Skip to main content
Glama
meringlab

Official STRING Database MCP Server

STRING: Get enrichment result figure (image URL)

string_enrichment_image_url

Retrieve a STRING enrichment figure image URL for a set of proteins to visualize enriched terms for one category at a time.

Instructions

Retrieves a STRING enrichment figure (image URL) for a set of proteins. For the enriched terms and FDR values, use string_enrichment.

  • Each figure shows a single enrichment category; call again with another category to show a different one.

  • Use the same proteins and species as the network and enrichment results already shown to the user, so the figure matches them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
x_axisNoValue shown on the X-axis; also selects and orders the terms. If omitted, STRING uses signal.
speciesNoNCBI/STRING taxon (e.g. 9606 for human, or STRG0AXXXXX).
categoryNoTerm category for enrichment. If omitted, STRING uses Process. Use Process/Function/Component for GO, KEGG for KEGG pathways, RCTM for Reactome, and PMID for publications.
graph_typeNoPlot type: dotplot or barplot (horizontal bar chart). If omitted, STRING uses dotplot.
identifiersYesProtein identifiers, separated by %0d.
color_paletteNoColor palette for FDR. If omitted, STRING uses mint_blue.
group_by_similarityNoVisually groups terms based on term similarity. Default: 0.8. Details: string_help topic 'enrichment_grouping'.
number_of_terms_shownNoMax number of terms shown on plot. Default: 10.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.13.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden; it does disclose the key non-obvious behavior that each figure shows a single enrichment category and that multiple calls are required for multiple categories, and that outputs are image URLs. It omits any note on cost/rate limits or whether the call is side-effect free, but 'Retrieves' implies a read.

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-loaded single-sentence purpose followed by two tight bullets; every sentence adds routing or behavioral value and nothing is redundant.

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?

An output schema exists, so the description need not explain the returned figure details, and it covers the routing and repeat-call semantics an agent needs. Only a brief note on read-only/no side effects would make it fully self-contained for an 8-parameter tool.

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?

Schema description coverage is 100%, so the schema already documents every parameter including defaults and the enum meanings for category, x_axis, graph_type, and color_palette. The description only reinforces the `category` parameter indirectly; baseline 3 is correct when the schema does the heavy lifting.

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 (Retrieves) and resource (STRING enrichment figure / image URL) scoped to a set of proteins, and explicitly separates itself from the sibling `string_enrichment` which returns terms/FDR values. An agent can distinguish the two without opening either schema.

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?

Names the alternative tool and the condition selecting it ('For the enriched terms and FDR values, use `string_enrichment`'), explains that each call yields one category so repeat calls are needed, and instructs the agent to reuse the same proteins/species as already-displayed results so the figure matches.

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