Skip to main content
Glama
Sign In
ChunkHound

ChunkHound

by chunkhound
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Requirements

  • Python 3.10+

  • uv — install via curl -LsSf https://astral.sh/uv/install.sh | sh

  • API keys (optional — regex search works without any):

Related MCP server: Paparats: Local Semantic Code Search

AI writes code blind

Agents can generate code, but they still miss the context that makes software safe to change: how behavior flows across files, what changed across a branch or release, and which external constraints matter.

Reviewers, support, and product teams hit the same wall when large PRs, merge conflicts, bugs, and release notes need implementation-backed explanation instead of guesses.

ChunkHound turns current code, git history, and technical web research into cited context before anyone edits, reviews, debugs, or explains software.

Deep understanding for four context-heavy jobs

ChunkHound applies codebase understanding to the workflows where missing context hurts most.

Research before editing

Give coding agents grounded architecture context, relevant files, recent changes, and external constraints before they write code.

Understand large PRs and releases

Turn branch diffs, commit ranges, tags, and specific commits into cited engineering briefs for review, release notes, and changelog drafts.

Trace bugs and incidents

Turn symptoms, stack traces, and customer reports into likely code paths, recent changes, and external constraints.

Reconcile code with external docs

Pinpoint the technical docs, APIs, issues, and articles your implementation depends on, then connect that external evidence to local code research.

What you can ask

Ground an agent before edits

chunkhound research "How does authentication work?"
chunkhound search "JWT refresh token validation"
chunkhound research "What changed in auth recently?" --last-n 20

Understand a large PR or release

chunkhound research "Summarize the behavior changes on this branch for reviewers" --commit-range main..HEAD
chunkhound research "Draft changelog bullets for billing since v2.4" --commit-range v2.4..HEAD
chunkhound search "database migration" --commit-hash abc1234

Get context before resolving conflicts

chunkhound research "Why did auth session handling change on each side?" --commit-range main..feature/auth
chunkhound search "session refresh conflict" --last-n 50

Trace a bug with external constraints

chunkhound research "why would webhook retries fail?"
chunkhound research "what changed in webhook handling this week?" --last-n 30
chunkhound websearch "Stripe webhook retry schedule"

Explain product behavior

chunkhound research "What happens when a user cancels a subscription?"
chunkhound research "What changed in billing since v2.4?" --commit-range v2.4..HEAD

What powers deep understanding

  • Semantic code search — find relevant code by meaning, not only exact text

  • Cited code research — explain behavior across files with source citations

  • Git history research — ask by last N commits, commit hash, tag, branch, or range to understand large PRs and releases

  • Pinpoint web research — bring cited external docs, APIs, issues, and articles into the same workflow as local code research

  • Autodoc — generate shareable docs from code-backed research

  • Local-first indexing — keep code search and indexing under your control

  • Python, JavaScript, TypeScript, Java, Go, Rust, C/C++, and more via Tree-sitter

Install

uv tool install chunkhound

Try it

chunkhound index .
chunkhound research "How does authentication work?"

Index once, ask a real architecture question, and get a grounded answer with citations. Regex search works without providers. Semantic search requires an embedding provider. Deep research requires an LLM provider and an embedding provider with reranking support; web research uses the same provider stack. Choose local providers for zero-code-egress setups.

For a full configurable setup, create .chunkhound.json in your project root:

{
  "embedding": { "provider": "voyageai", "api_key": "your-key" },
  "llm": { "provider": "claude-code-cli" }
}

For editor integration, all provider options, and advanced configuration:

→ chunkhound.ai/docs/getting-started

Search git history

In addition to searching your indexed codebase, ChunkHound can search code changes across git history — useful for understanding what changed in a PR, a release, or since a specific commit.

# Last N commits
chunkhound search "authentication changes" --last-n 20

# Changes introduced by a specific commit
chunkhound search "database migration" --commit-hash abc1234

# Custom git range
chunkhound search "API changes" --commit-range v2.0..HEAD

# Deep research over recent changes
chunkhound research "what changed in the auth module?" --last-n 50

--vector-source controls scope: diff (default, changed code only), both (merges diff + DB), db (ignore diff).

Good fit

ChunkHound is especially useful for:

  • large repos and monorepos

  • multi-language codebases

  • legacy systems

  • local-only or security-sensitive environments

  • engineering teams that want agents, support, and product questions grounded in the same code index

Community

ChunkHound is MIT licensed, open source, and community built.

License

MIT

Available Tools

2 tools
daemon_statusA

Report daemon startup, scan, and realtime indexing health.

USE FOR:

  • Checking whether initial indexing has completed

  • Inspecting backend-neutral realtime health and resync state

  • Debugging degraded daemon behavior without opening log files

OUTPUT: {status, query_ready, scan_progress}

Inputs: no named parameters.

searchA

Pinpoint specific code locations — find exact symbols, patterns, or concepts in the indexed codebase. Returns structurally-parsed code chunks (functions, classes) — large definitions may span multiple results.

TYPE — choose one:

  • regex: Match exact patterns against code content. Use for known identifiers, imports, or string literals. Examples: "def authenticate", "class.*Handler", "import.*pandas", "TODO:.*refactor"

  • semantic: Find code by meaning via embedding similarity. Use for concepts or when exact identifiers are unknown. Examples: "authentication logic", "retry with exponential backoff", "database connection pooling"

DECISION GUIDE:

  • Known symbol or pattern → regex

  • Concept or behavior → semantic

OUTPUT: Markdown blocks — file path, line range, symbol name, code block, pagination footer.

Inputs: type (required), query (required), path, offset, +5 more.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

    TDQS

    A4.1/5.0

    Scored across 2 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: daemon_status for health monitoring and search for code retrieval. No overlap in functionality.

    Naming Consistency5/5

    Both tool names use consistent snake_case format: daemon_status and search. Naming pattern is uniform.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a code indexing service. While functional, it lacks typical tools like index management or configuration, placing it below the ideal range.

    Completeness2/5

    The tool surface is severely incomplete for code indexing: missing index control, rescan triggers, configuration, or result filtering. Only basic search and status checks are covered.

    Maintenance

    ActivityActive
    ResponsivenessMixed handling

    Resources

    Related MCP Connectors

    Related MCP Servers

    • agentmako

      drhalto
      A
      license
      C
      quality
      C
      maintenance
      Local-first codebase intelligence engine providing AI coding agents with a typed MCP toolset for understanding and navigating code repositories.
      100
      54
      Apache 2.0

    • Paparats: Local Semantic Code

      IBazylchuk
      A
      license
      A
      quality
      B
      maintenance
      Self-hosted semantic code search for AI coding agents. Indexes many repositories at once with tree-sitter AST chunking, local llama.cpp embeddings and Qdrant, so Claude Code, Cursor, Codex and any MCP client can find code by meaning, trace usages and callers, and read the git history behind each chunk. No API keys, and your code never leaves your machine.
      2
      18
      34
      MIT

    • Atlas

      Brair-Mpagi
      A
      license
      Not graded
      quality
      D
      maintenance
      A local-first codebase intelligence layer for AI coding agents, providing a persistent, queryable model of a repository via an MCP server and CLI to enable structure queries instead of reading many files.
      1
      Apache 2.0

    • weavatrix-js

      Weavatrix
      A
      license
      Not graded
      quality
      B
      maintenance
      Local repository intelligence MCP server that builds a reusable graph of code structure for AI coding agents, providing 34 network-free tools for understanding, searching, and analyzing repositories without data leaving the machine.
      71 npm
      MIT

    Appeared in Searches