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corpus

vela.corpus.query

vela.corpus.query
Read-only

Ask the corpus by COORDINATE rather than keyword. Filter works by axis weights (e.g. religion ∈ {T,s} AND work ∈ {T,s} returns the God-and-Work source set across genres), controlled themes, qualities/emotions, genre, and free text. Returns card metadata: the one-line claim, themes, coordinates, qualities. FREE and read-only.

[capability: library-extraction-cards · mirrors: POST /api/corpus/query]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
axesNoAxis → allowed weights. ALL listed axes must match. e.g. {"religion":["T","s"],"work":["T","s"]}
textNoSubstring match over title / author / one-line claim.
genreNo
limitNo
topicNoCoordinate system to query (topic_configs.slug).being-human
offsetNo
themesNoCards citing ANY of these controlled themes.
qualitiesNoCards carrying ANY of these qualities (emotions, for being-human).
include_passagesNoInclude theme-anchored passage pointers.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsYes
topicYes
totalYes
appliedYes
returnedYes
truncatedNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false; the description reinforces this with 'FREE and read-only.' It adds coordinate-filter behavior and return metadata but does not disclose additional concerns like auth requirements, rate limits, or result ordering. No contradiction with annotations.

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 dense sentences front-load the core distinction ('by COORDINATE rather than keyword'), then list filters and the return payload. 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?

For a 9-parameter, 0-required tool, the description covers the query model, an example filter expression, return metadata, and read-only/free behavior; the output schema and default topic fill in the rest. It could be slightly richer on discovering valid controlled-theme/quality values and pagination, but those are partially covered by schema and sibling tools.

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?

With 67% schema coverage, the description adds meaningful semantics by explaining axis-weight composition ('religion ∈ {T,s} AND work ∈ {T,s}') and linking 'qualities/emotions' to the being-human vocabulary. It does not explain limit/offset/genre in prose, but those are structuvalid documented in the schema.

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 specific operation — ask/query the corpus by coordinate rather than keyword — and enumerates the filter dimensions and the return payload (card metadata). This clearly differentiates it from sibling corpus tools like describe_topic, list_topics, ground_claim, and witness.

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 frames when to choose this tool: coordinate-driven retrieval rather than keyword-first, and provides a concrete cross-axis example ('religion ∈ {T,s} AND work ∈ {T,s}'). It does not name an exact sibling alternative, but the intended usage context is clear enough for an agent to route correctly.

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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