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STRING: Get all interaction partners for proteins

string_all_interaction_partners

Retrieve all known interaction partners for one or more proteins from the STRING database. Use it to answer questions like 'What does TP53 interact with?' and filter by required_score.

Instructions

Retrieves all interaction partners for one or more proteins from STRING.

This tool returns all known interactions between your query protein(s) and any other proteins in the STRING database.

Use this when asking “What does TP53 interact with?”
It differs from string_interactions_query_set, which only shows interactions within the input set or a limited extension of it.

You can filter for strong interactions using required_score.

Evidence scores:
nscore (neighborhood), fscore (fusion), pscore (phylogenetic profile),
ascore (coexpression), escore (experimental), dscore (database), tscore (text mining)

Input Schema

TableJSON Schema

Name	Required	Description
`species`	No	NCBI taxonomy ID (e.g. 9606 for human) or STRING genome ID (e.g. STRG0AXXXXX for uploaded genomes). Only set when required.
`identifiers`	Yes	One or more protein identifiers, separated by carriage return (%0d).
`network_type`	No	Omit for the default functional network. Its typed view can include physical and directed regulatory attributes when STRING returns them; inspect `physical` and `regulatory.directions` before claiming those edge types. Set physical for binding, complex, or co-complex questions. Set regulatory for directed regulatory relationships between proteins.
`network_flavor`	No	Defaults are typed for functional networks, evidence for physical networks, and confidence for regulatory networks. Typed returns functional pairs with any physical and directed regulatory attributes that STRING reports; it does not make every pair physical or regulatory. Typed is available only for functional networks. Set evidence or confidence only when the user requests that edge display style.
`required_score`	No	Minimum interaction score to include. Omit unless a confidence threshold is requested or a broader/narrower threshold is needed.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.13.0Sep 30, 2026

TDQS

A4.3/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose meaningful scope behavior — that results extend to any protein in STRING rather than the input set — which is genuinely useful. However, it says nothing about result-size limits, truncation, pagination, or cost of a broad query, all of which matter for a tool that can return unbounded partner lists.

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?

Front-loads the core statement and the sibling differentiation, then uses bullets for use cases and evidence abbreviations. Reasonably tight, though the evidence-score list is a slight digression from the tool's selection decision and could be trimmed or deferred.

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 return values needn't be explained, and the description covers scope, alternative tool, and threshold filtering. It stops short of noting practical limits (very large result sets, species inference for non-human identifiers), which would matter for a multi-partner retrieval tool.

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 schema baseline is 3, but the description adds semantics the schema lacks: what `required_score` is for (confidence threshold filtering) and the meaning of the evidence channel abbreviations (nscore, fscore, pscore, etc.). It adds no extra guidance for `species` or `identifiers` beyond the schema.

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 all interaction partners for one or more proteins from STRING') and explicitly contrasts itself with the sibling `string_interactions_query_set`, which only covers interactions within the input set. 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?

Gives a concrete use case ('What does TP53 interact with?') and an explicit alternative plus the condition that selects it (in-set interactions → `string_interactions_query_set`). It also names the condition for tightening results via `required_score`.

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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