Skip to main content
Glama

record_decision

Record the final classification, confidence, and origin for a metabolite entry, determining whether it is endogenous, exogenous, or xenobiotic-excluded based on provenance.

Instructions

Commit the LLM's identity CALL for one entry — the ONLY tool that sets final_class/confidence/origin. origin is the compound's PROVENANCE and, for non- endogenous compounds, decides the class:

  • BIOLOGICAL but from outside the host -> origin in {diet, drug, microbial, plant}; final_class='exogenous' (KEPT + tagged; it is a real signal, may carry KEGG/HMDB IDs).

  • NON-biological / technical -> origin in {contaminant, industrial, additive, surfactant, plasticizer, reagent}; final_class='xenobiotic-excluded' (dropped from analysis).

  • host-produced -> origin='endogenous' (default for KEGG/HMDB/structure-only mappings).

For an exclusion pass final_class='xenobiotic-excluded' (or 'unmapped') with accepted={}; origin is auto-suggested from the contaminant lexicon when omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originNo
workdirYes
acceptedNo
rationaleYes
confidenceNo
feature_idYes
final_classNo
Behavior3/5

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

With no annotations, the description must carry the full burden. It discloses the core behavior (setting fields based on origin logic) and provides detailed rules. However, it omits important operational details such as idempotency, error states, required permissions, or what the function returns, limiting transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat verbose and dense, mixing core purpose with detailed conditional logic. It is front-loaded with the main verb and resource, but the length and lack of structure (e.g., bullet points) reduce readability. Every sentence earns its place, but brevity could improve.

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 the tool's complexity (7 parameters, no output schema, no annotations), the description covers the essential decision logic but fails to address the return value, error scenarios, or additional side effects. It is adequate for a focused agent but not fully complete.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It explains 'origin' and 'final_class' in detail, adding significant meaning. However, other parameters like 'workdir', 'accepted', 'rationale', and 'confidence' are not described, leaving gaps that the schema (only types) does not fill.

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: 'Commit the LLM's identity CALL for one entry — the ONLY tool that sets final_class/confidence/origin.' It uses a specific verb ('Commit') and explicitly distinguishes itself from sibling tools as the sole tool for this critical action.

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 implies when to use it (to commit a decision for an entry) and explicitly states it is the only tool for setting these fields, providing context. However, it lacks explicit guidance on when not to use it or prerequisites (e.g., feature must be identified first), which keeps it from a 5.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Wooyoung-kim91/metabo-idmapper'

If you have feedback or need assistance with the MCP directory API, please join our Discord server