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corpus_query

Format all observations from a named code corpus as markdown context with a question, enabling direct answers based on the corpus data.

Instructions

Format all observations of a named corpus as markdown context + a question, ready for answering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCorpus name previously built via corpus_build.
questionYesQuestion to answer with the corpus context.
projectNoProject name/path (default: active).
Behavior2/5

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

No annotations provided, so description is the sole source. Only states formatting but doesn't disclose side effects (e.g., does it modify the corpus?), auth needs, or behavior on missing corpus. The term 'format' is vague.

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?

Single, focused sentence that front-loads the purpose. No redundant words or filler.

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?

No output schema, so description should clarify return format. It says 'markdown context + a question' but lacks specifics (e.g., structure, example). Error conditions not covered. Adequate for simple query tool but could be richer.

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?

All three parameters are described in the input schema (100% coverage). The description adds 'ready for answering' but no extra semantics like default project or constraints on question format. Baseline 3 is appropriate.

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?

Description clearly states the action (format), resource (observations of a named corpus), and output (markdown context + question). Distinguishes from sibling corpus_build by specifying it operates on an already-built corpus.

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?

Implies usage after corpus_build, but no explicit guidance on when to use vs alternatives or when not to use. No mention of prerequisites like corpus existence.

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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