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codesynapse
Code graph as MCP tools makes your AI stops hallucinating
codesynapse indexes your codebase into a real code graph — callers, callees, class hierarchies, trait implementations — then exposes it as 32 MCP tools. Instead of grepping files, your AI traces actual call chains. Hybrid BM25 + dense embeddings (local, no API key) finds the right code even when the function name has nothing to do with the query. Works with Claude Code, Cursor, Windsurf, Kiro. Rust binary, ~10MB. Fully local — no data leaves your machine.
I built this after losing hours to the same frustrating loop: ask my AI assistant an architecture question, watch it grep files, get a confident but wrong answer.
The breaking point was a real bug. I asked "what handles auth token expiry in this app?" — it found the wrong file, explained the wrong flow, and I shipped a fix to the wrong place.
The problem isn't the model. It's that LLMs navigate code lexically — they see files, not structure. No concept of who calls who, which class inherits what, or how a request actually flows.
My first attempt was a simple symbol index. Better, but still missed connections. Then I added a call graph. Then I realized the search was broken for semantic queries — "what builds the 404 payload" would never match `core_exception_handler` lexically.
That's when I added dense embeddings trained specifically on code (potion-code-16M, 16M params, runs CPU-only). Combined with BM25 via RRF fusion, it finally worked.
32 MCP tools later — context lookup, blast radius, hierarchy traversal, cycle detection, shortest path — it's the tool I actually wanted when I started.
Happy to answer anything about the graph extraction, the embedding approach, or why I built it in Rust.
About codesynapse on Product Hunt
“Code graph as MCP tools makes your AI stops hallucinating”
codesynapse was submitted on Product Hunt and earned 5 upvotes and 1 comments, placing #89 on the daily leaderboard. codesynapse indexes your codebase into a real code graph — callers, callees, class hierarchies, trait implementations — then exposes it as 32 MCP tools. Instead of grepping files, your AI traces actual call chains. Hybrid BM25 + dense embeddings (local, no API key) finds the right code even when the function name has nothing to do with the query. Works with Claude Code, Cursor, Windsurf, Kiro. Rust binary, ~10MB. Fully local — no data leaves your machine.
On the analytics side, codesynapse competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how codesynapse performed against the three products that launched closest to it on the same day.
Who hunted codesynapse?
codesynapse was hunted by Sohil Ladhani. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of codesynapse including community comment highlights and product details, visit the product overview.
Sohil Ladhani