ContextSliver

A lightweight, always-live code-context MCP server for AI coding agents — with per-session deduplication so token cost goes down over a conversation.

The problem

When you ask an AI coding agent (Claude Code, Cursor, Copilot) to "fix the bug in AuthService," it repeatedly reads entire files to find the 5% that's relevant — burning 40,000–80,000 tokens on a question that needed ~3,000.

find . -name "*.ts"   → 2,000 tokens (a file listing)
cat AuthService.ts    → 3,000 tokens (whole file)
grep -r "AuthService" → 5,000 tokens (40 matches)
cat AuthMiddleware.ts → 2,500 tokens (whole file, again)
... × 10 more ...

What ContextSliver does

ContextSliver runs as a background MCP server on your machine. It indexes your codebase into a local SQLite graph of every function, class, and import. When the agent needs context, it calls an MCP tool instead of reading files:

Agent:   "What connects to AuthService? Budget: 2,000 tokens."

ContextSliver:
  symbol:        AuthService  (src/auth/AuthService.ts)
  callers:       [AuthMiddleware, LoginController]      ← who uses it
  dependencies:  [UserRepository, TokenService]         ← what it uses
  already_in_context: [UserRepository]                  ← skipped, agent already has it
  // ~380 tokens

Three things that make it different

1. Always-live, not a snapshot — a file watcher updates the SQLite index on every save. There's no "rebuild the graph" step and no stale artifact between commits; queries run against your code's current state.

2. Per-session deduplication — the session ledger tracks exactly what the agent has already received this conversation and skips it on later calls (listing it in already_in_context). Token cost decreases over a session — nobody else does this.

3. Zero-Python, pure Node — one command (npx contextsliver init), no Python/uv runtime, no API keys, no separate database server. Built for TypeScript/JavaScript shops that don't want a Python dependency in their toolchain.

How it compares

ContextSliver is a focused, early-stage tool. It is not a feature-for-feature replacement for more mature alternatives — and that's intentional. Here's an honest breakdown:

When to pick ContextSliver:

• You work in TypeScript/JavaScript and don't want a Python dependency

• You want a live index that updates as you edit, not a snapshot you rebuild

• You care about per-session token deduplication during long agent conversations

• You want a focused, hackable tool (small codebase, easy to contribute a language plugin)

When to pick something else:

• Graphify — if you want the most capable option today (36 languages, semantic extraction, docs/media indexing, PR tooling, visualizations). It's the more complete product.

• Aider — if you want a battle-tested agent with a built-in repo map, and you're happy in Python.

• Repomix — for a one-shot "explain my whole repo to an LLM" task.

ContextSliver's niche is being lightweight, live, and session-aware — not competing on breadth.

Quickstart

# In your project root:
npx contextsliver init      # creates .sliver/, .mcp.json, CLAUDE.md, AGENTS.md, copilot-instructions.md, indexes the repo
npx contextsliver start     # runs the MCP server + file watcher (stdio)

Then restart Claude Code / Cursor / Cline / Copilot — they'll pick up the tools via the generated config and instruction files. See the templates for client-specific config.

The five MCP tools

Pass the session_id from your first cs_get_context call to every subsequent call to enable deduplication.

Supported languages

• TypeScript / JavaScript / TSX (v0.1)

• Python (v0.1)

• Markdown — headings are indexed as searchable symbols and markdown links to code become graph edges. So cs_search_symbols finds docs (not just code) and cs_blast_radius on a function surfaces its documentation (README.md, docs/, CLAUDE.md, AGENTS.md, .github/copilot-instructions.md, …).

• Go, Rust, Java — planned (see roadmap)

Adding a language = add a grammar package + a grammars/<lang>/tags.scm query + a fixture, or implement the extract hook for formats that don't need a tree-sitter grammar. See CONTRIBUTING.md.

How it works

Your codebase  ──chokidar──▶  Parser (Tree-sitter)  ──▶  SQLite graph (.sliver/index.db)
                                                                    │
                              MCP server (stdio) ◀──────────────────┘
                                    │   session ledger (.sliver/index.db)
                                    ▼
                    Claude Code / Cursor / Cline / Copilot

• Parser: Tree-sitter extracts symbols + imports per file.

• Graph engine: stores symbol→symbol edges; bidirectional BFS (blastRadius) for blast radius with cycle detection.

• Session manager: per-session ledger computes deltas so already-sent context is skipped.

• MCP server: exposes the five tools over stdio.

• File watcher: debounced incremental re-index on every save (hash-based skip of unchanged files).

Token counting

Counts use gpt-tokenizer (cl100k_base) and are labeled ~approximate — close enough for budget guidance, not billing.

Development

npm install
npm test            # unit + integration tests
npm run test:bench  # indexing benchmarks
npm run build       # tsc → dist/
npm run lint        # eslint

Requires Node ≥ 20.

Roadmap

• v0.1 ✅ TS/JS + Python, SQLite graph, session ledger, 5 tools, CLI, watcher

• v0.2 — incremental indexing polish, Cursor integration, CI benchmarks

• v0.3 — Go + Rust, monorepo workspace resolution, language-plugin docs

• v0.4 — PreToolUse hook, Java, published token-reduction benchmarks

• v0.5 — Streamable HTTP transport, DuckDB backend for 50k-file repos, PageRank ranking

• v1.0 — frozen API, optional native (napi-rs) engine, SCIP/LSP precision backend

See contextsliver-spec.md for the full specification and DEVELOPMENT_LOG.md for the development retrospective.

License

MIT © DevMuneeb