code-context-control 2.63.2

Local MCP code-intelligence for AI coding tools: surgical search/read/edit, agent-config version history, path-level access + masking guards, and a multi-project hub.

Code Context Control

The local code-intelligence layer for AI coding tools.
Retrieve less, read less, edit safer — and control what an agent may read, write, or see.
Works with Claude Code, Codex, Copilot, Cursor, and Antigravity.

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The problem

LLM-driven coding tools have one expensive failure mode: they read too much. They cat whole files, regex the entire repo, dump 10k-line logs into context, edit-and-pray, and burn through budget before they touch a single line of code. On a half-day session you can spend $20+ on token waste that adds zero value.

And they read too widely: the same agent that greps your source also greps your .env, your customer CSV, and your production dumps.

What C3 does about it

A thin local layer between your IDE and your repo. Every AI tool call is routed through a narrow, surgical operation instead of a broad, wasteful one:

Without C3	With C3
Read the whole 2,000-line file	c3_compress returns a structural map at 40-70% of the original token count → c3_read(symbols=...) for the exact function
Grep the whole repo blindly	c3_search returns ranked candidates with TF-IDF + symbol awareness
Dump full pytest output into the prompt	c3_filter distills 500 lines → 30 actionable ones
Edit, hope it compiled	c3_edit writes via a ledger + c3_validate runs pyright/tsc automatically
Bash test runs that hang on Windows	c3_shell returns structured {exit_code, stdout, stderr, duration} with auto-filter
Lose all context on /clear	c3_session(snapshot) + c3_memory persist decisions across sessions
Re-explain the project every session	Auto-synced CLAUDE.md / AGENTS.md / copilot-instructions.md from one source of truth
Agent reads .env, a customer CSV, a prod dump	Access Guard denies the path outright, or Mask Guard serves a redacted view

Everything runs locally. No source code, prompts, or model output leaves your machine unless you explicitly opt into a third-party model API.

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Install

Requires Python 3.10+. Recommended via pipx:

pipx install code-context-control
c3 init /path/to/your/project

Or pip install "code-context-control[tui]" for the optional Textual UI.

c3 init walks you through IDE selection (Claude Code, Codex CLI, VS Code, Cursor, Antigravity, or Custom), optional git init, MCP registration, and — for Claude Code — a permission tier. Headless:

c3 init /path/to/project --force --ide claude --mcp-mode direct --permissions standard
c3 init /path/to/huge-repo --force --no-embed     # skip the embedding index on large repos

Upgrade with c3 upgrade (or pipx upgrade code-context-control). MCP is wired through the c3-mcp entry point, so upgrading needs no per-project reconfiguration.

Upgrading from before v2.60.1 on Windows: existing projects are not repaired by upgrading — re-run c3 init once per project to fix hook registration.

Full notes: Upgrading · Contributing

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The UI

Three local web apps, no Electron — pure Flask + vanilla JS: the Hub (c3 hub, port 3330), the per-project UI (c3 ui), and the optional Oracle (c3 oracle serve).

Project Hub — multi-project mission control

Every C3-initialized project registers itself here on c3 init. Filter by active/idle, see IDE / version / port / last activity per row, and launch your IDE or the project UI in one click. Ctrl+K searches code and memory across every registered project.

Three top-level views — Projects, Tasks (a cross-project kanban), and Credentials — plus a drill-in panel per project covering Overview, Sub-projects, Tasks, Artifacts, Memory, Ledger, Sessions, Health, Budget, Credentials, Config, and MCP.

Sub-projects make a nested repo a first-class child with its own .c3: the parent's index excludes the child's subtree, and c3_search / c3_memory fan out on demand (scope='all', or one child by name). → Sub-projects guide

Per-project UI

c3 ui                             # binds the first free port from 3333

Twelve tabs. The dashboard above shows token savings, indexed files, the live session, and a stream of recent tool calls and file changes.

Tab	What it's for
Dashboard	Token savings, codebase breakdown, live session counters, recent activity
Chat	Browse and search indexed IDE chat transcripts
Sessions	Every session with duration, decisions, files, tool calls, token cost
Memory	Durable facts across sessions — categories, semantic search, list ↔ graph
Tasks	Per-project PM: dependencies, milestones, time tracking, health
Edits	The Edit Ledger — every AI-driven change, versioned and restorable
Bitbucket	PRs, branches, activity, admin against Bitbucket Data Center
Jira	My Work board, JQL search, transitions, comments
Credentials	Named secrets — metadata only, values are never returned to the browser
Access Guard	Path rules: deny / read-only / mask, plus the path tester
Instructions	One editor for CLAUDE.md, AGENTS.md, copilot-instructions.md
Settings	Budgets, feature flags, background agents, delegate routing, MCP

Tasks (v2.45.0, extended v2.53.0) is a durable per-project tracker — dependencies and subtasks, milestones, decision notes, full event history, health reports, and both automatic and manual time tracking. It rolls up into the Hub's cross-project board.

Instructions keeps your agent-facing docs in sync. C3-generated content sits inside a <!-- C3:BEGIN … --> block; anything you write outside it is preserved. Since v2.60.0 generated docs point at .c3/MAP.md — a machine-owned, byte-stable repo map C3 refreshes automatically — instead of embedding a tree that goes stale. AGENTS.md serves both Codex and Antigravity; GEMINI.md is read if present but no longer generated (the Gemini CLI profile was removed in v2.52).

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The MCP tool suite

C3 exposes 20 tools as a native MCP server. Your IDE calls them directly:

Tool	What it does
c3_search	TF-IDF / regex / semantic search, ranked; fans out to sub-projects with scope=
c3_compress	AST-based file map (map, dense_map, smart, diff, bug_scan, ast)
c3_read	Surgical reads — by symbol name, regex, or line ranges
c3_edit	Atomic patch with ledger logging + content-addressable history
c3_validate	Type / syntax check (pyright, tsc, ruff — auto-detected)
c3_filter	Distill long terminal/log output via pattern + LLM summarization
c3_shell	Shell commands with structured returns + auto-filtered stdout
c3_status	Views: budget, health, notifications, sessions, ghost_files, access
c3_memory	Fact store with categories, recall, graph queries, index→fetch two-step
c3_session	Snapshot, restore, log decisions, compact history
c3_impact	Blast-radius analysis before editing shared symbols
c3_delegate	Offload heavy work to local Ollama / Codex / Gemini
c3_agent	Workflows: review_changes, investigate, preflight, prepare_context, validate_compress
c3_edits	Edit-ledger queries, version diffs, restore points, per-branch filter
c3_task	Per-project PM — tasks, dependencies, milestones, time tracking (v2.53.0)
c3_artifacts	Agent-config version history, diff & restore (v2.46.0)
c3_credentials	Named-secret vault; values never enter model context (v2.58.0)
c3_bitbucket	Bitbucket Data Center — PRs, branches, builds, admin (v2.30.0)
c3_jira	Jira Cloud + Data Center — JQL, issues, transitions (v2.56.0)
c3_project	Cross-project discovery & operations; guarded writes (v2.31.0)

Every tool is read-only safe in plan mode except c3_edit, c3_shell, c3_artifacts(action='restore'), c3_delegate with write delegation enabled, and write actions on c3_bitbucket / c3_jira / c3_credentials / c3_project / c3_task.

On Windows c3_shell uses Git Bash when available. Git Bash bundles no jq; use python -m json.tool for portable JSON formatting.

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Guards — what the agent may touch, and what it sees

Two questions, two answers. Access Guard (v2.62.0) answers may the agent touch this path? Mask Guard (v2.63.0) answers what should it see when it does?

c3 access add "secrets/**" --kind deny          # never read, never write
c3 access add "migrations/**" --kind read_only  # readable, never written
c3 access mask add "data/*.csv" --preset sample_rows --params "count=20,strategy=first"
c3 access mask activate                         # purge pre-mask artifacts, build views

- AWS_KEY = "AKIAIOSFODNN7EXAMPLE"          # your file
+ AWS_KEY = "«c3:redacted:aws_access_key»"  # what the agent sees

• Tighten-only. No allow list exists; global and project scopes merge as a union, so a cloned repo's config can only add protection.
• deny means deny-enumerate too — denied paths never appear in search results, maps, or the vector index.
• Masked means read-only, always. Cropped rows have no inverse, so an edit in transformed coordinates would corrupt the one file you protected.
• Four deterministic presets, no LLM in the read path: redact_secrets, redact_columns (salted one-way pseudonyms — joins survive, no reverse dictionary), sample_rows, signatures_only.
• Rule changes are human-only (UI tab or CLI) and ledger-logged. Agents have no mutation surface.
• Honest coverage. This guards cooperative agents against mistakes and prompt injection. It is not a sandbox — a raw shell outside C3's tools still sees the real bytes.

→ Access Guard · Mask Guard

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Integrations

	What you get	Guide
Credential vault (v2.58.0)	Named secrets, global + per-project, in the OS keyring. Agents use them by name — env_creds='NPM_TOKEN' or {{cred:NAME}} — and values are decoded only at the subprocess boundary, never in model context. Hub-wide Credentials view since v2.59.0.	guide
Bitbucket DC/Server (v2.30.0)	PRs, branches, builds, repo admin over REST + PAT. Merges and branch deletes land in the edit ledger.	guide
Jira (v2.56.0)	Cloud (REST v3) and Data Center (REST v2) behind one tool: raw JQL, My Work board, transitions, comments, and an Activity view linking ledger work to issue keys.	guide
Oracle Discovery API (v2.32.0)	Expose cross-project code + memory intelligence as tools for an external LLM, over MCP (:3332/mcp) or OpenAPI REST (:3331/api/discovery). Read + safe-action tools only, Bearer token in the keyring.	guide

All tokens live in the OS keyring (Windows Credential Manager, macOS Keychain, Linux Secret Service) — never in .c3/config.json. Each supports login --global so one login is reusable across every C3 project.

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Tiered local AI (optional)

Optional Ollama integration so the primary model doesn't spend context on grunt work:

Tier	Model class	Used for	Latency target
Nano	qwen2:0.5b	Intent routing, classification	<100 ms
Micro	deepseek-r1:1.5b	Last-turn Q&A, summarization	<1 s
Base	llama3.2:3b+	Code analysis, technical reasoning	<5 s

c3_delegate(task="summarize this 4k-line stacktrace", backend="ollama")
c3_delegate(task="rate-limit refactor", backend="auto")    # picks the right tier

Ollama is fully optional. C3 works without it.

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Permissions (Claude Code)

C3 manages .claude/settings.local.json with three tiers:

Tier	What it allows
read-only	Exploration only — no file writes, no git writes, no installs
standard	Normal dev workflow — edit, build, test, local git (recommended)
permissive	Full trust — everything except destructive ops

c3 permissions show
c3 permissions standard

All tiers allow C3's MCP tools and include a hard deny list (rm -rf, sudo, git push --force). Switching a tier preserves your own allow/deny rules — only C3-managed entries are replaced. The same applies to .mcp.json and your hooks.

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Benchmarks

Every number C3 advertises is reproducible on your own machine, against your own project:

c3 bench session                  # six realistic workflow scenarios, A/B with vs without C3
c3 benchmark /path/to/project     # per-operation micro-benchmarks
c3 bench aider                    # Aider Polyglot suite (external; burns real API tokens)
c3 bench swe                      # SWE-bench Lite (external)

The session benchmark's baseline models a competent agent working without C3 — one targeted search, each file read once — and scores answer quality alongside tokens. Runs against C3's own repository land around ~50% token savings (2×) at quality parity — 51.8% at v2.43.0, 49.9% at v2.63.2, with C3 scoring 98.8% on answer quality against the baseline's 96.5% in both. File sampling is deterministic (largest files first, no RNG), so a given commit reproduces the same figure. Your numbers will differ with your project's shape; that's why the harness ships with the tool.

C3 also records real per-tool usage to .c3/tool_telemetry.jsonl, so estimates can be checked against what actually happened.

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Security & privacy

• All web servers bind 127.0.0.1 by default and are guarded against browser-based attacks even on loopback — a Host-header allowlist (defeats DNS rebinding) plus an Origin/Referer check on every request (defeats cross-origin CSRF), with scoped, non-wildcard CORS. There is still no user authentication, so do not expose these servers to an untrusted network without auth/TLS in front. Binding to a non-loopback interface is opt-in and warned at startup. (Hardening added in v2.33.0.)
• No telemetry by default. The OSS package collects nothing. Opt-in Sentry crash reporting requires the [telemetry] extra plus both SENTRY_DSN and C3_TELEMETRY_OPT_IN=1; even then request bodies, local variables, and prompts are stripped.
• LLM memory distillation is local-first. Cloud distillation (v2.51.0) is off by default and opt-in per project.
• API keys for third-party providers are read from the environment and never persisted by C3.
• Full hardening guide and disclosure policy: SECURITY.md

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Support C3

C3 is free, open source, and built by one person. If it saves you tokens — that's the whole point — consider sponsoring on GitHub. Sponsorship funds API costs for cross-model test runs and dedicated development time.

License

Apache License 2.0 (LICENSE) — free for any use, including commercial. Third-party deps: THIRD_PARTY_LICENSES.md.

The author may introduce a paid offering or relicense future major versions; no commitment either way. Releases already published under Apache-2.0 (including all 2.x versions) keep that grant irrevocably. Background: LICENSING.md.

Links

• PyPI: https://pypi.org/project/code-context-control/
• Changelog: CHANGELOG.md
• Issues: https://github.com/drknowhow/code-context-control/issues
• Sponsor: https://github.com/sponsors/drknowhow