Tools

Seven tools spanning the structure-research arc — discover, fetch, find homologs, track ligands, compare, profile the corpus, and annotate — over experimental (PDB) and predicted (AlphaFold) structures from one surface:

protein_search_structures

Federated search across experimental (PDB) and predicted (computed-model) structures via RCSB Search v2.

• Free-text, protein-sequence (triggers an mmseqs2 similarity search), and organism / method / resolution filters
• content_type scopes the search to experimental, predicted, or all
• Experimental hits are enriched with title, method, resolution, and organism
• Optional facets return a method / organism / release-year breakdown alongside the hits at no extra call
• Chain hit IDs straight into protein_get_structure

protein_get_structure

Fetch structures with metadata and coordinate-file URLs, resolving across providers by source.

• source: experimental takes PDB entry IDs, batched in one RCSB GraphQL call
• source: predicted takes UniProt accessions and returns the AlphaFold model with pLDDT/PAE confidence
• source: best_available takes UniProt accessions and returns the top federated model (experimental if one exists, else the best prediction)
• Per-ID partial success — unresolved IDs are listed in failed[], not a batch-level error
• include_coords inlines coordinate content; when a batch overflows the response budget it returns a per-structure size outline, so you can re-call with sections: [ids] for specific structures
• Every response carries an attribution block naming the upstream data licenses and citations (see Upstream data licensing)

protein_find_similar

Find structurally or evolutionarily related proteins, by sequence or by fold.

• by: sequence runs a synchronous RCSB mmseqs2 search; by: structure runs an asynchronous Foldseek search against experimental and predicted databases
• Query from a raw one-letter sequence, a PDB ID, or a UniProt accession
• Foldseek targets default to pdb100 + afdb50; override via databases (e.g. afdb-swissprot, BFVD)
• Async jobs that exceed the poll budget return status: computing with a ticketId — re-call with ticket_id set to that value to poll the same job instead of resubmitting
• Each hit names the engine and source database it came from

protein_track_ligands

Ligand discovery and binding-site analysis across the PDB.

• mode: find_ligand resolves a name or formula to chemical component IDs with formula, weight, SMILES, and InChIKey
• mode: structures_with_ligand returns PDB entries containing a ligand by exact component ID
• mode: binding_site returns the protein residues lining a ligand's pocket in a structure, with contact distances
• Binding sites are experimental-only — computed from deposited coordinates (predicted models carry no bound ligands)

protein_compare_structures

Structural alignment of multiple structures (up to the configured PROTEIN_MAX_COMPARE_STRUCTURES cap) via the RCSB Structural Comparison service.

• Methods: tm-align, fatcat-rigid, fatcat-flexible
• reference: first aligns every structure to the first; reference: all_pairs computes the full pairwise matrix
• Optional per-structure chain restricts the alignment to a single chain
• Each pair is an independent async job, fanned out with a concurrency cap and per-pair partial success — a pair still computing when the budget elapses returns status: computing with its job uuid, and a failed pair degrades its row without sinking the others
• Re-call with a matching { a, b, uuid } entry in resume[] (copied from a prior response's pairs[]) to poll a computing pair's job instead of resubmitting
• Returns TM-score, RMSD, and aligned-residue count per pair

protein_analyze_collection

Profile the PDB into distributions and trends over an optional scoping query — backed by RCSB's server-side facet engine (one call, compact buckets, no row pull).

• Group by method, organism, polymer_type, resolution, release_year, or molecular_weight
• One group_by dimension for a breakdown, or two for a cross-tab (the first nests the second)
• interval sets the bin width for value histograms or the period for date histograms (year / month / quarter)
• Scope with a free-text query, organism, method, or max_resolution; content_type selects the structure universe
• bucket_limit caps buckets per dimension; truncation is flagged in the response

protein_get_annotations

Sequence and functional annotation for a protein.

• UniProt features (domains, binding sites, PTMs) and natural sequence variants
• InterPro domain/family memberships (Pfam, PROSITE, …) with associated GO terms
• Provide a UniProt accession directly, or a PDB ID — resolved to a UniProt accession via the structure's sequence cross-reference
• A multi-chain PDB entry can map to several accessions; the default is the deterministic lowest-author-chain pick, with the alternatives listed under ambiguity. Pass chain (an author chain ID, e.g. A) to select a specific one
• include scopes which annotation classes are fetched: features, domains, variants, or all
• Every response carries an attribution block naming the upstream data licenses and citations (see Upstream data licensing)

Resources

All resource data is also reachable via tools — pdb://{entry_id} mirrors protein_get_structure for source: experimental, and af://{uniprot} mirrors it for source: predicted. Many MCP clients are tool-only and don't surface resources; the summaries remain reachable through the tools.

Features

Built on @cyanheads/mcp-ts-core:

• Declarative tool and resource definitions — single file per primitive, framework handles registration and validation
• Unified error handling — handlers throw, framework catches, classifies, and formats
• Pluggable auth: none, jwt, oauth
• Swappable storage backends: in-memory, filesystem, Supabase, Cloudflare KV/R2/D1
• Structured logging with optional OpenTelemetry tracing
• STDIO and Streamable HTTP transports

Protein-specific:

• One federated surface over experimental (PDB) and predicted (AlphaFold / 3D-Beacons) structures — search, fetch, and compare treat both universes the same
• Keyless across every upstream — RCSB, AlphaFold DB, 3D-Beacons, UniProt, InterPro, and Foldseek, no API keys to provision
• Corpus analytics run server-side on RCSB's facet engine — distributions, histograms, and cross-tabs in one call, no row pull and no SQL workspace
• Async alignment and Foldseek jobs poll within a bounded budget and hand back a job ticket (ticketId / per-pair uuid) instead of blocking — re-call with ticket_id or a resume[] entry to poll the same job instead of resubmitting

Agent-friendly output:

• Provenance on every response — each hit carries a source (experimental / predicted), the engine and database that produced it, and effective-query / total-count echoes so agents can reason about coverage
• Graceful partial failure — batch fetches and pairwise comparisons return per-item rows (failed[], per-pair status) instead of failing the whole request, each with actionable recovery text
• Discriminated output contracts — typed source and status unions, computing results with resume tickets, and budget-overflow outlines let callers branch on data, not string parsing

Project structure

Development guide

See CLAUDE.md/AGENTS.md for development guidelines and architectural rules. The short version:

• Handlers throw, framework catches — no try/catch in tool logic
• Use ctx.log for request-scoped logging, ctx.state for tenant-scoped storage
• Register new tools and resources via the barrels in src/mcp-server/*/definitions/index.ts
• Wrap external API calls: validate raw → normalize to domain type → return output schema; never fabricate missing fields

Upstream data licensing

Structure and annotation data comes from public upstream databases, each under its own license. protein_get_structure and protein_get_annotations carry an attribution block on every response — the license, citation, and homepage for each source that contributed to that specific response — so the attribution obligation travels with the data to downstream consumers rather than living only here. CC BY / CC BY-SA sources require attribution on redistribution; CC0 sources are citation-only (attribution encouraged, not required).

best_available federates predicted models through 3D-Beacons, so the attribution block credits the actual contributing provider (AlphaFold DB, SWISS-MODEL, BFVD, …); a provider without a curated license entry carries a See provider terms fallback pointing back to 3D-Beacons rather than a fabricated license. InterPro's own domain/family classifications are CC0; the GO terms carried alongside them are separately CC BY 4.0, so each is credited independently only when it actually contributes. Full citations for each source travel in the attribution block of the relevant tool responses. This covers upstream data licensing — the server's own code is licensed separately (see License).

Getting started

Public Hosted Instance

A public instance is available at https://protein.caseyjhand.com/mcp — no installation required. Point any MCP client at it via Streamable HTTP:

{
  "mcpServers": {
    "protein": {
      "type": "streamable-http",
      "url": "https://protein.caseyjhand.com/mcp"
    }
  }
}

Self-hosted

Add the following to your MCP client configuration file. No API key is required — every upstream provider is keyless.

{
  "mcpServers": {
    "protein-mcp-server": {
      "type": "stdio",
      "command": "bunx",
      "args": ["@cyanheads/protein-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "MCP_LOG_LEVEL": "info"
      }
    }
  }
}

Or with npx (no Bun required):

{
  "mcpServers": {
    "protein-mcp-server": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@cyanheads/protein-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "MCP_LOG_LEVEL": "info"
      }
    }
  }
}

Or with Docker:

{
  "mcpServers": {
    "protein-mcp-server": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "MCP_TRANSPORT_TYPE=stdio", "ghcr.io/cyanheads/protein-mcp-server:latest"]
    }
  }
}

For Streamable HTTP, set the transport and start the server:

MCP_TRANSPORT_TYPE=http MCP_HTTP_PORT=3010 bun run start:http
# Server listens at http://localhost:3010/mcp

Prerequisites

• Bun v1.3.2 or higher (or Node.js v24+).
• No accounts or API keys — RCSB, AlphaFold DB, 3D-Beacons, UniProt, InterPro, and Foldseek are all public and keyless.

Installation

1. Clone the repository:

git clone https://github.com/cyanheads/protein-mcp-server.git

2. Navigate into the directory:

cd protein-mcp-server

3. Install dependencies:

bun install

Configuration

All upstream providers are keyless, so the server runs out of the box with no configuration. Every variable below is optional.

See .env.example for the full list of provider base-URL overrides and tuning limits.

Running the server

Local development

• Build and run:

  Copy

  # One-time build
  bun run rebuild
  
  # Run the built server
  bun run start:stdio
  # or
  bun run start:http

• Run checks and tests:

  Copy

  bun run devcheck   # Lint, format, typecheck, security
  bun run test       # Vitest test suite
  bun run lint:mcp   # Validate MCP definitions against spec

Docker

docker build -t protein-mcp-server .
docker run --rm -e MCP_TRANSPORT_TYPE=http -p 3010:3010 protein-mcp-server

The Dockerfile defaults to HTTP transport, stateless session mode, and logs to /var/log/protein-mcp-server. OpenTelemetry peer dependencies are installed by default — build with --build-arg OTEL_ENABLED=false to omit them.

License

Apache-2.0 — see LICENSE for details.