AlphaFold Sovereign MCP

Answering a structural-biology or variant question usually means querying many public databases by hand — AlphaFold DB, Open Targets, ClinVar, gnomAD, and more — and reconciling their formats. This server wraps those sources behind one set of MCP tool calls that run as a local process on your own machine, with no hosted service of ours in the path, no telemetry, and a local SQLite knowledge graph that never leaves your disk. In the default online mode the tools query those public upstreams directly, so the identifiers you look up are sent to them (and one, DisGeNET, needs its own free API key); set ALPHAFOLD_OFFLINE=1 to refuse outbound requests before any socket opens, so no identifier leaves the machine (the knowledge-graph tools still answer from local data; the upstream tools report their source as unavailable). "Sovereign" here means local-first — your compute and stored results stay on your machine — not that the server runs without a network.

A Model Context Protocol server — an AlphaFold MCP server — that wraps AlphaFold DB and 8 other public biomedical data sources behind a set of MCP tool calls, backed by a local SQLite knowledge graph with query and export tools (results can be persisted through its API; automatic per-invocation persistence is not yet wired).

This is an unfunded, independent open-source project. It is not a service, not certified for any regulated use, and its outputs are research aids that should be reviewed by qualified humans before any clinical or regulatory use.

This project is not affiliated with, endorsed by, or sponsored by Google DeepMind or EMBL-EBI. "AlphaFold" is a trademark of its respective owner and is used here only to describe the public data (the AlphaFold DB API) that this software consumes.

OpenSSF Scorecard

Status: Beta. Engineering-validated (100% line and branch coverage). Not yet scientifically validated by independent domain experts; not yet deployed in production. See STATUS.md and LIMITATIONS.md.

What this is

A Python MCP server that:

• Wraps AlphaFold DB, MONDO, HPO, Open Targets, ClinVar, gnomAD, DisGeNET, ChEMBL, and Ensembl behind MCP tool calls. Each call is a thin orchestration over those upstreams; the server does not add scientific judgement.
• Composes upstreams into multi-source workflows: variant cross-reference reports, disease–target landscape summaries, heuristic target-druggability scoring, drug-repurposing candidate ranking, and cross-species structural-distance computation.
• Ships a local SQLite knowledge graph (storage/knowledge_graph.py) with query, export, and traversal tools. It loads a curated boot seed automatically when empty (storage/seed.py, 16 entities and 15 relationships; disable with AFSMCP_DISABLE_KG_SEED=1) and can be extended by writing through the knowledge-graph API. There is no automatic per-invocation persistence: the analysis tools do not write to the store on their own.
• Includes a topological-data-analysis (TDA) module that computes persistent-homology fingerprints (Betti numbers β₀, β₁, β₂) over Vietoris-Rips filtrations of Cα coordinates, and an L2-distance comparator between those fingerprint vectors. The full persistent-homology features require the optional [tda] extra (gudhi).

It targets mcp-spec 2025-06-18 and runs on Python 3.10–3.13.

What this is not

• It is not a hosted service or a SaaS.
• It is not certified for any regulated use (HIPAA, GxP, 21 CFR Part 11, FedRAMP, FIPS, SOC 2). The code structures audit logging in a way that could later support such a certification, but no such audit has been performed.
• It does not train, fine-tune, or publish AlphaFold models — it consumes AlphaFold DB's public REST API.
• The "ACMG/AMP criteria" that generate_variant_clinical_report produces are a draft surface of the upstream evidence the server can fetch automatically. They are not a substitute for clinical-laboratory variant review.
• The "druggability tier" that assess_target_druggability returns is a heuristic built from drug-precedent counts, Open Targets tractability labels, pLDDT, and gnomAD constraint. It is not a validated prediction.
• "Structural distance" between proteins is an L2 distance on length-normalised TDA fingerprint vectors. It measures topological similarity of the Cα point cloud. It is not a sequence similarity, RMSD, optimal-transport Wasserstein distance, or functional-equivalence measure.
• The AlphaFold structures consumed here are predicted models with per-residue pLDDT confidence, not experimental structures. Low-pLDDT regions are unreliable; some proteins (BRCA1 among them) are largely low-confidence, and structural inference over those regions should be treated with caution.

For a complete, itemised list of known limitations (with module references, impact, and planned resolution), see LIMITATIONS.md. For the high-level posture — what is engineering-validated vs. what is not yet scientifically validated — see STATUS.md.

Install

From PyPI (recommended)

pip install alphafold-sovereign-mcp

Or run it without installing using uvx:

uvx alphafold-sovereign-mcp

Every release on PyPI is built by the release.yml workflow under OIDC Trusted Publishing and attached to a signed GitHub Release with Sigstore (cosign) signature bundles, a CycloneDX SBOM, and a Zenodo DOI mirror. SLSA L3 build provenance is generated in CI by slsa-github-generator; attaching the attestation to each release is a roadmap item. scripts/replicate.sh downloads the exact published wheel and sdist, recomputes their PyPI SHA-256 digests, verifies the GitHub Release Sigstore bundles against those bytes and this repository's release workflow identity, and independently verifies that the released CycloneDX SBOM is bound to the downloaded wheel. If SLSA provenance is attached to a future release, the same script verifies it against the wheel when slsa-verifier is installed.

From source

git clone https://github.com/smaniches/alphafold-sovereign-mcp
cd alphafold-sovereign-mcp
uv pip install -e .
# With persistent-homology TDA (requires gudhi):
# uv pip install -e ".[tda]"

Verify the install

$ alphafold-sovereign --version
1.4.9
$ alphafold-sovereign --self-test
SELF-TEST PASS - ACMG helpers behave as expected on the BRCA1 c.5266dupC fixture.

If you ran it via uvx without installing, use uvx alphafold-sovereign-mcp --self-test instead (the bare alphafold-sovereign script is only on PATH after a pip/uv install).

--self-test runs fully offline: it checks the deterministic ACMG-evidence helpers (VEP, gnomAD, and AlphaMissense mapped to ACMG criteria) against a built-in BRCA1:c.5266dupC fixture. Returns exit code 0 on PASS, non-zero on FAIL. No network calls, no credentials required.

Configure Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "alphafold-sovereign": {
      "command": "alphafold-sovereign-mcp",
      "args": []
    }
  }
}

Restart Claude Desktop and the tools become available in conversations. Try asking, for example: "Triage BRCA1 c.5266dupC" or "Assess EGFR as a drug target". See the examples/ directory for three end-to-end illustrations of what a session looks like.

Offline mode

ALPHAFOLD_OFFLINE=1 alphafold-sovereign-mcp

Refuses outbound HTTP before a socket is opened (raising AirGapError), except to hosts you explicitly allowlist via ALPHAFOLD_ALLOW_HOSTS. The knowledge-graph query and export tools still answer from the local SQLite store. The upstream-querying tools have no local cache and report their source as unavailable; note that the structure tools currently surface this as a "no AlphaFold model" result rather than an explicit offline error.

Tool inventory

The server exposes 30 MCP tools across four modules. Each tool's input schema is a Pydantic model; results are JSON.

Disease & ontology (tools/disease.py)

Precision medicine (tools/precision_medicine.py)

The ACMG/AMP criteria produced are a draft: they reflect the upstream evidence the server can fetch automatically, and they are not a substitute for clinical-laboratory review.

Structure intelligence (tools/structure_intelligence.py)

Knowledge graph (tools/knowledge_graph_tools.py)

Example usage

For three documented end-to-end illustrations of a Claude Desktop session against this server — variant triage on BRCA1 c.5266dupC, target characterisation on EGFR, and a drug-discovery walk-through on Imatinib → BCR-ABL → CML — see the examples/ directory. Each example includes the user prompt, the tool calls the model issues, the server's response shape, and the model's paraphrased reply.

Clinical variant report

generate_variant_clinical_report(hgvs="BRCA1:c.181T>G")

The server resolves the HGVS, fetches ClinVar, gnomAD, AlphaMissense (via AlphaFold DB), Open Targets disease evidence, ChEMBL drug data, and Ensembl VEP consequence annotations, and returns a single JSON record with the cross-referenced fields plus the ACMG/AMP criteria that the available evidence supports.

Drug repurposing

find_drug_repurposing_candidates(disease_mondo_id="MONDO:0007739")

Returns drugs whose Open Targets evidence connects them to the disease, ranked by a composite of OT evidence score × the maximum ChEMBL clinical phase reached against the target.

Cross-species structural divergence

find_evolutionary_structural_shifts(
    gene_symbol="ACE2",
    target_species=["mus_musculus", "rhinolophus_ferrumequinum"]
)

For each species: fetches the ortholog (Ensembl), the AlphaFold structure, computes the TDA fingerprint, and returns the L2 fingerprint distance from the human structure along with sequence identity.

Data sources

UniProt accessions are used throughout as protein identifiers — they key AlphaFold structures and Open Targets cross-references — but the UniProt API itself is not queried as a data source. Domain (InterPro), Gene Ontology, experimental-structure (RCSB PDB), and tissue-expression (Human Protein Atlas) lookups are not integrated in this release.

See NOTICE for full attributions.

Architecture

clients/_base.py
  ├── Air-gap enforcement (refuses sockets when ALPHAFOLD_OFFLINE=1)
  ├── Token-bucket rate limiting (aiolimiter)
  ├── Exponential backoff with jitter (tenacity)
  ├── Circuit breaker (CLOSED / OPEN / HALF_OPEN)
  └── HTTP/2 transport with connection pooling and keep-alive (httpx)

storage/knowledge_graph.py
  ├── SQLite WAL mode (embedded, ACID)
  ├── 6 entity tables: proteins, variants, diseases, drugs, genes, phenotypes
  ├── 4 relationship tables: protein_disease, protein_drug, variant_disease, gene_phenotype
  ├── tool_invocations audit table (SHA-256 of input + output, timestamps)
  └── Analytical views: variant_summary, drug_landscape

domain/disease.py
  └── Pure Python frozen dataclasses (PathogenicityClass, VariantReport, ...)

domain/druggability.py
  └── Pure, I/O-free target-druggability heuristic (named/cited weights and
      cut-offs, plus confidence / data-completeness / borderline reporting)

See ARCHITECTURE.md for the full module map.

Testing & quality

• A comprehensive offline unit-test suite (respx-mocked upstreams, including parametrised expansions); the full suite runs hermetically in under a minute on a laptop.
• Coverage on the shipped surface (src/alphafold_sovereign/clients, domain, storage, server, tools): 100% line + branch, every shipped module at 100%.
• Lint: ruff (full ruleset). Type checking: mypy --strict on the full source tree.
• Security: bandit plus CodeQL security-extended.
• Supply chain: release CI builds and attests the distributions, generates a wheel-bound CycloneDX SBOM from the installed runtime dependency closure, and signs release artifacts with Sigstore. scripts/replicate.sh independently downloads the published wheel and sdist, verifies their PyPI hashes and Sigstore bundles, and checks the released CycloneDX binding. SLSA provenance is generated in CI; attaching it to each GitHub Release remains a roadmap item.

The full CI matrix (Python 3.10, 3.11, 3.12, 3.13 × Ubuntu, macOS) runs on every push. The coverage percentage above is the number a git clone && uv run nox -s cov produces on the current HEAD; if you find a divergence, please open an issue.

Contributing

DCO sign-off required (git commit -s). No copyright assignment. Coverage gate: CI enforces 100% line and branch coverage on the shipped surface (nox -s cov). Full guide: CONTRIBUTING.md.

Related MCP servers by the same author

• uniprot-mcp — Model Context Protocol server for UniProt Swiss-Prot and TrEMBL (pip install uniprot-mcp-server).
• semantic-scholar-mcp — Semantic Scholar MCP server, 200M+ academic papers (pip install s2-mcp-server).

Citation

Machine-readable metadata: CITATION.cff (GitHub renders a "Cite this repository" button in the sidebar that consumes this file).

@software{maniches_alphafold_sovereign_mcp,
  author    = {Maniches, Santiago},
  title     = {AlphaFold Sovereign MCP},
  year      = {2026},
  version   = {1.4.9},
  url       = {https://github.com/smaniches/alphafold-sovereign-mcp},
  license   = {Apache-2.0},
  orcid     = {0009-0005-6480-1987},
  doi       = {10.5281/zenodo.20134773}
}

When citing results derived from this software, please also cite the upstream data sources (AlphaFold DB, Open Targets, ChEMBL, Ensembl, ClinVar, gnomAD, MONDO, HPO, DisGeNET) according to their own citation requirements.

License

Copyright 2024–2026 Santiago Maniches.

Licensed under the Apache License, Version 2.0. See LICENSE.

Patent reservation: see PATENTS.md. Trademark policy: see TRADEMARKS.md.