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metabo-idmapper

metabo-idmapper

by Wooyoung-kim91
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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ingest_names

Load raw metabolite names, normalize them, and seed the ledger while flagging parenthetical abbreviations, combined names, and isomer-sensitive digit locants for LLM review.

Instructions

Load raw names (list OR an xlsx column), normalize, and seed the ledger.

Flags entries the LLM should look at: parenthetical abbreviations, combined names, and isomer-sensitive digit locants (never auto-substitute those).

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>"
    },
    "name": "ingest_names"
  }
}

Input Schema

NameRequiredDescriptionDefault
xlsxNo
namesNo
sheetNo
columnNo
workdirYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.1

TDQS

A3.7/5.0
Behavior3/5

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

The description mentions normalization, ledger seeding, and flags for LLM attention (parenthetical abbreviations, combined names, isomer-sensitive digit locants). However, without annotations, it lacks details on destructive potential, permissions, side effects, or reversibility. Partial disclosure.

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?

Two sentences: first sentence states core purpose concisely; second sentence adds critical behavioral nuance without redundancy. No wasted words.

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

Completeness3/5

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

Given 5 parameters, no output schema, and no annotations, the description covers input methods and a behavioral flag but omits details on return value, error cases, normalization specifics, and ledger semantics. Adequate but incomplete.

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?

The description adds meaning to parameters by explaining that input can be a list (names parameter) or an xlsx column (xlsx, sheet, column). This compensates for 0% schema coverage. However, the workdir parameter is not explained.

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 action: 'Load raw names (list OR an xlsx column), normalize, and seed the ledger.' It specifies the input sources and the processing pipeline, distinguishing it from sibling tools that perform different tasks like structure_lookup or exact_match.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description does not mention exclusions, prerequisites, or context for selection. The agent must infer usage from the tool purpose alone.

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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