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  1. Home
  2. πŸ’° Finance & Fintech
  3. Chart Library MCP
Chart Library MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 1:19:53 PM

Chart Library MCP

User RatingsBe the first to rate and review this MCP server!
View Repository20 GitHub StarsTotal stargazers on GitHub for the source repository (20 stars).Visit Website
financestockschart-patternscohort-intelligencefinancial-analysis

Provides historical stock chart pattern search and analysis with 25M+ embeddings across 19K stocks and 10 years of data.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "grahammccain-chart-library-mcp": {
      "command": "uvx",
      "args": [
        "chartlibrary-mcp"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’° More in Finance & Fintech

Overview

This MCP server offers a cohort intelligence engine for stock chart patterns, enabling AI agents to find historical analogs to specific stock ticker-date patterns and analyze their forward returns and distinguishing features. It supports searching by ticker and date or by screenshot, returning empirical distributions of outcomes without forecasts or hallucinations. Use it to integrate historical pattern context, regime analysis, and trade simulation into financial AI applications.

Use cases

β€’Search historical analogs for a stock chart pattern by ticker and date
β€’Analyze forward return distributions and win rates for cohorts
β€’Identify features that differentiate winning from losing patterns
β€’Simulate trade outcomes based on historical cohorts
β€’Assess market regime and sector rotation signals

Key features

β€’25M+ precomputed pattern embeddings across 19K+ stocks
β€’10 years of historical stock data
β€’19 tools including pattern search, forward returns, regime analysis, and trade simulation
β€’Supports search by ticker+date or screenshot
β€’Provides empirical forward return distributions and cohort scoring
β€’Compatible with multiple MCP clients and supports remote HTTP/SSE endpoints

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Chart Library MCP.

Extracted Tool Capabilities
25M+ precomputed pattern embeddings across 19K+ stocks
10 years of historical stock data
19 tools including pattern search, forward returns, regime analysis, and trade simulation
Supports search by ticker+date or screenshot
Provides empirical forward return distributions and cohort scoring
Compatible with multiple MCP clients and supports remote HTTP/SSE endpoints

Documentation Overview

Chart Library MCP

Market memory for AI, with published research for agents and people.

Version 6.3.3 marks failed public tool calls as MCP errors while preserving their structured recovery details. Invalid requests explain how to correct the inputs; stale publication versions retain instructions to restart from the current overview. Successful response schemas and direct Python calls remain compatible.

Five read-only tools

ToolInputResult
market_stateSymbol; optional dateCompleted-session state, historical analogs, outcome ranges, transition memory and tape
daily_noteOptional datePublished daily research, selection rule and settled-note tally
research_qualityNonePublished calibration receipt, dated sample and qualifications
search_researchQuery; optional kind, limit and offsetRelevant publications, IDs, findings, sample receipts, limitations and versions
read_researchResearch ID; optional section, offset and versionOverview or an exact source document, with pagination and hashes

Market-state requests need no preliminary search or cohort handle. For published research, search for an ID, then read its evidence.

Example question: β€œRead AAPL's latest completed-session state. Report the historical analog ranges, sample sizes, session date and limitations.”

Omitting date uses the latest built/published session, not a real-time quote. Dates use YYYY-MM-DD. Missing or thin evidence stays missing or thin.

Connect

The primary remote endpoint is https://chartlibrary.io/mcp, using Streamable HTTP and no authentication.

For a command-based client, install or upgrade the package:

sh
python -m pip install --upgrade chartlibrary-mcp

Then use the equivalent of this configuration in your MCP client:

config.json
{
  "mcpServers": {
    "chartlibrary": {
      "command": "chartlibrary-mcp"
    }
  }
}

No API key or server-side Python packages are required. An optional CHART_LIBRARY_API_KEY is forwarded as a Bearer token when set. An empty key does not send an Authorization header. CHART_LIBRARY_API_URL can select your own compatible endpoint; only point it at a server you trust with that key.

Python 3.10+ is required. This release uses the MCP 1.x FastMCP interface and pins mcp>=1.28.1,<2.0.0; MCP 2.x is a separate migration.

The equivalent HTTP calls

Terminal
curl "https://chartlibrary.io/api/v1/state-packet?symbol=AAPL"
curl "https://chartlibrary.io/api/v1/daily"
curl "https://chartlibrary.io/api/v1/calibration"

Use only the call relevant to the question. For a historical state add &date=YYYY-MM-DD. Daily REST requests call the date parameter session.

Research workflow

python
search_research(query="IONQ noon", kind="casebook")
read_research(research_id="casebook:ionq-noon", section="article",
              version="FULL_VERSION_FROM_SEARCH")

Replace FULL_VERSION_FROM_SEARCH with the full 64-character version from the chosen search result. Updated servers supply documents[section].read_arguments directly; copy those into the tool and follow content.next_read until null. With earlier servers, pass the returned version and next_offset explicitly. Omitting the optional version reads the current publication without checking it against earlier responses. A changed version returns an error explaining how to restart from the current overview; the client does not retry automatically.

Search summaries are discovery metadata, not a source read. Read available guides and relevant limiting findings before making claims. Keyword search ranks document relevance, not market similarity or evidence strength. A partial result means a publication source was unavailable. Withdrawn articles cannot be read. Publication dates and market cutoffs differ; the noon IONQ case must not be merged with completed-session state analogs.

Complete IONQ example Β· Agent research plan

Read the evidence accurately

  • Preserve dates, sample sizes, informative receipts, warnings and provenance.
  • State-packet excess returns are percentage-point observations relative to a date-matched liquid-stock baseline. Each horizon has its own observed n.
  • Raw historical percentiles are not automatically calibrated forecasts.
  • research_quality audits only the method and population its receipt names. Do not transfer its coverage percentage to every market state or all research.
  • Daily research has its own selection rule and settled-note tally.
  • Historical frequencies are not recommendations to buy or sell.

Research access is free. Service limits and underlying data terms still apply. Honor HTTP 429 and retry guidance; do not assume unlimited throughput.

Existing integrations

Existing tool names stay registered and callable, including pull_comps, search, state_packet, and the cohort inspection tools. To discover the extended menu in a local client, set CHART_LIBRARY_MCP_PROFILE=advanced. This changes discovery, not authorization or access to private research.

The public menu does not expose private fund operations or every experimental state-memory method. Legacy examples in examples/ use the advanced interface.

Build with us

For a custom integration, a larger study, or a product built on the memory, contact Graham McCain β€” graham@chartlibrary.io.

Developer guide Β· Methodology Β· Data terms Β· Privacy

Local verification

sh
python -m pytest test_vendor_import.py

The import smoke runs without an API key and without application services/ or db/ packages. No package or registry publication is performed by tests.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
20
Stargazers on the source repository.
Last commit
3mo ago
Most recent push to the default branch.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet β€” be the first to share how this listing worked for you.

Frequently Asked Questions about Chart Library MCP

No, it provides retrieval of historical analog patterns and empirical forward return distributions without making forecasts or predictions.

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Technical Specs & Signals

CategoryπŸ’°Finance & Fintech
PricingFreemium
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
AuthAPI key
LicenseMIT
ClientsClaude Desktop, Cursor, Cline / VS Code
Last updatedAug 9, 2026
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars20
GitHub Star CountTotal stargazers on GitHub representing community popularity (20 stars).
Last commit3mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Jun 10, 2026
51Quality signal: Good Β· 51/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools25/30
Adoption & activity3/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 23d ago via OSV.dev Β· chartlibrary-mcp (PyPI)

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