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ingest_repo

Transform a code repository into an AI-summarized Obsidian vault with searchable ChromaDB index for developer queries.

Instructions

Ingest a code repository into codelore — generates the vault and search index.

Accepts either a local directory path or a GitHub URL (https://github.com/owner/repo). Runs the full pipeline:

  1. Generates AI summaries for every file and directory via Claude CLI

  2. Writes an Obsidian-compatible vault of markdown notes

  3. Indexes code chunks as developer questions into ChromaDB

extra_frontmatter_json — optional JSON object of extra fields to add to every vault note's frontmatter, e.g. '{"project": "myapp", "status": "draft", "tags": ["backend", "python"]}'. Strings, numbers, booleans, and flat lists are all supported. These fields are merged after the built-in fields.

After ingestion, the tool prints the vault and chroma paths. Pass these as vault_root and chroma_path to the query tools, or set them as env vars.

WARNING: calls 'claude --print' once per file + directory + chunk. Run estimate_cost first on large repos.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_path_or_urlYes
vault_output_pathNo
extra_frontmatter_jsonNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses all behavioral traits: runs AI summaries via Claude CLI, writes Obsidian vault, indexes into ChromaDB, and warns about cost. No contradictions.

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?

Well-structured: clear first sentence, then parameter details, post-ingestion steps, and warning. No redundant sentences. Front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all aspects for a complex tool: inputs, pipeline steps, outputs, warnings, and links to other tools. Output schema exists, so return values need not be detailed. Complete for effective use.

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?

Schema description coverage is 0%, so description must compensate. It explains repo_path_or_url (local or URL) and extra_frontmatter_json in detail. vault_output_path is only implied in post-ingestion instructions, but overall adds significant meaning beyond bare 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?

Description clearly states the tool ingests a code repository into codelore, generating a vault and search index. It distinguishes from sibling tools like estimate_cost, explore_repo, etc., by specifying the full pipeline unique to this tool.

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

Usage Guidelines5/5

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

Explicit guidance on when to use: accepts local path or GitHub URL. Warns to run estimate_cost first on large repos, and directs to pass outputs to query tools. Provides clear context for usage.

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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