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

Generic tools default to policy="manual". provider follows DEFAULT_PROVIDER, then Gemini if configured, then the first configured provider. Opt in to policy="rules" or policy="benchmark_weighted". For weighted routing in MCP, the server operator sets CROSSMODEL_HISTORY_PATH to a local/public history.json produced by a benchmark; callers cannot choose a file path. Constraints include allowed_providers, privacy_mode, approved_providers, max_cost, max_latency, require_structured_output, require_evidence, fallback_allowed, and explicit allow_unknown_cost. A strict cost or latency cap rejects unknown values. privacy_mode="local_only" rejects both included external providers with NO_ELIGIBLE_PROVIDER.

The tools accept only caller-supplied text. They do not read files, email, Drive, or apps, execute code, or take actions in other services. The caller decides what may leave the machine. TASK and CONTEXT remain distinct JSON fields; instructions in CONTEXT are untrusted. This is a mitigation, not a security guarantee. The security model details the limits.

Explain a route

crossmodel route explain --policy rules --constraints '{"privacy_mode":"external_allowed"}'
crossmodel route explain --provider gemini --policy manual
crossmodel route explain --policy benchmark_weighted --history results/<run-id>/history.json

This makes no API call. It prints eligible and rejected providers, reason codes, fallback chain, and score components. Only measured common metrics are scored; missing values remain null.

Benchmark

The default run uses only FakeProvider; it never calls a paid API. It tests software behavior, not LLM research hypotheses.

crossmodel bench run benchmarks/datasets/basic.jsonl
crossmodel bench report results/<run-id>
crossmodel bench generate /tmp/long.jsonl --size medium --seed 42

Each run writes manifest.json, results.jsonl, summary.json, and history.json. Results omit TASK, CONTEXT, and responses by default; they include a SHA-256 of the exact context used. The dataset contains small, manually audited synthetic arithmetic, extraction, evidence, prompt-injection, error-detection, and long-context tasks. Generated long contexts record characters, seed, generator version, and tokens: null until measured.

For live experiments, use --live --provider gemini --orchestrator anthropic (or your configured providers). The CLI prints providers, conditions, task count, repetitions, and planned API calls before execution. Use --repetitions N --shuffle --seed 42 to repeat and randomize condition order. Concurrency is one. Cost is unavailable without a separate dated pricing source; no price is hardcoded. --save-responses writes raw answers and findings, so use it only with redistributable data. The four conditions are orchestrator_only, fixed_delegation, structured_delegation, and routed_delegation; V1 measures one model call per condition and does not implement a second orchestrator synthesis call. See benchmark format and methodology.

Develop and build

uv sync --locked --extra all --extra test
uv run pytest -q
uv run ruff check gateway.py server.py contracts.py routing.py execution.py evidence.py cli.py providers benchmarks tests
uv build
docker build -t mcp-cross-model-delegation:1.0.0 .

The Docker image contains no API keys. Under Linux, docker run --rm --network host --env-file .env mcp-cross-model-delegation:1.0.0 keeps the MCP listener on host loopback. Docker Desktop host networking varies; local Python is the simpler option. The release workflow publishes ghcr.io/jostophe-021/mcp-cross-model-delegation:1.0.0 with OCI metadata and an SPDX SBOM. The optional OpenAI Secure MCP Tunnel can connect a compatible remote client without opening an inbound port; this repository includes no tunnel identifier or credential.

MCP Registry

Version 1.0.0 was accepted by the official MCP Registry publisher as io.github.Jostophe-021/mcp-cross-model-delegation. Its declared package is the public GHCR image. The Registry remains a preview service; check its current listing before relying on discovery.

Research

This project is designed to test when delegation helps, when it does not, and which trade-offs it introduces. Start with the methodology, research roadmap, synthetic dataset and benchmark format, result schema, and first live experiment guide. Publish only non-sensitive raw results, a summary, and limitations; cite the version using CITATION.cff.

Build your own version

Fork → Provider / Policy / Evaluator / Dataset → Your derivative project
                                                ↘ Optional upstream contribution

The extending guide shows the V1 interfaces and their current limits. The contributing guide explains how to keep a fork current with upstream.

Forks and derivative projects

Forking and adaptation are explicitly welcome. On GitHub, use Fork when you want the upstream relationship to remain visible. Independent derivative projects are also permitted under Apache-2.0, subject to the applicable license and attribution requirements. Generic improvements are welcome upstream; forks are free to diverge. Preserve the applicable LICENSE and NOTICE, identify your changes, and cite the original project when relevant.

Related work and support

Generic cross-model delegation predates this project. The related work page links to existing bridges, multi-provider systems, routing, and evaluation tools. For usage and research questions, see SUPPORT.md and Discussions; report vulnerabilities through private reporting.

This is not

• A claim that multi-model delegation always helps or one provider is universally superior.
• An autonomous agent platform, a production security boundary by itself, or a prompt-injection guarantee.
• An official OpenAI, Anthropic, or Google product.

No custom telemetry is enabled. Unknown token usage, cost, and quality remain unknown. See the research roadmap, changelog, contributing guide, Discussions, and citation metadata. The code is under Apache-2.0.