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mesa-clm

mesa-clm

by idss-mesa
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mesa_avu_from_term

Converts an OLS term and user value into a canonical iRODS AVU triple with attribute, value, and CURIE unit, without writing to iRODS.

Instructions

Pure transformation: turn an OLS term + user value into the canonical AVU triple (attribute='.<snake_case_label>', value=, unit=) without writing to iRODS.

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": {
      "ontology_id": "<ontology_id>",
      "value": "<value>"
    },
    "name": "mesa_avu_from_term"
  }
}

Input Schema

NameRequiredDescriptionDefault
iriNoFull IRI of the term to use. Either ``iri`` or ``curie`` must be supplied, alongside ``label`` if you want to skip the OLS lookup.
curieNoTerm CURIE (e.g. 'ENVO:00000428') — used directly as the AVU unit.
labelNoTerm label. If omitted, mesa-mcp fetches it from OLS using ``ontology_id`` + ``iri``.
valueYesUser-supplied AVU value. Often the term label, but free-form.
ontology_idYesOntology identifier (e.g. 'envo').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the key side-effect profile ('Pure transformation', no iRODS write) plus the exact output structure (attribute/value/unit). It omits whether an OLS network lookup occurs and any error behavior, leaving a modest gap.

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?

A single dense sentence with the transformation and the no-write constraint front-loaded; every clause earns its place with no 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?

There is no output schema, so the description correctly compensates by describing the returned triple. Combined with the thorough input schema this is nearly complete, though OLS lookup/fallback behavior is left to the schema.

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 100%, so all five parameters are already documented, including the iri/curie/label interplay. The description's attribute='<ontology>.<snake_case_label>' and unit=<CURIE> mapping adds useful framing but mostly restates what the schema already conveys.

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?

States a precise verb and transformation ('turn an OLS term + user value into the canonical AVU triple') and even spells out the resulting triple shape. The phrase 'without writing to iRODS' cleanly separates it from the write-oriented sibling mesa_avu_apply_term.

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 'Pure transformation ... without writing to iRODS' framing tells the agent this is for computing an AVU triple rather than persisting one, implicitly steering toward mesa_avu_apply_term for writes. It never names the alternative explicitly or states exclusions, so it stops short of a full 5.

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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