Skip to main content
Glama

doc_index_repo

Index a GitHub repository's documentation by fetching .md and .txt files, parsing sections, and saving to local storage for efficient 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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesGitHub repository URL or owner/repo string
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.
use_ai_summariesNoUse AI to generate section summaries.
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
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.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the file types processed (.md/.txt), the parsing and storage steps, and the auto-enabling condition for embeddings based on environment configuration. This covers key behavioral traits without 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?

The description is extremely concise at three sentences, with no filler. Each sentence adds distinct value: core action, process steps, and embedding configuration condition. Front-loaded with the verb and resource.

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

Completeness2/5

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

Despite no output schema, the description does not mention what the tool returns after indexing (e.g., success message, index summary). It also omits details about overwrite behavior or side effects on existing indices. For a tool with 6 parameters, this is a gap in completeness.

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?

The input schema already provides 100% parameter description coverage, so the description does not add significant new meaning beyond what the schema offers. The description's mention of embeddings aligns with the use_embeddings parameter but does not deepen understanding of other parameters.

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 action (Index), the resource (GitHub repository's documentation), and the process (fetches .md/.txt files, parses sections, saves to local storage). It uniquely distinguishes from sibling tools like index_local which targets local directories.

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?

The description implies usage context (when you need to index a GitHub repo's docs) but does not explicitly state when to use this tool vs. alternatives like index_local or other indexing tools. No when-not conditions or alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jgravelle/jdocmunch-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server