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STRING Database MCP Server

by org.string-db

STRING: Search proteins by amino acid sequence

string_sequence_search

Searches the STRING database using amino acid sequences to identify matching proteins.

  • Accepts a single sequence or multiple sequences in FASTA format.

  • Returns the most similar STRING protein(s) for the specified species, based on sequence similarity.

  • Use this when the protein identifier is unknown or unresolvable by string_resolve_proteins.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
speciesNoRequired. NCBI or STRING taxonomy ID. You can query with a clade or species. eg.g 2 for bacteria, 7742 for vertebrates, 511145 for E. coli
sequencesYesOne or more protein sequences in plain or FASTA format.For multiple sequences, use standard FASTA headers (lines beginning with '>'). Only amino acid sequences are supported — nucleotide sequences are not accepted.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It discloses that multiple sequences are accepted in FASTA format and that results are based on sequence similarity for a specified species. However, it does not mention potential limitations, error behaviors, or how similarity is determined. It is adequate but not rich.

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 compact and well-structured: a clear opening sentence followed by three concise bullet points. No filler or redundant information; every sentence adds value.

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?

Given that an output schema exists (covering return format) and the schema covers parameters well, the description is quite complete. It captures the main use case, input format, and key alternative. Minor gaps (e.g., behavior with no matches, species requirement emphasis) are acceptable because the schema and output schema fill in some details.

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?

The schema already covers both parameters with descriptions (100% coverage). The tool description adds meaningful semantic details beyond the schema: it specifies that sequences can be FASTA, that multiple sequences require headers, and that only amino acid sequences are accepted. This enriches the schema's description.

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 states a specific action ('Searches the STRING database') with a specific input ('amino acid sequences') and a clear goal ('identify matching proteins'). It differentiates from siblings by focusing on sequence-based search rather than identifier resolution, and the title reinforces this.

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 explicitly states 'Use this when the protein identifier is unknown or unresolvable by string_resolve_proteins', providing a clear use case and naming an alternative tool. This gives the agent direct guidance on when to choose this tool over a sibling.

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