Skip to main content
Glama

STRING Database MCP Server

by org.string-db

STRING: Get links to interaction evidence pages

string_interaction_evidence

Retrieves direct links to STRING evidence pages for protein–protein interaction pairs.

Use this tool only when a STRING evidence page/link is needed. To determine whether an interaction is supported, use string_interactions_query_set.

It returns URLs linking to STRING’s evidence pages, which display the underlying data sources (experimental results, publications, and curated databases) supporting each predicted interaction.
A URL can be generated even for unsupported pairs; the URL is not itself an interaction verdict.

Parameters:

  • identifier_a: Query protein identifier (Protein A)

  • identifiers_b: One or more target protein identifiers (Protein B), separated by %0d

  • species: NCBI taxonomy ID (e.g. 9606 for human or 10090 for mouse)

Typical user questions that should trigger this tool:

  • "Can you show me the STRING evidence for this interaction?"

  • "Show me the details supporting this interaction."

  • "What supports the interaction between TP53 and MDM2?"

  • "Where can I find the STRING evidence for this pair?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
speciesNo
identifier_aYesRequired. Protein A identifier.
identifiers_bYesRequired. One or more protein B identifiers, separated by %0d.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses a key nuance: 'A URL can be generated even for unsupported pairs; the URL is not itself an interaction verdict.' It also explains what the evidence pages display, which adds meaningful context beyond the basic retrieval purpose.

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 and front-loaded with the purpose. The parameter list and typical user questions add value but make it slightly longer than necessary. The caveat about unsupported pairs is placed prominently, and the overall organization 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, usage, and output nature, and the presence of an output schema covers return details. The only notable gap is that it doesn't mention the `species` parameter is optional with a null default, which could lead to confusion given the schema marks it as 'Required' in its description. Otherwise, it is complete.

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 description adds clarity to the two required parameters by restating them in plain language and adding the separator detail for `identifiers_b` ('separated by `%0d`'). It also provides concrete species examples (9606, 10090) that go beyond the schema's description. Since schema coverage is 67%, this added context helps compensate.

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 direct links to STRING evidence pages for protein–protein interaction pairs.' It clearly distinguishes itself from the sibling tool by noting that `string_interactions_query_set` should be used to determine if an interaction is supported, making the purpose unambiguous.

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 when to use this tool with 'Use this tool only when a STRING evidence page/link is needed' and provides the alternative: 'To determine whether an interaction is supported, use `string_interactions_query_set`.' It also lists typical user questions that should trigger the tool, giving clear contextual guidance.

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

All STRING Database MCP Server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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