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smaniches/uniprot-mcp

by Various

Auditable UniProt MCP server with per-response SHA-256 provenance, release pinning, verification, and offline replay.

MCP

smaniches/uniprot-mcp

Added 3 Sept 2026

#bioinformatics #claude #claude-ai #claude-code #clinvar-database #clinvar-database-mcp #drug #drug-design

Overview

A Model Context Protocol (MCP) server for UniProt data that adds per-response SHA-256 provenance, release pinning, verification, and offline replay. Written in Python, it provides an auditable interface for retrieving protein data with reproducible results.

Best for

Best for
Developers needing auditable, reproducible UniProt data access in MCP workflows

Use cases

  • Integrating UniProt data into MCP-based agent workflows
  • Auditing the provenance of protein data responses
  • Replaying responses from pinned UniProt releases for reproducibility

How to use

Install

uvx uniprot-mcp-server

Tools exposed

  • uniprot_get_entry
  • uniprot_search
  • uniprot_get_sequence
  • uniprot_get_features
  • uniprot_get_variants
  • uniprot_get_go_terms
  • uniprot_get_cross_refs
  • uniprot_id_mapping
  • uniprot_batch_entries
  • uniprot_taxonomy_search
  • uniprot_get_keyword
  • uniprot_search_keywords
  • uniprot_get_subcellular_location
  • uniprot_search_subcellular_locations
  • uniprot_get_uniref
  • uniprot_search_uniref
  • uniprot_get_uniparc
  • uniprot_search_uniparc
  • uniprot_get_proteome
  • uniprot_search_proteomes

Tested with

Claude Desktop, Claude Code

Notes

A Model Context Protocol (MCP) server for UniProt data that adds per-response SHA-256 provenance, release pinning, verification, and offline replay. Written in Python, it provides an auditable interface for retrieving protein data with reproducible results.

3 stars on GitHub. Last updated 2026-09-03. Licensed Apache-2.0.

Use cases

  • Integrating UniProt data into MCP-based agent workflows
  • Auditing the provenance of protein data responses
  • Replaying responses from pinned UniProt releases for reproducibility

Pros

  • Per-response SHA-256 provenance enables audit trails
  • Release pinning and offline replay support reproducible queries
  • Simple Python implementation for easy extension

Cons

  • Low community adoption (3 stars) suggests limited validation
  • Python-only limits use in non-Python environments
  • Offline replay requires pre-cached data, which is not documented here

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Per-response SHA-256 provenance enables audit trails
  • Release pinning and offline replay support reproducible queries
  • Simple Python implementation for easy extension

Cons

  • Low community adoption (3 stars) suggests limited validation
  • Python-only limits use in non-Python environments
  • Offline replay requires pre-cached data, which is not documented here
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