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build_corpus

Read-onlyIdempotent

Save a snapshot of project context to disk for consistent LLM priming across multiple queries.

Instructions

Pack a slice of project context into a persistent corpus on disk so future query_corpus calls can prime an LLM with the same snapshot without re-running the pack pipeline. Mutates the corpora store; returns JSON with the saved manifest. Pair with query_corpus for "ask this codebase" workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCorpus slug — alphanumeric + dash + underscore, ≤64 chars, must start with a letter or digit
scopeYesPack scope: project (whole repo), module (subdirectory), feature (NL query rank)
module_pathNoSubdirectory path when scope=module (e.g. "src/auth")
feature_queryNoNatural-language query when scope=feature (e.g. "JWT auth and refresh flow")
token_budgetNoToken budget for the packed body (default 50000)
pack_strategyNoPack strategy: most_relevant (default; feature/PageRank ranked), core_first (PageRank wins, surfaces architecturally central code), compact (signatures only — drops source bodies, lets outlines cover much more of the repo per token)
descriptionNoOptional human-readable description stored on the manifest
overwriteNoReplace an existing corpus with the same name (default false)
Behavior1/5

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

Description states 'Mutates the corpora store', which contradicts the annotation readOnlyHint: true. This is a serious inconsistency that undermines trust in behavioral claims. Without annotations, the description would still need to clarify mutation details.

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 sentences, front-loaded with the core purpose, no fluff. Every sentence adds value, including the pairing suggestion.

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?

Description explains high-level purpose and returns JSON manifest but lacks details on mutation behavior, output structure, and edge cases (e.g., overwrite behavior). Schema covers parameters well, but no output schema increases the burden on description.

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 description adds no additional parameter meaning beyond the schema. Description does not elaborate on any parameter details, meeting the baseline but not exceeding.

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 uses specific verb 'Pack' and resource 'slice of project context into a persistent corpus'. It explains the purpose of saving context for future query_corpus calls, distinguishing it from siblings like query_corpus (queries) and delete_corpus (deletes).

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?

Description suggests pairing with query_corpus for 'ask this codebase' workflows, providing clear context. However, it does not compare to similar tools like pack_context or explicitly state when not to use it, leaving room for improvement.

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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