Brain-MCP Developer Documents Tools for Claude AI Instead of Humans
A developer maintaining the Brain-MCP server, which gives Claude memory across conversations, discovered that the primary consumer of their documentation was Claude rather than human GitHub readers. They added a "For AI Assistants" section at the top of the README containing behavioral instructions rather than just tool descriptions.
Key Details from the Experiment
The developer created a dedicated page at brainmcp.dev/for-ai with specific guidance for AI assistants. The behavioral instructions in the README function like a system prompt for tool usage.
The immediate difference observed was that Claude started using tools more intelligently—not just when explicitly asked, but proactively injecting relevant context when the user switched topics.
Documentation Pattern for AI Consumption
The developer suggests that if your MCP server is consumed by an AI, you should write documentation specifically for the AI. This includes:
- Not just tool names and parameter types
- Actual guidance on when and how to use tools effectively
- Behavioral instructions that influence how Claude uses the tools
Specific Examples from the Documentation
The documentation includes practical guidance such as:
- When to proactively search (e.g., when user says "where did I leave off" → call tunnel_state)
- How to present results ("synthesize, don't dump raw search results")
- When NOT to search (pure commands, continuation of same thread)
Technical Details
The Brain-MCP server can be installed with: pipx install brain-mcp && brain-mcp setup
The project includes 25 tools, is 100% local, and is MIT licensed.
The developer is asking other MCP developers if they've experimented with similar approaches to influence how Claude uses their tools beyond basic tool descriptions.
📖 Read the full source: r/ClaudeAI
👀 See Also
AgentMind: A Claude Code Plugin That Learns and Applies Your Coding Preferences
AgentMind is a Claude Code plugin that observes your coding patterns, learns preferences like tool choices and style rules, and automatically injects that context into future sessions. It uses a six-step core loop and confidence scoring to determine when to apply learned preferences.
Detrix MCP Server Adds Runtime Debugging to AI Coding Agents
Detrix is a free, open-source MCP server that enables MCP-compatible agents to observe live variables in running code without restarts or code changes. It supports Python, Go, and Rust applications running locally or in Docker.
pxpipe: Cut Claude Code Token Usage 60% by Rendering Context as Images
pxpipe is a local proxy that renders bulky context (system prompts, tool docs, history) into compact PNGs, cutting input tokens ~10x and costs ~60% on token-dense workloads.
Qwen3.5-9B-Claude-4.6-Opus-Uncensored-v2 Model Released with LM Studio Configuration
A merged uncensored model combining Qwen3.5-9B architecture with Claude 4.6 Opus training data is now available, with specific LM Studio 0.4.7 settings provided for optimal performance including temperature 0.7 and top K sampling 20.