lsai-xmp4.public

The 30-second pitch

Your AI coding agent is burning tokens grepping OSS libraries it will barely use. xmp4 is a hosted MCP server that pre-indexes 856 popular open-source libraries with SCIP — the semantic code format Sourcegraph uses — and serves them through 17 tools. No clone. No grep. No false positives.

ASK: "Who calls Flask.wsgi_app in the flask repo and what does it do?"

with grep + local clone:
  git clone flask/flask          ~40 MB,   ~2 min
  grep -rn "wsgi_app" .          200+ matches, mostly noise
  cat src/flask/app.py | sed ... read 1000+ lines to find the body
  filter false positives         model spends tokens deciding what's real
  ──────────────────────────────────────
  total:                         ~15,000 tokens + disk + wall time

with xmp4:
  xmp4_info(symbol_name="Flask",    file_path="src/flask/app.py")         → signature,  20 tok
  xmp4_source(symbol_name="wsgi_app", file_path="src/flask/app.py")       → body,      180 tok
  xmp4_callers(symbol_name="wsgi_app", file_path="src/flask/app.py")      → 1 caller,   50 tok
  ──────────────────────────────────────
  total:                                                                    ~250 tokens

xmp4 is 60× cheaper here — and every result is SCIP-resolved, not text-matched.

The measured numbers (4 big OSS libs · reproducible)

Same realistic question on spring-boot · tokio · django · efcore: *"give me the signature, body, and real callers of X."*

> GitMCP and Context7 look cheaper per call because they return less. To reach the same answer, GitMCP balloons to 42× more tokens — and still can't produce the semantic caller list. Context7 can't at any cost. **Full whitepaper with Python harness →**

Connect in 30 seconds

// Claude Code / Cursor / Claude Desktop — project `.mcp.json` or client config
{
  "mcpServers": {
    "xmp4": {
      "type": "http",
      "url": "https://mcp.example4.ai/mcp"
    }
  }
}

(The ready-to-paste config also lives at `.mcp.json` in this repo.)

Teach Claude how to use xmp4 (optional but recommended)

Install the xmp4 skill once per version — Claude will pick the cheapest tool path automatically (tests_for + view over grep):

# Claude Code
mkdir -p ~/.claude/skills/xmp4 && \
  curl -sfL https://example4.ai/xmp4-skill.md -o ~/.claude/skills/xmp4/SKILL.md

# Other clients: just tell Claude to read the URL when using xmp4 tools
#   https://example4.ai/xmp4-skill.md

Restart your client. Then try (every step verified live 2026-04-24):

> *"Using xmp4, find the Flask class in `flask/Flask` and list its usages."*

You should see `Type Flask src/flask/app.py:81` and 165 usages across 33 result pages. One semantic call per question. Zero grep loops.

Setup for Cursor · Claude Desktop · Continue · Windsurf → `docs/connect-instructions.md`

The 17 tools

**Full reference with live examples**

Semantic core (where the value lives)

`xmp4_projects` · `xmp4_search` · `xmp4_info` · `xmp4_usages` · `xmp4_callers` · `xmp4_callees` · `xmp4_hierarchy` · `xmp4_outline` · `xmp4_source` · `xmp4_tests_for` · `xmp4_deps` · `xmp4_symbol_at`

Convenience

`xmp4_view` (raw file excerpt by line range) · `xmp4_grep` (server-side regex when semantics isn't enough)

Meta

`xmp4_guide` (returns a versioned skill pointer to `https://example4.ai/xmp4-skill.md` — fetch once per version and save as a local Claude Code skill; embeds a minimal cheatsheet as offline fallback) · `xmp4_server` (version + stats)

Language coverage

Tier 1 — full coverage contract

`C#` · `TypeScript` · `Python` · `Java` · `Rust` · `PHP`

Tier 2 — best-effort, documented quirks

`Go` · `JavaScript` · `Dart` · `Ruby` · `C++`

Every known limitation — empty `hierarchy.base` on TS/Rust/Java/PHP, Python cross-module `usages` under-count, C# explicit-interface-impl dotted-name behaviour — is listed verbatim in `docs/tiers-and-quirks.md`. We'd rather set expectations correctly than have a reviewer find a gap and assume the whole thing is inflated.

Coverage grows by demand, not by guesswork

The index currently holds 856 repositories / 15 921 SCIP-indexed projects. We add new libraries based on two signals, combined:

1. Aggregate query logs — symbol names and project filters, no PII, no user code. If many AI agents search for a library we don't have, we see it.

2. Your request — file a repo-request issue with the GitHub URL, the language, and one concrete query you want to run. A single user request + downstream query demand almost always means indexed within days.

A public `/stats/top-missing` endpoint is planned — full transparency on what drives the growth loop.

Privacy — short version

• ✓ We log: aggregate query counts (symbol/project/tool names, coarse timestamps) to grow the index by demand.
• ✗ We don't log: the contents of your codebase · personal identifiers · request bodies beyond declared tool parameters.
• Standard nginx access logs kept 7 days for abuse prevention, then purged. Not joined with query tallies.

Full detail → `docs/privacy.md`.

Status

• 🟢 Live — `mcp.example4.ai` v1.2.1 · 856 repos · 15 921 projects · 17 tools · 11 languages
• 🟢 Benchmark published — reproducible whitepaper with Python harness
• 🟢 Listed on the Official MCP Registry as `ai.example4/xmp4` (DNS-authed on `example4.ai`)
• 🟢 Smithery — smithery.ai/servers/0ics-srl/xmp4
• 🟢 Cursor Directory — cursor.directory/plugins/lsai-xmp4public
• 🔧 More registry submissions in flight — MCP.so, mcpservers.org, partnerships@github.com, awesome-lists (PRs open on punkpeye/jaw9c/appcypher)
• 🔧 Demand-driven growth loop — in progress

What's in this repository

Related

• **LSAI protocol** — open spec for semantic code intelligence in AI agents.
• **SCIP** — the semantic code format xmp4 is built on (Sourcegraph-developed, BSD-3).
• **Model Context Protocol** — the open transport spec xmp4 speaks.

License

Apache 2.0 for this documentation repository — see LICENSE.

The hosted `mcp.example4.ai` endpoint is free for personal and commercial use (TOS link pending).

Commercial licensing or self-hosted deployment enquiries → open a GitHub issue labelled `commercial` on this repo.