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  3. MCP Cli
MCP Cli logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 2:33:05 PM

MCP Cli

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View Repository2.0k GitHub StarsTotal stargazers on GitHub for the source repository (2,019 stars).Visit Website
mcpclideveloper-toolsllmtesting

CLI for testing and interacting with MCP servers through chat, direct commands, scripted modes, and tool execution.

Quick Install

Automated & IDE Setup

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

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

Manual Client & Custom JSON ConfigExpand JSON ā–¾

Client Config & Setup

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

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

Install Directory Badge Claim listing AlternativesšŸ› ļø More in Other Tools and Integrations

Overview

chrishayuk/mcp-cli MCP server is a command-line client for interacting with Model Context Protocol servers and their tools. It supports chat, interactive, command, and direct-command modes, with LLM integration through CHUK Tool Processor and CHUK-LLM. Reach for it when you need to test MCP servers, automate tool calls, inspect health, or manage conversations from a terminal; it defaults to local Ollama with the gpt-oss model.

Use cases

•Test MCP server tools from a terminal
•Automate MCP calls in Unix pipelines
•Inspect server health and tool behavior
•Run and resume multi-step tool plans
•Manage local LLM conversations with attachments

Key features

•Chat, interactive, command, and direct-command modes
•Concurrent MCP tool execution
•Session persistence and conversation export
•Virtual-memory conversation management
•Execution plans with checkpointing and resume
•Browser dashboard and MCP Apps support

Capabilities & Tool Schemas

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

Extracted Tool Capabilities
Chat, interactive, command, and direct-command modes
Concurrent MCP tool execution
Session persistence and conversation export
Virtual-memory conversation management
Execution plans with checkpointing and resume
Browser dashboard and MCP Apps support

How MCP Cli works

What chrishayuk/mcp-cli MCP server does

chrishayuk/mcp-cli MCP server provides a terminal interface for connecting to and exercising Model Context Protocol servers. It can discover and invoke server tools directly, or let an LLM select and use tools during a streamed conversation. The project is intended for testing, interactive operation, and scriptable automation rather than for exposing a business-system API of its own.

Four operating styles are described: chat mode for conversational work, interactive mode for command-driven sessions, command mode for Unix pipelines, and direct commands for individual operations. Responses can include Markdown formatting, syntax highlighting, progress indicators, timing information, and tool execution statistics.

How it works

The CLI acts as the orchestration layer between MCP servers, an LLM provider, and the terminal interface. CHUK Tool Processor handles asynchronous tool execution, while CHUK-LLM supplies the unified model-provider layer. The README also identifies CHUK-Term as the terminal UI component.

In chat mode, the client streams model output and can execute multiple tools concurrently while preserving conversation order. Users can interrupt response generation or tool execution with Ctrl+C. Conversation sessions can be saved, loaded, listed, and automatically saved every 10 turns; conversations can also be exported as Markdown or JSON with metadata.

The experimental virtual-memory feature manages conversation context with configurable budgets and modes. Execution plans can represent multi-step tool-call graphs, run independent steps in parallel, support dry runs, persist checkpoints, and resume after interruption. Plans can be created through the /plan command or, when enabled, generated by the model.

Setup and configuration

The default configuration uses Ollama with the gpt-oss reasoning model, allowing local operation without an API key according to the README. Other provider and model behavior is available through the CHUK-LLM integration, but the supplied material does not specify the required provider configuration or installation command.

Runtime behavior can be adjusted with options including --vm, --vm-budget, --vm-mode, --plan-tools, --log-file, --health-interval, --dashboard, and repeatable --attach arguments. Server-specific tool_timeout and init_timeout settings can override global and default timeout values.

Tools and capabilities

The chrishayuk/mcp-cli MCP server includes:

  • Chat, interactive, command, and direct-command modes.
  • Streaming responses, reasoning visibility, concurrent tool calls, and token usage tracking.
  • Image, audio, text, and code attachments through commands, flags, inline file references, or the dashboard.
  • Health checks through /health, with optional background polling and failure diagnostics.
  • Experimental virtual memory inspection through /memory, /vm, or /mem.
  • Plan creation, inspection, execution, deletion, dry runs, checkpointing, and resume operations.
  • Browser-rendered MCP Apps in sandboxed iframes with WebSocket communication.
  • A real-time dashboard with conversation output, tool activity, plan progress, tool discovery, configuration controls, and file uploads.
  • Secret redaction for bearer tokens, API keys, OAuth tokens, and authorization headers in log output.

Limitations and notes

The virtual-memory functionality is explicitly experimental. The material also identifies later roadmap tiers for traces, memory scopes, skills, scheduling, and multi-agent features, so those should not be assumed to be available. The README describes this project as a CLI client; it does not establish that the package itself hosts an MCP server endpoint.

The default local Ollama setup does not require an API key, but using other model providers may require separate credentials or configuration. Exact supported provider setup, package installation instructions, license details, and MCP client compatibility are not provided in the supplied material.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
2k
Stargazers on the source repository.
Last commit
1mo ago
Most recent push to the default branch.
Directory activity
3 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about MCP Cli

The supplied material identifies a PyPI package but does not provide an installation command.

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Technical Specs & Signals

CategoryšŸ› ļøOther Tools and Integrations
PricingFree
More technical detailsExpand ā–¾
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseMIT
Last updatedSep 7, 2026
10/10 checks healthy over the last 32d
Views3
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 stars2,019
GitHub Star CountTotal stargazers on GitHub representing community popularity (2,019 stars).
Last commit1mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 5, 2026
45Quality signal: Fair Ā· 45/100How this signal is calculated ā–¾
Server availabilityNot measured

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

Verified ownership10/20
Documentation & tools16/30
Adoption & activity8/15
Community engagement0/10

A guidance signal from public completeness & health data — 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.

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Scanned 20d ago via OSV.dev Ā· mcp-cli (PyPI)

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