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STRING Database MCP Server

STRING: Create a downloadable STRING result file

string_create_file

Creates a downloadable file for STRING-derived results.

Use this tool when the user explicitly asks to download, save, export, or receive a file containing STRING data, tables, protein lists, enrichment results, networks, etc.

When a response would otherwise include a publication-style or supplementary result table, or another table clearly intended for reuse outside chat, mention that a downloadable TSV/CSV file can be generated on request. Ask whether they want the file, unless they already requested it. Do not create the file until the user asks for it.

Do not store unrelated data or full conversation transcripts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesRequired STRING-derived file content. For .tsv/.csv: one rectangular table, one header row, matching delimiter, no Markdown/prose/repeated headers/multiple tables. Use one row per entity, edge, cluster member, annotation, or enrichment term. Use bare numeric scores/FDR/p-values; put interpretation and caveats in chat or .md/.txt.
filenameYesRequired. Suggested output filename with a safe extension such as .tsv, .csv, .json, .md, or .txt. Match content to the extension; prefer .tsv for reusable tabular STRING data. Use a concise name that reflects the STRING analysis result, for example string-enrichment.tsv.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4/5.0
Behavior3/5

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 does disclose key behaviors: do not create the file until asked, and do not store unrelated data. However, it doesn't mention potential side effects (e.g., file storage duration, access controls, overwrite behavior) or what the tool returns. The output schema may cover return values, but the description alone leaves some gaps.

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 concise and well-structured: a clear opening sentence, followed by usage conditions, proactive guidance, and prohibitions. Every sentence adds value, and the structure front-loads the primary purpose before moving to procedural details.

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 the tool's simplicity and the presence of an output schema, the description is quite complete. It covers when to use, what to include, and what to avoid. It could mention error handling or the post-creation outcome (e.g., URL or file path), but the output schema likely covers returns. The guidance is sufficient for an agent to decide when and how to invoke this tool correctly.

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 parameters (filename and content) are fully documented in the schema. The description reinforces that content must be STRING-derived and mentions file types (.tsv, .csv, etc.), but adds little beyond the schema's detailed parameter descriptions. Baseline 3 applies since 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?

The description uses a specific verb ('Creates a downloadable file') and clearly specifies the resource (STRING-derived results). It distinguishes itself from sibling tools by focusing on file creation/export rather than analysis, and even lists the types of data that can be exported (tables, protein lists, enrichment results, networks).

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?

Explicitly states when to use this tool: 'when the user explicitly asks to download, save, export, or receive a file containing STRING data'. It also provides guidance on proactively mentioning downloadable files for publication-style tables and instructs not to create the file until the user asks. It doesn't explicitly name alternative sibling tools for when not to use it, but the context is clear.

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.