A Python implementation of a state-based agent orchestration system using the Model Context Protocol (MCP).
A Python implementation of a state-based agent orchestration system using the Model Context Protocol (MCP).
The Model Context Protocol (MCP) allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. With MCP, you can build servers that expose:
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{
"mcpServers": {
"agent-orchestrator": {
"command": "python",
"args": [
"/ABSOLUTE/PATH/TO/YOUR/PROJECT/orchestrator.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
Read, search and edit files inside allowed directories with the Model Context Protocol reference Filesystem server.
Retrieve version-aware library documentation and code examples with Context7 MCP, using resolve-library-id and query-docs.
Works with ChatGPT, Claude, Gemini, xAI, Cursor, VS Code and any MCP client.
French real-estate intelligence from official open data: DVF, valuations, rents, tax, DPE, risks.
An MCP to restart Claude Desktop App with enabled debugger port and inject a JavaScript into it, which extends Claude with MCP auto-approve
MCP stdio server that serves local repo memory and org memory from GitHub PR history, code, tests, regressions, and cross-repo impact to AI coding