metabo-idmapper

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search_synonym

Look up metabolite synonyms from KEGG, PubChem, and ChEBI to verify candidate identifiers proposed by an LLM, returning matches for manual review.

Instructions

Search KEGG / PubChem / ChEBI for LLM-proposed query strings (abbrev expansions, typo fixes, synonyms). Returns candidates; the LLM picks + verifies. Never auto-accepts.

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": {
      "workdir": "<workdir>",
      "feature_id": "<feature_id>",
      "queries": "<queries>"
    },
    "name": "search_synonym"
  }
}

Input Schema

Name	Required	Description	Default
`dbs`	No
`queries`	Yes
`workdir`	Yes
`feature_id`	Yes
`max_per_term`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.1Jul 28, 2026

TDQS

A3.5/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses the tool returns candidates and the LLM must pick and verify, signaling it is read-only and non-committal. However, it omits details about error behavior, rate limits, or whether it modifies any state.

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 two concise sentences, front-loaded with the core action (search databases for query strings) and immediately followed by critical behavioral notes (returns candidates, never auto-accepts). No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the purpose is clear, the description lacks details about parameters and output format. With 5 parameters, no output schema, and no annotations, the agent is insufficiently equipped to use the tool correctly without external knowledge.

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 description provides no information about the five parameters (workdir, feature_id, queries, dbs, max_per_term). Schema coverage is 0%, so the agent has no guidance on how to set these fields, risking incorrect usage.

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 searches KEGG, PubChem, and ChEBI for query strings like abbreviation expansions, typo fixes, and synonyms. It distinguishes itself from siblings like exact_match by emphasizing it returns candidates and the LLM must pick and verify.

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 specifies the tool is for LLM-proposed query strings (abbrev expansions, typo fixes, synonyms) and that it never auto-accepts, implying a manual verification step. However, it does not explicitly mention when not to use it or point to alternative tools.

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