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aarontrmartin

cindex

cindex

Local, offline semantic code search: tree-sitter AST chunks → local GGUF embeddings (Qwen3-Embedding-0.6B via llama-server) → SQLite → brute-force vector search → CLI + MCP tool for coding agents. No network calls, no API keys. The index is content-addressed, so unchanged code is never re-embedded — reformatting a repo or moving files/functions costs zero inference.

What gets indexed: Python, C++, JavaScript (AST chunks: functions, class skeletons with bodies stripped, merged top-level blocks), Markdown (heading sections), plain text (100-line windows). Other file types are currently skipped (see roadmap).

Setup

macOS

git clone <this-repo> && cd code-indexer
uv sync                        # install python deps from the lockfile
brew install llama.cpp         # provides llama-server

Linux

git clone <this-repo> && cd code-indexer
curl -LsSf https://astral.sh/uv/install.sh | sh   # if uv is not installed
uv sync

# llama.cpp: use a prebuilt release from https://github.com/ggml-org/llama.cpp/releases
# or build from source:
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j
sudo cp build/bin/llama-server /usr/local/bin/    # or add build/bin to PATH
cd ..

Model weights (both platforms, one time, ~630 MB, gitignored)

uv tool install "huggingface_hub[cli]"
hf download Qwen/Qwen3-Embedding-0.6B-GGUF Qwen3-Embedding-0.6B-Q8_0.gguf --local-dir models/

Related MCP server: CodeGrok MCP

Starting it

Terminal 1 — the inference server. Leave it running; Ctrl-C stops it.

uv run cindex serve

Always start llama-server through cindex serve: it pins flags the client depends on (--pooling last -ub 4096 -b 4096; without -ub the server crashes on long inputs — details in docs/llama-server-pinned.md).

Terminal 2 — build the index, then query.

uv run cindex init                 # create the database (one time)
uv run cindex index                # embed the repo; re-run any time, only changes cost inference
uv run cindex query "where do we decide if a file needs re-embedding" -k 5

Every command fails fast with the fix in the message (server down, no index yet, model/config mismatch), so if something's wrong the output says what to run.

Using it

uv run cindex query "natural language question"        # top 8 by meaning
uv run cindex query "..." -k 3                         # fewer results
uv run cindex query "..." --path src/                  # restrict to a subtree
uv run cindex query "..." --no-instruct                # raw query embedding (A/B)
uv run cindex index                                    # refresh after editing code

Results are score file:start-end [chunk-type] symbol plus the live snippet read from disk. Stale results self-heal: if a file changed since indexing, the hit is re-indexed inline and correct line numbers are returned.

Multiple indexes (one config = one root = one database)

The default config.toml indexes this repo. To index any other tree, copy documents.toml's pattern — set root (relative to the config file) and a distinct db — and pass --config:

uv run cindex --config documents.toml index                      # workspace-wide index
uv run cindex --config documents.toml query "..." --path 6106    # scope to one project

Querying the default config only searches this repo; if you expected results from a sibling project, you queried the wrong index — add --config.

Agents (MCP)

Opening this repo in Claude Code auto-registers the search_code tool via the committed .mcp.json (approve the server when prompted). AGENTS.md instructs agents to prefer it over grep for meaning-based lookup. Requirements: cindex serve running; the MCP server creates/updates the index itself at session start and repairs stale files lazily at query time.

To give agents in ANY directory a workspace-wide index, register at user scope:

claude mcp add cindex --scope user -- uv --directory /abs/path/to/code-indexer run cindex --config documents.toml mcp

Agents can pass path_prefix to search_code to stay inside one subproject.

Upcoming improvements

  • More languages — TypeScript, Go, Rust, Java: each is one tree-sitter wheel + one walker extension entry + one chunker LangSpec.

  • Catch-all indexing — unknown text file types as blob windows so nothing is invisible, just coarser.

  • Chunker versioning — stamp chunker_version in meta and force re-chunk on upgrade (today an unchanged file keeps its old chunking).

  • Finer prose chunking — paragraph windows for .txt instead of 100-line blobs.

  • ANN search (sqlite-vec) once brute-force matmul exceeds ~50 ms (~10⁶ chunks).

  • int8 quantization — the encoding column is already reserved for it.

  • Watcher daemon — filesystem events instead of explicit cindex index.

  • Hybrid ranking — path/symbol signal fused at rank time (never into content vectors), plus optional reranker.

  • Bench harnesses — speed + recall regression tracking with tagged baselines.

  • GPU offload flags — config-only change when needed.

Layout

src/cindex/        config db walker hasher chunker embedder indexer search resolver cli mcp_server
docs/              llama-server-pinned.md — the pinned inference server contract
config.toml        default index (this repo) · documents.toml — example second index
.mcp.json          wires Claude Code to `cindex mcp` · AGENTS.md — usage instruction for agents

Available Tools

1 tool
search_codeA

Semantic search over this repo's code and docs. Finds code by meaning (e.g. "where do we refuse a mismatched database"), not just exact text — use it before falling back to grep. Returns JSON: file_path, start/end lines, symbol, score, snippet. Optional path_prefix restricts results to a subtree (e.g. "src/").

ParametersJSON Schema
NameRequiredDescriptionDefault
kNo
queryYes
path_prefixNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior4/5

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

No annotations provided, so the description carries the burden. It discloses that the tool returns JSON with specific fields (file_path, start/end lines, symbol, score, snippet) and supports optional path_prefix. No destructive behaviors implied; it is a read-only search, which is clear from context.

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?

Three sentences: first states purpose, second explains how it differs from grep, third describes output and optional features. No wasted words, front-loaded with main action.

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?

For a search tool, the description covers the essential: what it searches, how it differs from alternatives, what it returns, and optional parameters. Even though the output schema is mentioned as present in context, the description itself adequately describes the return format. No major gaps.

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 the description must compensate. It explains the query parameter (the search term), path_prefix with an example ('src/'), but does not mention the k parameter (integer, default 8). The return fields described are not parameters, so partial coverage.

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 performs semantic search over code and docs, distinguishes itself from exact text search (grep), and provides a concrete example query. The verb 'search' and resource 'code and docs' are specific.

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?

Explicitly advises use before falling back to grep, establishing a clear ordering between tools. Also explains the path_prefix parameter to restrict results to a subtree, guiding when to use that.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedsearch_code

TDQS

A4.6/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity between tools. The tool is clearly and uniquely defined.

Naming Consistency5/5

The single tool name 'search_code' follows a clear verb_noun pattern (snake_case), which is consistent within the server.

Tool Count3/5

One tool is on the lower end of appropriateness. While the server's purpose (semantic code search) is narrow, a single tool may feel thin for a typical MCP server; however, it is not extreme.

Completeness4/5

The single tool covers semantic search over code and docs, which appears to be the server's complete purpose. Minor gaps like index management are absent, but not necessarily required based on the description.

Maintenance

ActivitySlowing
ResponsivenessSyncing

Resources

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