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STRING Database MCP Server

by org.string-db

STRING: Query species and clades in STRING

string_query_species

Search for species or clades available in STRING by free-text query and return their NCBI taxonomy IDs.

  • Use this when the user asks which species or clades are present in STRING, or when you need the correct NCBI taxon ID to pass to other tools.

  • use this to resolve NCBI taxons IDs to their scientific names.

  • Accepts up to 100 taxon IDs separated by %0d.

  • The results are limited to the top 50 matches per query.

  • When the user asks for a species list, do not list clades.

  • If the requested species cannot be matched (i.e. the correct species is not present in the results), immediately invoke the 'string_help' tool with topic='missing_species'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
species_textYesRequired. One species/clade search term or multiple NCBI taxon IDs separated by carriage return (%0d). Examples: 'human', 'mouse', 'vertebrates', '511145', or '9598%0d10090'. For multiple queries, use taxon IDs rather than free-text names.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the burden. It discloses important behaviors: accepts up to 100 taxon IDs separated by %0d, limits results to top 50 matches, and instructs immediate fallback to string_help when a species isn't found. This goes well beyond the schema.

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

Conciseness5/5

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

The description is well-structured: a clear opening sentence followed by a focused bullet list covering use cases, constraints, and fallback behavior. Every sentence provides actionable information with no redundancy.

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

Completeness5/5

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

Given the presence of an output schema and the tool's moderate complexity, the description covers all critical aspects: purpose, usage timing, formatting constraints, result limits, and error handling. It is complete and self-sufficient.

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?

The input schema already provides 100% coverage for the single parameter, including examples and guidance to use taxon IDs for multiple queries. The description adds usage context but does not significantly expand the parameter's meaning beyond what the schema documents.

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 explicitly states the tool searches for species or clades in STRING and returns NCBI taxonomy IDs. It distinguishes itself from siblings by focusing on species/clade queries and taxonomy ID resolution, rather than protein interactions or annotations.

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 provides explicit use cases: 'when the user asks which species or clades are present in STRING' and 'when you need the correct NCBI taxon ID to pass to other tools'. It also includes a clear exclusion ('do not list clades when species list requested') and a fallback instruction to invoke string_help for missing species.

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.

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