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xmp4
Stop grepping library source.
Your AI gets the compiler's view of 856 OSS libraries via MCP.
     
Real callers. Real source. Real hierarchy. In 3 tool calls.
→ Landing page · → Benchmark whitepaper · → Connect in 30 seconds
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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."
| | xmp4 | grep + clone | GitMCP | Context7 | |---|---:|---:|---:|---:| | Total tokens (same question) | 1 558 | 2 978 | 65 629 | — | | vs xmp4 | 1× | 1.9× more | 42× more | can't answer | | Returns real source body? | ✅ | ✅ noisy | ✗ file paths only | ✗ curated docs only | | Semantic callers? | ✅ | ✗ | ✗ | ✗ | | Type hierarchy? | ✅ | ✗ | ✗ | ✗ | | Setup cost | 0 | GBs of clone | 0 | 0 |
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.mdRestart your client. Then try (every step verified live 2026-04-24):
"Using xmp4, find the Flask class in
flask/Flaskand 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 <br>C# · TypeScript · Python · Java · Rust · PHP
Tier 2 — best-effort, documented quirks <br>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:
- 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.
- 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.aiv1.2.1 · 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 onexample4.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
| Path | Purpose | |---|---| | docs/connect-instructions.md | 5 MCP clients, proof-of-life sequence, troubleshooting | | docs/tool-reference.md | 17 tools with live-verified examples and workflow rules | | docs/tiers-and-quirks.md | Language tier matrix + every known limitation, verbatim | | docs/privacy.md | What we log, what we don't, GDPR contact | | docs/request-repo.md | How the demand-driven queue actually works | | .mcp.json | Ready-to-paste MCP client config (type: http, URL) | | skills/xmp4/SKILL.md | Claude Code skill — workflow, cost budget, grep policy, common mistakes | | html/xmp4-skill.md | Same skill, served publicly at https://example4.ai/xmp4-skill.md | | server.json | Official MCP Registry manifest (DNS-authed ai.example4/xmp4) | | glama.json | Glama catalog auto-index hook | | .github/ISSUE_TEMPLATE/ | Bug · feature-request · request-repo templates |
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.
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Made with semantic intelligence instead of grep.
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