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propaths-mcp

propaths-mcp

by Tahsin-Kazi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_protein

Read-onlyIdempotent

Retrieve a protein's compact summary: key metadata, pathway roots, and interaction headlines to quickly assess its roles without verbose details.

Instructions

Overview map for one protein, the main entry point.

Returns a COMPACT overview sized for an agent: protein metadata, counts, the top-level pathway roots, and one headline row per interaction (id, oriented source/target, kind, direction, type, a one-line supportSummary, resolved pathway names, and function/evidence counts). It deliberately omits the heavy per-edge mechanism and evidence prose (the full protein page is ~700K tokens for a hub protein).

Drill into any row by id for full depth: get_interaction(id) for an edge's mechanism + kinetics + evidence.

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": {
      "symbol": "<symbol>"
    },
    "name": "get_protein"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so safety is covered. The description goes beyond annotations by explaining the compactness rationale ('~700K tokens for a hub protein'), listing the fields returned (metadata, counts, pathway roots, headline rows per interaction), and explicitly stating what is omitted (heavy per-edge mechanism and evidence prose). This gives the agent crucial expectations about response size and content. No contradiction with annotations.

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 about 140 words, well-structured with a clear opening statement of purpose, followed by a list of return contents, an explicit note on omitted data for token efficiency, and a direct drill-down instruction. Every sentence adds value; no fluff. The crucial scope note ('compact overview', 'main entry point') is front-loaded.

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 tool with one parameter and no output schema, the description is mostly complete: it details the return structure (fields per row, counts, pathway roots), notes what is omitted, and gives a follow-up action. The only major omission is the definition of `symbol`, which is critical for correct invocation. Lacking that, completeness is reduced, but the rest is well covered.

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

Parameters1/5

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

The schema has one required parameter `symbol` with only a title 'Symbol' and no description. Schema description coverage is 0%, so the tool description must compensate. It does not. The description never explains what `symbol` stands for (e.g., gene symbol, UniProt ID, common name), leaving the agent to guess. This is a critical gap for a single-parameter tool.

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: 'Overview map for one protein, the main entry point.' It specifies the resource (protein) and the action (get overview). It further distinguishes itself from siblings by explaining it returns a compact overview and explicitly names the sibling `get_interaction` for deeper detail. This is specific and unambiguous.

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 provides clear usage context: it is the 'main entry point' for exploring a protein. It explicitly directs the agent to `get_interaction(id)` for drilling into edge details ('mechanism + kinetics + evidence'). This covers the most relevant alternative. However, it does not mention other sibling tools like `search_proteins` or `get_pathway_tree`, so guidance is not exhaustive, but sufficient for common use cases.

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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