open the area where the AI application allows external tools, apps, connectors, integrations, or remote MCP servers to be added.
Add Coinrule's official MCP server endpoint:
text
```
https://cloud.coinrule.com/mcp
```
Complete the Coinrule OAuth flow and choose the minimum permission level needed for your use case.
Begin with a low-risk read operation, such as:

List my Coinrule strategies.

This confirms the connection before you attempt more sophisticated workflows.

How to Prompt an AI Trading Agent Effectively

The quality of an AI-assisted trading workflow depends heavily on the quality of the instructions.

A vague request leaves important variables open to interpretation.

For example:

Build me a profitable Bitcoin strategy.

This request does not specify:

Trading timeframe
Capital allocation
Market venue
Risk tolerance
Entry conditions
Exit conditions
Position sizing
Maximum loss
Strategy frequency
Whether trading should be live or simulated

A more useful instruction would be:

Help me design a BTC trend-following strategy for research purposes. Use a four-hour timeframe, define an objective trend filter, limit individual positions to a predefined percentage of capital, include explicit entry and exit conditions, and explain every rule before running a historical backtest. Do not launch anything live.

The second prompt establishes boundaries.

That is especially important when an assistant has write permissions.

Useful Information to Include in AI Trading Prompts

When relevant, specify:

Objective

What are you trying to achieve?

Trend following, accumulation, mean reversion, diversification, risk reduction, or another goal?

Asset

Which cryptocurrency, stock, ETF, or market should the strategy consider?

Venue

Which connected exchange or broker should be used?

Timeframe

Is the strategy operating over minutes, hours, days, or longer periods?

Entry Logic

What conditions must be satisfied before entering?

Exit Logic

What conditions should close or reduce the position?

Capital Allocation

How much capital may the strategy use?

Position Sizing

How large can each individual trade become?

Risk Controls

Should the strategy include a stop loss, allocation cap, cooldown period, maximum number of entries, or other limits?

Testing Requirements

Should the strategy be validated, backtested, or paper traded before any live action?

Execution Permission

Are you requesting analysis only, strategy creation, simulation, or actual deployment?

Explicitly stating this can help reduce ambiguity.

Example AI Trading Prompts

Portfolio Analysis

Review my Coinrule portfolio, summarise my largest exposures, list my active strategies, and highlight any recent signals. Do not make any changes.

Strategy Investigation

Explain what my currently running momentum strategy is designed to do, summarise its recent activity, and identify its main risk controls.

Strategy Design

Design a rule-based ETH momentum strategy using objective technical conditions. Include a trend filter, an entry condition, an exit condition, position-sizing limits, and a cooldown mechanism. Explain the complete logic before creating anything.

Backtest Comparison

Compare three versions of this strategy using conservative, moderate, and aggressive entry thresholds. Run historical tests and summarise differences in trade frequency and risk characteristics. Do not launch a live strategy.

Risk Review

Review this proposed strategy and identify ambiguous instructions, potential overtrading risks, concentration risks, and any conditions that could lead to unexpectedly large exposure.

Strategy Monitoring

Summarise all signals generated by my running strategies during the last 24 hours and explain which strategies were most active.

Basket Research

Help me design a diversified multi-asset basket. Explain the reasoning behind each target allocation and identify where concentration risk could arise before creating the basket.

These examples illustrate an important principle:

The best AI trading prompts describe both the objective and the boundaries.

Find more copy-paste examples in docs/PROMPTS.md.

Best Practices for AI-Assisted Trading

Begin With Observation

Before allowing an AI assistant to manage strategies, use it to inspect your portfolio and understand your existing automation.

This provides an opportunity to learn how the assistant interprets your requests.

Use the Minimum Necessary Permissions

If analysis is sufficient, use Read-only access.

Expand permissions only when additional functionality is genuinely required.

Separate Research From Execution

Be explicit about whether you are asking the AI to:

Explain an idea
Design a strategy
Validate a configuration
Run a backtest
Create a strategy
Launch a strategy

These are different actions.

Require Explanations

Before executing a consequential strategy, ask the assistant to explain exactly what the strategy will do.

Define Risk Before Entry

Risk controls should be part of the initial strategy design rather than an afterthought.

Test Before Going Live

Backtesting and paper trading can help expose problems before real capital is involved, although neither can guarantee future results.

Monitor Running Automation

An automated strategy should still be reviewed.

Use the AI assistant to investigate signals, positions, performance, and unexpected activity.

Treat AI Output as Fallible

Language models can misunderstand instructions and generate incorrect analysis.

AI should therefore accelerate the trading workflow without eliminating independent judgement.

Who Is Coinrule MCP For?

Coinrule MCP may be useful for several types of traders.

Traders Managing Multiple Automated Strategies

An AI assistant can provide a single conversational interface for investigating strategy activity.

Traders Who Prefer Natural Language

Users who understand their trading objective but do not want every interaction to begin with dashboard navigation may find conversational control more intuitive.

Strategy Researchers

AI can help turn an informal hypothesis into a more structured strategy specification that can then be reviewed and tested.

Systematic Traders

Users already familiar with rule-based trading can use AI to accelerate strategy iteration, comparison, monitoring, and management.

Portfolio-Focused Traders

Conversational portfolio queries and multi-asset basket workflows can help users reason about exposure at a broader level.

Traders Exploring Agentic AI

For users interested in AI agents, MCP creates a practical connection between language-model reasoning and authorised trading tools.

The common theme is not complete autonomy.

It is the ability to use AI as a more intelligent interface for systematic trading infrastructure.

Coinrule MCP and the Future of Agentic Trading

The long-term significance of MCP extends beyond convenience.

Trading platforms have traditionally required humans to learn the software's interface.

Users adapt their thinking to menus, forms, dashboards, and configuration structures.

Conversational AI reverses part of that relationship.

The trader begins with an objective.

The AI interprets the objective.

External tools provide the capabilities required to act on it.

The result is an emerging form of agentic trading, where AI assistants can participate throughout the lifecycle of a systematic strategy while operating through defined permissions and trading infrastructure.

A future AI trading workflow may increasingly resemble a conversation:

What is happening in my portfolio?

Which strategy is responsible?

Why did it enter?

How would the strategy behave with a tighter risk limit?

Compare the alternatives.

Backtest the revised version.

Explain the results.

Prepare the strategy for review.

The interface changes from a collection of isolated actions into a continuous reasoning process.

The opportunity is not simply to make trading faster.

It is to make the relationship between human intent, artificial intelligence, systematic rules, and automated execution more direct.

Frequently Asked Questions

Is Coinrule MCP an AI Trading Bot?

Not exactly.

Coinrule MCP is the connection layer that allows compatible AI assistants to interact with authorised Coinrule trading functionality.

The AI assistant provides the conversational interface, while Coinrule provides the underlying strategy and automation infrastructure.

Can I Use AI to Analyse My Coinrule Account Without Allowing It to Trade?

Yes.

A Read-only permission level allows the assistant to inspect supported account and strategy information without granting write access for strategy-management actions.

Can an AI Assistant Create Trading Strategies?

With the appropriate tools and permissions, Coinrule MCP can support strategy creation and management workflows through natural-language instructions.

Can I Backtest Strategies Through an AI Conversation?

Yes. Backtesting can be included in the conversational strategy-development workflow, allowing the user to move from idea generation to historical testing and comparison.

Can Coinrule MCP Work With Different AI Assistants?

Coinrule MCP is designed for compatible AI clients that support remote MCP connections.

Does the AI Assistant Receive My Coinrule Password or Exchange API Keys?

The connection uses scoped authentication rather than giving the AI assistant direct access to sensitive account credentials such as your Coinrule password or exchange API keys.

Can AI-Generated Strategies Lose Money?

Yes.

AI-generated trading strategies can contain errors, rely on incorrect assumptions, behave differently from expectations, or perform poorly when market conditions change.

Trading itself involves significant risk.

Neither artificial intelligence, trading automation, historical backtests, nor simulations guarantee profitable results.

Should I Let an AI Trading Agent Operate Autonomously?

That is a risk and permission decision for the individual user.

A more controlled approach is to begin with Read-only access, use explicit prompts, independently review strategies, test configurations, define strict risk parameters, and understand every consequential action before permitting live execution.

Supported venues and assets

Use Coinrule MCP with the accounts you have connected to Coinrule, including:

Binance
Coinbase
Kraken (spot and perpetuals)
Hyperliquid
KuCoin
Trading 212

Supported asset types include crypto spot, perpetuals, and stocks, with paper-trading equivalents. See exchanges and connections for current venue support and setup instructions.

Distribution

This repository contains a standards-based server.json manifest for the hosted Streamable HTTP server. It is ready to support publication in the official MCP Registry; registry publishing and namespace verification are separate release steps.

Coinrule MCP Resources

This repository and the Coinrule MCP are provided by Coinrule, AI-powered trading automation backed by Y Combinator.

Risk notice: AI-generated analysis, automated trading strategies, and algorithmic systems can contain errors and may produce unexpected results. Trading cryptocurrencies, stocks, ETFs, derivatives, and other financial instruments involves risk and may result in loss of capital. Backtests, simulations, hypothetical results, and past performance do not guarantee future outcomes. Review all strategies independently, understand the permissions granted to connected AI assistants, and carefully verify consequential actions before using real funds. Nothing in this article constitutes financial, investment, or trading advice.