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doc_index_repo

Indexes GitHub repository documentation by fetching .md/.txt files, parsing sections, and storing them locally for AI agent navigation.

Instructions

Index a GitHub repository's documentation. Fetches .md/.txt files, parses sections, and saves to local storage. Embeddings auto-enable when a provider is configured (GOOGLE_API_KEY, OPENAI_API_KEY, openai-compatible + JDOCMUNCH_OPENAI_COMPAT_URL + JDOCMUNCH_OPENAI_COMPAT_MODEL, or sentence-transformers). Indexes .md and .txt only; every other file in the repo is ignored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoOptional GitHub branch, tag, or commit-ish to index. If omitted, HEAD is used. The ref is resolved to a commit SHA before fetching content; repo@sha remains the durable lookup handle.
urlYesGitHub repository URL or owner/repo string
nameNoOptional stored index name override. If omitted, the GitHub repo name is used. Must be a safe storage component: letters, numbers, dot, underscore, and hyphen only.
incrementalNoWhen true (default), skip all HTTP fetches if the selected GitHub ref's commit SHA is unchanged; otherwise only re-index changed files. Set to false to force a full re-index.
use_embeddingsNoGenerate semantic embeddings for each section. true/false/"auto". "auto" (default) enables embeddings when an embedding provider is configured, including openai-compatible + JDOCMUNCH_OPENAI_COMPAT_URL + JDOCMUNCH_OPENAI_COMPAT_MODEL.auto
use_ai_summariesNoUse AI to generate section summaries.
Behavior4/5

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

The description discloses key behavioral traits: it only indexes .md and .txt files, ignores all other files, and auto-enables embeddings when a provider is configured. It also explains the incremental behavior (skips HTTP fetches if commit SHA unchanged). The annotation readOnlyHint=false is consistent with the description's implication of writing to local storage. The description adds value beyond the annotation by detailing the file-type filtering and embedding configuration.

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 concise and front-loaded with the main purpose. It uses two sentences to convey the core functionality and then adds a third sentence about file-type filtering. It's efficient with no wasted words, though it could be slightly more structured (e.g., bullet points for embedding providers).

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?

Given the tool's complexity (6 parameters, embedding configuration, incremental behavior), the description covers the essential aspects: what it does, what files it processes, how embeddings are enabled, and the incremental behavior. It doesn't explain the return value (no output schema), but that's acceptable since the tool likely returns a status or index ID. The description is complete enough for an agent to understand when and how to use it.

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?

The schema description coverage is 100%, so the schema already documents all parameters. The description adds context about the overall behavior (e.g., 'Indexes .md and .txt only') but doesn't add much per-parameter detail beyond what the schema provides. However, it does clarify the embedding auto-enable logic and the incremental behavior, which are not fully captured in the schema. Given the high schema coverage, a baseline of 3 is appropriate, but the description's additional context on embedding and incremental behavior justifies a 4.

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?

The description clearly states the tool's purpose: 'Index a GitHub repository's documentation.' It specifies the verb (Index), the resource (GitHub repository's documentation), and the scope (.md/.txt files). It distinguishes from siblings like index_local (which likely indexes local content) and doc_list_repos (which lists repos).

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 provides clear context on when to use this tool: to index GitHub repository documentation. It also explains what it does (fetches .md/.txt, parses sections, saves to local storage) and mentions the embedding auto-enable behavior. However, it doesn't explicitly state when NOT to use it or name alternative tools for different scenarios (e.g., if you need to index local files, use index_local).

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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