Skip to main content
Glama
meringlab

Official STRING Database MCP Server

STRING: Get homologs in specified target species

string_homology

Finds homologs by retrieving Smith–Waterman bit scores for query proteins, optionally against specified NCBI species or clades, then filters low scores and returns top matches.

Instructions

Retrieves pairwise protein similarity scores (Smith–Waterman bit scores) for the query proteins.

  • If no target species (species_b) is provided, results are intra-species (within the query species).

  • To retrieve homologs in other species or clades (e.g. vertebrates, yeast, plants), specify one or more NCBI taxon IDs in species_b.

  • Multiple target species are supported; ask the user to clarify if needed.

  • Always report species names together with their taxon IDs.

  • Bit scores < 50 are not reported.

  • Results are truncated to the top 50 proteins per input protein.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
speciesNoNCBI/STRING taxon (e.g. 9606 for human, or STRG0AXXXXX for uploaded genomes).
proteinsYesOne or more protein identifiers, separated by %0d.
species_bNoOne or more NCBI taxon IDs for target species, separated by comma (e.g. 9606,7227,4932 for human, fly, and yeast).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.13.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the Smith-Waterman scoring method, the <50 bit-score reporting cutoff, and truncation to the top 50 proteins per input. It omits rate limits, error behavior, and authentication needs, keeping it just below the top score.

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?

Front-loads the core purpose in the opening sentence, then presents usage, constraints, and output behavior as scannable bullets. No filler; every bullet carries actionable information.

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?

For a read-only retrieval tool with an output schema covering return values, the description supplies everything needed to invoke it correctly: parameter behavior, the reporting threshold, and truncation limits. Despite absent annotations, the missing safety profile is low-risk for an obvious read operation.

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

Parameters4/5

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

Schema coverage is 100% so the baseline is 3, but the description adds genuine semantic value: it explains that omitting species_b yields intra-species results and that species_b accepts clades and multiple comma-separated IDs, plus the reporting convention for species names and taxon IDs. This meaning goes beyond the raw schema text.

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 pairwise protein similarity scores / homologs') and clarifies scope (intra-species vs. cross-species). An agent can tell this apart from string_sequence_search or string_resolve_proteins based on the homology-scoring focus without opening the schema.

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

Usage Guidelines4/5

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

Gives clear when-to-use conditions: omit species_b for intra-species results, supply one or more taxon IDs (or clades) for cross-species. Explicitly tells the agent to ask the user to clarify when multiple target species are ambiguous. It stops short of naming sibling alternatives like string_sequence_search to route away from.

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