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Official STRING Database MCP Server

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Python
Hybrid

STRING: Retrieve functional annotations for proteins

string_functional_annotation

Retrieve curated functional annotations for proteins, including Gene Ontology, KEGG, tissue, compartment, and domain terms to explain what they do, where they act, or which pathways they join.

Instructions

This tool retrieves curated functional annotations for a set of proteins.

Each input protein is mapped to known biological terms from ontologies, pathway databases, tissues, compartments and domains — such as Gene Ontology (GO), KEGG, and UniProt Keywords.

  • Use this when the user asks what a protein does, where it's localized, expressed, or which pathways it participates in.

  • Keep the output short and focused by highlighting a few diverse and specific annotations for each protein.

  • This tool does not perform statistical enrichment — use the enrichment tool for that.

Output fields (per protein):

  • stringId: STRING protein identifier

  • preferredName: Gene name or alias

  • annotation: Functional description or keyword

  • category: Source category (e.g. GO, KEGG, Keyword)

  • term: Functional term or ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
speciesNoNCBI/STRING taxon (e.g. 9606 for human, or STRG0AXXXXX for uploaded genomes).
identifiersYesSeparate multiple protein queries by %0d.
detail_for_termNoExact functional term ID to return with the full list of matching input proteins. Use this when a previous result says a protein list was shortened, omitted, or replaced with 'many'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.13.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden, and it does well: it discloses the output style ('short and focused, highlighting a few diverse and specific annotations'), implying truncated result sets rather than exhaustive output. It stops short of quantifying the cap or describing the 'many' substitution behavior in the main body, which would be the last piece of behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then bullets for usage and a clean field list; the alternative-routing sentence sits where it belongs. The per-protein output field enumeration is the one section that does not earn its space, since an output schema already exists and covers return values.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-oriented annotation lookup with a full output schema and 100% schema coverage, the description covers purpose, usage, exclusions, and result character adequately. It is arguably redundant to restate output fields rather than spending that space on behavior such as result-set size limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents species, identifiers, and detail_for_term, including the recovery workflow for shortened lists. The description adds no per-parameter syntax or format detail beyond what the schema states, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('retrieves curated functional annotations for a set of proteins') and enumerates the source ontologies (GO, KEGG, UniProt Keywords). It also explicitly distinguishes itself from the enrichment sibling, so an agent can route correctly without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use condition ('when the user asks what a protein does, where it's localized, expressed, or which pathways it participates in') and an explicit when-not with the named alternative ('does not perform statistical enrichment — use the enrichment tool for that'). Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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