Alphafold Sovereign MCP

What smaniches/alphafold-sovereign-mcp MCP server does

The smaniches/alphafold-sovereign-mcp MCP server exposes biomedical research workflows through MCP tools. It connects AlphaFold DB with MONDO, HPO, Open Targets, ClinVar, gnomAD, DisGeNET, ChEMBL, and Ensembl. The resulting tools cover disease and phenotype lookup, gene phenotype profiles, drug-target relationships, variant triage, protein structures, and cross-species comparisons.

Several tools combine results from multiple sources. Examples include generating a draft variant report from an HGVS expression, mapping phenotypes to structures and targets, assembling a protein dossier, ranking drug-repurposing candidates, and mapping a disease to approved and pipeline drugs. Local tools can query stored variant or protein assessments, traverse drug-gene-disease relationships, report graph statistics, and export tables as JSON.

How it works

The smaniches/alphafold-sovereign-mcp MCP server is a Python process that communicates with an MCP client. In its normal online mode, individual tools make requests to public upstream services and reconcile their responses. The project describes these operations as orchestration rather than independent scientific judgment.

A SQLite knowledge graph is included for local entities, relationships, queries, traversal, and exports. When the database is empty, a curated seed is loaded with 16 entities and 15 relationships unless seeding is disabled. Analysis calls do not automatically persist every result; data must be written through the knowledge-graph API.

Structural analysis includes pLDDT confidence summaries, PAE-derived domain boundaries, geometric pocket scoring, intrinsic-disorder maps, and optional persistent-homology calculations over C-alpha coordinates. The topology comparator uses length-normalized 64-dimensional fingerprints and L2 distance, so it is not an RMSD, sequence-similarity, or functional-equivalence calculation.

Setup and configuration

Install the published package with pip, or run it without a permanent installation:

uvx alphafold-sovereign-mcp

Source installation uses uv pip install -e .. The optional tda extra adds the gudhi dependency for full persistent-homology features. The project targets Python 3.10 through 3.13 and MCP specification 2025-06-18.

Set ALPHAFOLD_OFFLINE=1 to prevent outbound requests before a socket is opened. In that mode, local knowledge-graph tools continue to use local data, while upstream-backed tools report that their sources are unavailable. The default online mode sends identifiers to the relevant public upstreams. DisGeNET requires a separate free API key. AFSMCP_DISABLE_KG_SEED=1 disables automatic loading of the initial graph seed.

Tools and capabilities

Available capability groups include:

• Search MONDO diseases, resolve ICD-10 codes, inspect HPO terms, and build gene phenotype profiles.
• Find disease targets, target-associated diseases, target overlap, drug landscapes, repurposing candidates, and drug-gene network paths.
• Cross-reference HGVS variants with ClinVar and gnomAD, create draft ACMG/AMP checklists, and query locally stored triage results.
• Retrieve AlphaFold model metadata, download links, and optional coordinates; assess confidence, disorder, pockets, topology, and evolutionary structural shifts.
• Assess heuristic target druggability, synthesize protein dossiers, inspect local graph statistics, and export research data as JSON.

Limitations and notes

The smaniches/alphafold-sovereign-mcp MCP server is beta software and has engineering validation but no independent scientific validation or production deployment described in the provided material. It is not a hosted service, does not train AlphaFold models, and is not certified for regulated use. Generated ACMG/AMP criteria are draft evidence surfaces, not clinical-laboratory review. Druggability tiers are heuristic. AlphaFold inputs are predicted structures with per-residue confidence, so low-pLDDT regions require caution. The project is licensed under Apache-2.0 and is not affiliated with Google DeepMind or EMBL-EBI.