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Glama

harvest.compose

Compose balanced training data mixes from a catalog of candidate items to match a given goal, returning selected picks with rationale without requiring an LLM.

Instructions

Compose a balanced mix from a catalog of items with the local mixer — no LLM/mouth required. Returns picks with rationale.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesWhat the training library is for, e.g. 'small vision-language model'.
catalogYesCandidate items (e.g. from harvest.search).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds useful context ('local mixer — no LLM/mouth required' and 'returns picks with rationale'), but it does not discuss permissions, side effects, failure modes, or how 'balanced' is determined. The unusual 'no LLM/mouth' phrasing also reduces clarity.

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

Conciseness4/5

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

The description is compact and front-loaded with the core action before the return statement. The only blemish is the unclear 'no LLM/mouth' phrase, but the sentence is otherwise economical and free of redundancy.

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?

For a two-required-param tool with a fully described schema and an output statement, this is workable. However, it never defines what 'balanced' means, when composition might fail, or how the rationale is structured, and there is no output schema to fill those gaps.

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 the schema already documents both parameters. The plain-language description adds context by framing the catalog as candidate items and the goal as the purpose of the training library, but it does not substantially enrich parameter meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete action ('compose a balanced mix'), a specific resource ('a catalog of items'), and an output ('returns picks with rationale'). It distinguishes the tool from search-like siblings by emphasizing the local mixer and selection output, though it does not explicitly name alternatives like harvest.pack.

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

Usage Guidelines3/5

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

The intended use is implied: supply a goal and a catalog of candidate items to get a balanced selection. There is no explicit when-to-use guidance, no mention of when to prefer a sibling, and no exclusion criteria.

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