MCP Cross-Model Delegation

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An open, reproducible and security-conscious framework for measuring, comparing, routing and verifying bounded work across heterogeneous language models.

One Python core powers an MCP server and a benchmark CLI: measure → compare → route → verify → improve. V1 supports Gemini, Anthropic, and a deterministic fake provider for offline tests. Routing is explicit, quotation evidence is checked locally, and no improvement in quality, cost, or latency is claimed without measurement.

flowchart LR
    A["TASK + CONTEXT"] --> B["Caller constraints"]
    B --> C["Router"]
    H["Benchmark history"] -. "weighted policy" .-> C
    C --> D["Gemini or Anthropic"]
    D --> E["Result"]
    E --> F["Local quote check<br/>for extraction"]
    E --> G["Optional benchmark evaluation"]
    F --> G

What you can do today

• Delegate bounded text work to Gemini or Anthropic through one interface.
• Apply privacy, capability, cost, and latency constraints before provider selection; inspect routing reasons.
• Verify extracted quotations against the supplied context.
• Run deterministic offline benchmarks without API keys, or opt in to live cross-provider experiments.

Why this is different

The same loop measures, routes, executes, verifies evidence, and evaluates results. The default policy is manual; rules and benchmark_weighted require explicit opt-in. Historical scores do not promise future quality or latency. See the architecture, methodology, and use cases.

Try it in 5 minutes

Python 3.12 or 3.13 and uv are supported. A Gemini API key and an Anthropic API key are independent; configure either or both. A Claude subscription does not provide Anthropic API credits.

git clone https://github.com/Jostophe-021/mcp-cross-model-delegation.git
cd mcp-cross-model-delegation
uv sync --locked --extra all --extra test
uv run crossmodel doctor
uv run crossmodel providers
uv run crossmodel bench run benchmarks/datasets/basic.jsonl

The benchmark above uses FakeProvider and needs no API key. Software validation only — not an LLM performance result. See the four offline examples.

Try a real provider

Set GEMINI_API_KEY or ANTHROPIC_API_KEY only when you intend to call a real provider. Live API usage may incur provider charges. Start with synthetic or redistributable text, and inspect the planned call count before a live benchmark.

From this repository, install only the needed adapter with uv sync --locked --extra gemini or uv sync --locked --extra anthropic. The base package needs neither provider SDK for offline benchmarks. PyPI publication is a separate release step; install from the repository until a PyPI release is verified.

Set GEMINI_API_KEY and/or ANTHROPIC_API_KEY in your own environment. .env.example shows names only; never commit .env or paste a key into a command, issue, or chat. Set DEFAULT_PROVIDER=anthropic if Anthropic is your manual default. Run crossmodel serve to expose streamable HTTP MCP at http://127.0.0.1:8000/mcp. MCP_HOST=0.0.0.0 is refused because the server has no public client authentication.

MCP tools

... View full README on GitHub