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reactome-mcp

reactome-mcp

by tc2fh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_interactors

Fetch curated protein-protein interactors for a UniProt or ChEBI accession, with pagination and interaction scores from IntAct.

Instructions

Fetch curated protein-protein interactors for an accession (IntAct).

Args:
    accession: A UniProt/ChEBI accession, e.g. "P04637".
    page: 1-based page number; -1 (default) returns all.
    page_size: Page size; -1 (default) returns all.

Returns:
    The interactor summary object, including the list of interacting
    accessions and their interaction scores.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "accession": "<accession>"
    },
    "name": "get_interactors"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNo
accessionYes
page_sizeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the return format (interactor summary object with accessions and scores) and the -1 default behavior for pagination parameters. It doesn't mention error cases, but it is transparent about the core behavior.

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 one-line summary followed by Args and Returns sections. Every sentence provides useful information, and there is no redundancy or filler.

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?

For a simple read tool with no output schema, the description covers the essential behavior, parameters, and return format. It could add details about edge cases (e.g., empty results), but it gives enough context for correct selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must explain the parameters. It does so thoroughly: 'accession: A UniProt/ChEBI accession', 'page: 1-based page number; -1 (default) returns all', and 'page_size: Page size; -1 (default) returns all'. This fully compensates for the schema's lack of descriptions.

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 clearly states the tool's purpose: 'Fetch curated protein-protein interactors for an accession (IntAct)'. It uses a specific verb ('fetch') and resource ('protein-protein interactors'), and differentiates from siblings like get_event_participants by specifying IntAct and protein-protein interactors.

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?

The description gives clear context: use this tool to retrieve interactors for a given UniProt/ChEBI accession, with pagination options. It does not explicitly name alternatives or exclusions, but the use case is clearly implied.

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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