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STRING Database MCP Server

by org.string-db

STRING: Retrieve functional annotations for proteins

string_functional_annotation

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
speciesNo
identifiersYesSeparate multiple protein queries by %0d. e.g. SMO%0dTP53
detail_for_termNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It explains that output is curated and intentionally short ('Keep the output short and focused'), mentions the types of data sources (GO, KEGG, UniProt Keywords), and discloses a limitation (no enrichment). It does not mention potential truncation details, but the schema parameter detail_for_term fills in that gap.

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?

The description is well-structured with a clear opening sentence, bullet points for usage, and an output field list. It is somewhat long but every section adds useful information; the front-loaded purpose statement makes it easy to scan.

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?

The description covers the tool's purpose, appropriate use cases, a key alternative, and detailed output fields. With an output schema present, it does not need to restate return types. It could mention the requirement for species, but the schema covers that, so overall completeness is strong.

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

Parameters2/5

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

The tool description does not explain any of the three parameters by name or provide usage details. Schema description coverage is only 33%, and the description does not compensate by clarifying how to format identifiers, supply species, or use detail_for_term. The output field list is helpful but does not address parameter semantics.

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?

The description opens with a specific verb and resource: 'retrieves curated functional annotations for a set of proteins.' It clearly distinguishes itself from sibling tools by explicitly stating it does not perform statistical enrichment and points to the enrichment tool for that purpose.

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?

The description gives explicit when-to-use guidance: 'Use this when the user asks what a protein does, where it's localized, expressed, or which pathways it participates in.' It also provides an exclusion and alternative: 'This tool does not perform statistical enrichment — use the enrichment tool for that.'

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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TDQS

A3.6/5.0
Disambiguation2/5

Several tool pairs have overlapping purposes: string_network_link and string_visual_network both return network URLs with nearly identical behavior, and string_all_interaction_partners vs string_interactions_query_set both query interactions, differing subtly in scope. The enrichment/annotation tools also share boundaries, making it difficult for agents to disambiguate without reading detailed descriptions.

Naming Consistency3/5

All tools share the 'string_' prefix, but the pattern is inconsistent: some are verb-first (create_file, resolve_proteins), some are noun-first (interactions_query_set, network_link), and some are bare nouns (enrichment, homology). This mixed convention reduces predictability.

Tool Count4/5

At 17 tools, the server is slightly above the ideal range, but it covers a broad domain: interactions, networks, enrichment, annotation, homology, species resolution, and sequence search. The count is justified by the complexity of the STRING database.

Completeness4/5

The tool set covers core STRING workflows: querying interactions, constructing networks, computing enrichment, retrieving annotations, identifying homologs, mapping IDs, and exporting data. Minor gaps exist (e.g., direct edge-list tables and intricate interaction score details), but the overall surface is robust.

Resources