| # LEADER: modelcontextprotocol/python-sdk |
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| **URL:** https://github.com/modelcontextprotocol/python-sdk |
| **License:** MIT |
| **License SHA (blob):** `3d48435454b105021b4f777c11b6b07d8d2ffea3` |
| **HEAD commit (main):** `161834d4aee2633c42d3976c8f8751b6c4d947d5` |
| **Commit date:** 2026-05-08T16:42:44Z |
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| ## Why chosen |
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| | Criterion | python-sdk | gorilla | |
| |---|---|---| |
| | License | MIT ✓ | Apache-2.0 ✓ | |
| | Dispatch primitive | `session.call_tool(name, args)` — 1 RPC call | OpenFunctions requires inference server | |
| | Kernel extractability | Pure Python, sync-wrappable | Coupled to model weights | |
| | Mockability | `invoke` callable injection trivial | Requires HTTP stub | |
| | Dependency footprint | `anyio` + `pydantic` | `torch` / vLLM | |
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| **Decision:** python-sdk wins. Its `ToolManager.call_tool(name, args, context)` pattern reduces to a single dict lookup + async run — extractable to ≤10 sync lines with a mockable callable injected at the boundary. gorilla/OpenFunctions is powerful for LLM-driven selection but mandates a model server, violating the kernel-size constraint. |
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| ## Dispatch anatomy (from source) |
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| ``` |
| src/mcp/client/session.py → ClientSession.call_tool(name, args) |
| src/mcp/server/mcpserver/tools/tool_manager.py → ToolManager.call_tool(name, args, ctx) |
| src/mcp/server/mcpserver/tools/base.py → Tool.run(args, ctx) |
| ``` |
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| Core pattern: `tool = registry[name]; result = tool.fn(**args)`. Everything else is schema validation and async transport — stripped in the kernel. |
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