# code-context [Health: Active]
**Category:** 🗄️ Databases
**Repository:** https://github.com/infino-ai/code-context
**GitHub Stars:** 27
**Views:** 0
**Installs:** 0
**Upvotes:** 0
**Directory Page:** https://allmcps.com/mcp/code-context
## Description
Local code search for AI agents: hybrid ranked search and SQL aggregation over an in-repo index.
## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):
```json
"mcpServers": {
"code-context": {
"command": "npx",
"args": ["-y","@infino-ai/code-context"]
}
}
```
## Documentation & README
[](https://github.com/infino-ai/code-context/actions/workflows/ci.yml)
[](https://www.npmjs.com/package/@infino-ai/code-context)
[](LICENSE)
[](https://nodejs.org/)
[](https://deepwiki.com/infino-ai/code-context)
**code-context** is the retrieval layer under your coding agent: one local
index over the whole repo (keyword, semantic, hybrid, and SQL), reached
through an MCP server and a CLI, with the index living in plain files inside
your repo. Your agent answers questions about the codebase without reading it
file by file.
The rule of thumb: the more a question spans the repo, the more this saves,
because the answer comes from a ranked index instead of pulling source into
context one file at a time.
**On your own codebase, ~30-40% fewer tokens and ~50% fewer tool calls**
(so answers land faster too - aggregation questions run about 2× quicker).
The harness is in the repo, so you can reproduce it on your own code.
**Try it live (early preview):** ask questions about any public GitHub repo
at [lantern.infino.ai](https://lantern.infino.ai), a demo agent that runs on
code-context.
- 🔎 **Find code by words or meaning.** One ranked pass fuses exact keyword
matching with semantic similarity, and every hit carries the code with
`path:line` citations.
- 📊 **Ask questions grep can't answer.** Search works as a SQL table
function, so "which files have the most code about X" is one query:
ranked by relevance, tallied by `GROUP BY`.
- ⚡ **Searching in seconds, fresh forever.** The keyword index commits
before the embedding model even finishes downloading, vectors backfill in
the background, and edits re-sync incrementally: only changed files
re-chunk and re-embed.
- 🔒 **Nothing leaves your machine.** No accounts, no API keys, no database
server, no telemetry. Embedding is a small local model, downloaded once;
after that everything works offline.
Built on [infino](https://github.com/infino-ai/infino), a fast retrieval
engine that runs SQL, full-text search, and vector search over a single copy
of your data. Text and numeric data is stored as spec-compliant Parquet, and
the same engine handles logs, docs, and agent memory.
Claude Code answering questions about a repo through code-context: index it, then ask, and it reaches for search and SQL on its own.
## Quick start
Install the Claude Code plugin - nothing to paste into a config:
```
/plugin marketplace add infino-ai/code-context
/plugin install code-context@infino-ai
```
It registers code-context's three tools with `alwaysLoad` already set, so the
agent keeps them in view and reaches for the index directly instead of falling
back to plain file search.
Not on Claude Code, or prefer a one-line command? Add it as an MCP server:
```
claude mcp add-json code-context -s user '{"command":"npx","args":["-y","@infino-ai/code-context","mcp"],"alwaysLoad":true}'
```
The `alwaysLoad` flag pins this small tool set so that in a setup with many MCP
servers - where clients defer tool definitions behind a tool-search step - the
agent doesn't miss the index and fall back to plain file search. (Use *either*
the plugin or this command, not both.)
Then just ask a question about the code. The first `search` or `sql` on an
unindexed repo builds the index inline and answers on the same call: keyword
search is live in seconds, and vectors backfill in the background. (Prefer to
kick it off yourself? The `reindex` tool does the same build on demand.)
CI-tested on Linux x64 (glibc) and macOS arm64; linux-arm64, musl, and
Windows-via-WSL are expected to work through the engine's prebuilt bindings
but are not CI-covered.
## Evaluation
Real agent runs over a codebase-Q&A suite (claude-sonnet-4-6, the same
minimal prompt for both lanes), on a repo the model has not memorized -
[infino](https://github.com/infino-ai/infino), the engine this is built on -
because that is the realistic case for your private code. Baseline is stock
file tools including Bash; the code-context lane is the same tools plus the
MCP server. Measured on three axes:
| Category | Tokens | Tool calls | Wall time |
|---|---|---|---|
| Aggregation ("most code about X") | **-43%** | **-71%** | **-48%** |
| Comprehension ("how does X work") | **-29%** | **-27%** | **-13%** |
| Blended | **-32%** | **-53%** | **-32%** |
Aggregation is the structural win - ranked search composed with `GROUP BY`,
which file tools cannot express at any budget - and it roughly halves
end-to-end time. These numbers are on a strong model; weaker, cheaper models
explore less efficiently, so the savings tend to be **larger** there. On
pinpoint symbol lookup, where a single grep is already cheap, an index
matches file tools rather than beating them.
Full methodology and per-question tables are in
[docs/benchmark.md](https://github.com/infino-ai/code-context/blob/HEAD/docs/benchmark.md), with the harness in
[`bench/`](https://github.com/infino-ai/code-context/blob/HEAD/bench/) so you can run the same lanes on your own repo.
## What you get
One index and a deliberately small tool surface for agents:
| Tool | What it does | When agents use it |
|---|---|---|
| `search` | One ranked pass fusing exact keyword matching (BM25) with semantic similarity (reciprocal-rank fusion). Hits carry the chunk content, so answers come straight from results. | A strong default for finding and understanding code: how a subsystem works, code by meaning or exact term, context before a change, similar implementations - exact identifiers and paraphrases in the same call. |
| `sql` | Read-only SQL over the index, with the ranked search functions (`bm25_search`/`hybrid_search`) usable as table-valued relations. | Counts, rankings, aggregates over the whole repo in one query. |
| `reindex` | Incremental sync (the server also auto-syncs in the background). | After significant edits. |
Three tools is a deliberate design: one way to find, one way to count, one
way to stay fresh. Every additional near-duplicate retrieval tool worsens an
agent's tool selection, and hybrid search's keyword half already ranks
exact identifier terms highly, so a separate lexical tool has no job left.
### The SQL move
Search-as-a-table composes with aggregation. Ranked by relevance, tallied by
SQL, one engine pass:
```sql
SELECT path, SUM(end_line - start_line + 1) AS lines, COUNT(*) AS chunks
FROM bm25_search('chunks', 'content', 'vector index quantization', 300)
GROUP BY path ORDER BY lines DESC LIMIT 15
```
`hybrid_search(...)` and `vector_search(...)` work the same way. The CLI and
MCP server embed `{{name}}` placeholders server-side, so agents never handle
raw vectors.
### Staged readiness
`cx index` commits the keyword (BM25) index first. On a ~3,000-chunk repo
that takes under a second, so search works before any embedding model even
exists on the machine. Vectors backfill in the background with a local model
(downloaded once, no key; about two minutes for that same repo), and
hybrid/semantic ranking unlocks automatically when they land. If the vector
stage fails, keyword search stays live and the index says so honestly.
The default model optimizes quality-per-minute. See
[docs/embedder-eval.md](https://github.com/infino-ai/code-context/blob/HEAD/docs/embedder-eval.md) for how it was chosen.
### Your index is just files
Everything lives in `.infino/` in your repo root (added to your
`.gitignore` automatically on first index): plain files you can copy,
cache in CI, or put on object storage. It's a live index the engine queries in place, not a snapshot you
export and pass around.
## Setup for agents
code-context is an MCP server over stdio, so any MCP client works. Register
it once and the tools (`search`, `sql`, `reindex`) become available to the
agent.
Claude Code
**Install as a plugin** - `alwaysLoad` already set, nothing to paste into a
config:
```
/plugin marketplace add infino-ai/code-context
/plugin install code-context@infino-ai
```
**Or register it as an MCP server** directly:
```bash
claude mcp add-json code-context -s user '{"command":"npx","args":["-y","@infino-ai/code-context","mcp"],"alwaysLoad":true}'
```
`alwaysLoad: true` pins code-context's tools into context so the agent reaches
for the index directly. In sessions with many MCP servers Claude Code defers
tool definitions behind a tool-search step; without `alwaysLoad` the agent can
miss code-context and fall back to grep/read. It's a small, always-loaded set
(three tools). Omit it (or use the shorter `claude mcp add code-context -- npx
-y @infino-ai/code-context mcp`) if you'd rather leave the tools deferred.
Use *either* the plugin or the `add-json` command, not both. They register the
same `code-context` server, so running both just collides.
**For a team,** commit a project-scoped `.mcp.json` at the repo root so
everyone gets it (after the one-time project-server approval):
```json
{ "mcpServers": { "code-context": { "command": "npx", "args": ["-y", "@infino-ai/code-context", "mcp"], "alwaysLoad": true } } }
```
Cursor
Add to `.cursor/mcp.json`:
```json
{ "mcpServers": { "code-context": { "command": "npx", "args": ["-y", "@infino-ai/code-context", "mcp"] } } }
```
Codex CLI
In `~/.codex/config.toml` (note the key is `mcp_servers`):
```toml
[mcp_servers.code-context]
command = "npx"
args = ["-y", "@infino-ai/code-context", "mcp"]
```
Gemini CLI
In `~/.gemini/settings.json`:
```json
{ "mcpServers": { "code-context": { "command": "npx", "args": ["-y", "@infino-ai/code-context", "mcp"] } } }
```
Windsurf, Cline, and other MCP clients
Standard stdio MCP config:
```json
{ "mcpServers": { "code-context": { "command": "npx", "args": ["-y", "@infino-ai/code-context", "mcp"] } } }
```
Point the server at a repo explicitly with `env: { "CX_ROOT": "/path/to/repo" }`
when the client's working directory is not the repo.
Tools: `search`, `sql`, `reindex` (incremental sync: an unchanged repo is
a fast no-op, and the server also auto-syncs in the background as queries
arrive, so results track your edits without anyone asking).
**Multiple repos in one session.** Each tool takes an optional `path` (an
absolute repo root). Omit it and the server uses its startup root; set it to
target a specific repo when a session spans more than one. One server
instance serves them all, each with its own index in its own `.infino/` -
no restart, no per-repo config.
## Configuration
| Variable | Default | Purpose |
|---|---|---|
| `CX_INDEX_DIR` | `/.infino` | where the index lives |
| `CX_SEARCH_K` | 10 | default number of hits `search` returns (also settable per call and via the CLI `-k` flag) |
| `CX_MAX_FILES` / `CX_MAX_FILE_BYTES` | 20000 / 1MB | indexing caps (files over the file cap are left out; `search`/`sql` then flag the index as partial so an absence isn't read as proof) |
| `CX_ROOT` | current directory | default repo root for the MCP server / CLI when not run from the repo (each tool call can override it with a `path` argument) |
| `CX_AUTO_INDEX` | on | `0` makes a query on an unindexed repo error instead of building the index inline on the first `search`/`sql` |
| `CX_AUTO_SYNC` | on | `0` disables the MCP server's background staleness sync |
| `CX_SYNC_INTERVAL_SECS` | 30 | auto-sync debounce between staleness checks |
| `CX_NO_EMBED` | off | keyword-only mode for the MCP server (skip the vector stage) |
| `CX_NO_RECEIPT` | off | `1` turns off usage accounting - the per-call receipt on results and the `cx usage` ledger |
Every `search` / `sql` result carries a **usage receipt** - a terse, local line
showing the tokens it returned, the files it spanned, and a running session
total (e.g. `returned ~1.2k tokens | 4 chunks / 3 files | session ~8.4k over 7
queries`). Every figure is a `~` estimate, computed in-process - nothing about
your queries or code leaves the machine.
## CLI
The same index is reachable from the terminal too, for scripting, CI, or
inspecting results yourself. Install the binary, then run any command inside
a repo:
```
npm install -g @infino-ai/code-context
```
```
cx index [path] sync the index (incremental; --full rebuilds, --watch follows edits)
cx search exact terms + meaning, one ranked pass (-k hits)
cx sql read-only SQL; --embed q="text" fills {{q}}
cx status what the index holds, how fresh, vector readiness
cx usage ledger of queries run and what each returned (-n, --all, --clear, --json)
cx mcp serve the MCP tools over stdio
```
`cx usage` reads the local ledger at `.infino/usage.jsonl` - every `search` /
`sql` (from the CLI or the MCP server) appends one line recording the query and
a compact summary of what came back (paths and line ranges for search, row
count for sql), plus the token figures from the receipt. It's a deterministic,
model-independent view of what went through the index - no running server or
agent needed to read it back. `CX_NO_RECEIPT=1` turns off both the inline
receipt and this ledger.
### How often does the agent actually reach for it?
`cx usage` can also show, per session, in how many of your prompts code-context
was used - e.g. `code-context used in 2 of 3 prompts (2 calls)`. The MCP server
can only count its own calls, not your prompts, so this ratio comes from two
Claude Code hooks that keep a local tally (nothing is sent anywhere). Add them
to your Claude Code settings (`~/.claude/settings.json` or a project
`.claude/settings.json`):
```json
{
"hooks": {
"UserPromptSubmit": [
{ "hooks": [{ "type": "command", "command": "cx usage --hook" }] }
],
"PostToolUse": [
{ "matcher": "mcp__code-context.*", "hooks": [{ "type": "command", "command": "cx usage --hook" }] }
]
}
}
```
`cx usage --hook` reads the event on stdin, updates `.infino/prompt-stats.json`,
and prints nothing. If you run code-context via `npx`, use
`npx -y @infino-ai/code-context usage --hook` as the command.
## What it is, and what it isn't
code-context's lane is ranked **content** retrieval and content-relevance
aggregation: find code by words or meaning, rank whole files by how much
they're about a topic, always with `path:line` receipts. It deliberately
does **not** do structural code intelligence (call-graph tracing, dead-code
detection, type resolution). Tools that do are complementary: MCP servers
stack, so run both.
## Architecture
- **Chunking:** tree-sitter (WASM, no native compiles) cuts at definition
boundaries for TypeScript/JS, Python, Rust, Go, Java, C/C++, Ruby, C#, PHP;
Markdown splits at headings; everything else falls back to fixed windows.
Every chunk carries `path, start_line, end_line, lang, content`.
- **Index:** [infino](https://github.com/infino-ai/infino) tables in
`.infino/`: BM25 (FTS) and IVF vector indexes over a single copy of the
data, queried in-process through the Node binding. No server.
- **Embeddings:** always local. A small model (chosen by a
[measured eval](https://github.com/infino-ai/code-context/blob/HEAD/docs/embedder-eval.md)) downloaded once; no key, no
per-query network, code never leaves the machine. Queries embed with the
same model the index was built with, and a mismatch is a clear error, not
silently wrong results.
- **Freshness:** incremental by design. A per-file state map (size/mtime
prefilter, then content hash) means a sync re-chunks and re-embeds only
the files that changed: on a ~3,000-chunk repo an unchanged tree checks
in ~20ms and a one-file edit syncs in ~0.7s with vectors kept current
(larger-repo numbers in the [benchmark](https://github.com/infino-ai/code-context/blob/HEAD/docs/benchmark.md)). The MCP
server auto-syncs in the background as queries arrive (never blocking a
query), `cx index` is incremental by default (`--full` to rebuild), and
`cx index --watch` syncs on file events.
## Learn more
- [Code search for coding agents](https://github.com/infino-ai/code-context/blob/HEAD/docs/concepts/code-search-for-coding-agents.md) - the crawl-vs-retrieve model and when an index saves tokens.
- [FAQ](https://github.com/infino-ai/code-context/blob/HEAD/docs/faq.md) - what it is, when to use it, local-only guarantees, freshness.
- [Tradeoffs](https://github.com/infino-ai/code-context/blob/HEAD/docs/tradeoffs.md) - the honest limits.
- [Benchmark](https://github.com/infino-ai/code-context/blob/HEAD/docs/benchmark.md) - measured results, with a harness to reproduce them on your own repo.
## License
Apache-2.0