The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the MCP Cross Model Delegation listing page.
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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.
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.
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.
The benchmark above uses FakeProvider and needs no API key. Software validation only — not an LLM performance result. See the four offline examples.
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.
| Tool | Purpose |
|---|---|
delegate_task | Delegate a bounded TASK with optional CONTEXT, provider, policy, and constraints. |
extract_findings | Extract structured findings, then locate quotations locally in CONTEXT. |
gemini_delegate_task, gemini_extract_findings | V0.1 compatibility aliases using Gemini and manual routing. |
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.
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.
The default run uses only FakeProvider; it never calls a paid API. It tests software behavior, not LLM research hypotheses.
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.
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.
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.
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.
The extending guide shows the V1 interfaces and their current limits. The contributing guide explains how to keep a fork current with upstream.
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.
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.
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.