Drop the nim grammar and refresh language counts

Removes the heaviest vendored grammar (66 MB) along with its language
wiring and test fixtures; the supported-language count moves to 158
across README, site, and manifest. The grammar manifest additionally
records the canonical-source decisions for the five
registry-disagreement grammars and the per-directory license files
restored earlier this week.

docs/index.html

@@ -4,7 +4,7 @@
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <title>codebase-memory-mcp — Code Intelligence Knowledge Graph for AI Coding Agents</title>
-    <meta name="description" content="codebase-memory-mcp is an open-source MCP server that indexes any codebase into a persistent knowledge graph so AI coding agents answer structural questions with ~120x fewer tokens. 159 languages, Hybrid LSP type resolution, local semantic vector search, code-clone detection, sub-1ms queries, Linux kernel indexed in 3 minutes. Single static C binary, zero dependencies. Works with 11 agents including Claude Code, Codex CLI, Gemini CLI, Cursor, and Zed.">
+    <meta name="description" content="codebase-memory-mcp is an open-source MCP server that indexes any codebase into a persistent knowledge graph so AI coding agents answer structural questions with ~120x fewer tokens. 158 languages, Hybrid LSP type resolution, local semantic vector search, code-clone detection, sub-1ms queries, Linux kernel indexed in 3 minutes. Single static C binary, zero dependencies. Works with 11 agents including Claude Code, Codex CLI, Gemini CLI, Cursor, and Zed.">
     <meta name="keywords" content="MCP server, code intelligence, knowledge graph, tree-sitter, Hybrid LSP, semantic code search, code embeddings, code clone detection, cross-repo analysis, data-flow analysis, Claude Code, Codex CLI, Gemini CLI, Cursor, Zed, code exploration, token reduction, call graph, dead code detection">
     <meta name="author" content="DeusData">
     <meta name="robots" content="index, follow, max-image-preview:large, max-snippet:-1">
@@ -12,7 +12,7 @@
 
     <!-- Open Graph -->
     <meta property="og:title" content="codebase-memory-mcp — Code Intelligence Knowledge Graph for AI Coding Agents">
-    <meta property="og:description" content="Index any codebase into a persistent knowledge graph. AI agents answer structural questions with ~120x fewer tokens. 159 languages, Hybrid LSP, sub-1ms queries, single static C binary.">
+    <meta property="og:description" content="Index any codebase into a persistent knowledge graph. AI agents answer structural questions with ~120x fewer tokens. 158 languages, Hybrid LSP, sub-1ms queries, single static C binary.">
     <meta property="og:type" content="website">
     <meta property="og:url" content="https://deusdata.github.io/codebase-memory-mcp/">
     <meta property="og:image" content="https://deusdata.github.io/codebase-memory-mcp/graph-ui-screenshot.png">
@@ -24,7 +24,7 @@
     <!-- Twitter Card -->
     <meta name="twitter:card" content="summary_large_image">
     <meta name="twitter:title" content="codebase-memory-mcp — Code Intelligence for AI Coding Agents">
-    <meta name="twitter:description" content="Index any codebase into a knowledge graph. ~120x fewer tokens for AI code exploration. 159 languages, Hybrid LSP, sub-1ms queries, single static C binary.">
+    <meta name="twitter:description" content="Index any codebase into a knowledge graph. ~120x fewer tokens for AI code exploration. 158 languages, Hybrid LSP, sub-1ms queries, single static C binary.">
     <meta name="twitter:image" content="https://deusdata.github.io/codebase-memory-mcp/graph-ui-screenshot.png">
 
     <link rel="canonical" href="https://deusdata.github.io/codebase-memory-mcp/">
@@ -40,8 +40,8 @@
       "applicationCategory": "DeveloperApplication",
       "applicationSubCategory": "Model Context Protocol (MCP) server",
       "operatingSystem": "macOS, Linux, Windows",
-      "softwareVersion": "0.7.0",
-      "description": "An open-source MCP server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links, so AI coding agents answer structural questions with roughly 120x fewer tokens than file-by-file search. Parses 159 languages via tree-sitter with Hybrid LSP semantic type resolution for 9 language families. Ships as a single static C binary with zero runtime dependencies.",
+      "softwareVersion": "0.8.1",
+      "description": "An open-source MCP server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links, so AI coding agents answer structural questions with roughly 120x fewer tokens than file-by-file search. Parses 158 languages via tree-sitter with Hybrid LSP semantic type resolution for 9 language families. Ships as a single static C binary with zero runtime dependencies.",
       "url": "https://deusdata.github.io/codebase-memory-mcp/",
       "downloadUrl": "https://github.com/DeusData/codebase-memory-mcp/releases/latest",
       "codeRepository": "https://github.com/DeusData/codebase-memory-mcp",
@@ -54,7 +54,7 @@
         "priceCurrency": "USD"
       },
       "featureList": [
-        "Indexes 159 programming languages via vendored tree-sitter grammars",
+        "Indexes 158 programming languages via vendored tree-sitter grammars",
         "Hybrid LSP semantic type resolution for Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust",
         "14 MCP tools for structural search, call-path tracing, and Cypher graph queries",
         "Semantic vector code search via bundled nomic-embed-code embeddings (no API key, fully local)",
@@ -125,7 +125,7 @@
           "name": "What is codebase-memory-mcp?",
           "acceptedAnswer": {
             "@type": "Answer",
-            "text": "codebase-memory-mcp is an open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query that graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens. It parses 159 languages and ships as a single static C binary with no runtime dependencies."
+            "text": "codebase-memory-mcp is an open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query that graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens. It parses 158 languages and ships as a single static C binary with no runtime dependencies."
           }
         },
         {
@@ -141,7 +141,7 @@
           "name": "Which programming languages does codebase-memory-mcp support?",
           "acceptedAnswer": {
             "@type": "Answer",
-            "text": "It supports 159 languages through vendored tree-sitter grammars compiled into the binary, including Python, Go, JavaScript, TypeScript, Rust, Java, C, C++, C#, PHP, Ruby, Kotlin, Swift, and many more. Nine language families — Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust — additionally get Hybrid LSP semantic type resolution."
+            "text": "It supports 158 languages through vendored tree-sitter grammars compiled into the binary, including Python, Go, JavaScript, TypeScript, Rust, Java, C, C++, C#, PHP, Ruby, Kotlin, Swift, and many more. Nine language families — Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust — additionally get Hybrid LSP semantic type resolution."
           }
         },
         {
@@ -444,11 +444,11 @@
             The fastest, most efficient code intelligence engine for AI coding agents. It indexes any
             repository into a persistent knowledge graph — full-indexing an average repo in seconds and the
             Linux kernel in 3 minutes — so your agent answers structural questions with ~120x fewer tokens.
-            Tree-sitter parsing across 159 languages, Hybrid LSP type resolution, single static C binary.
+            Tree-sitter parsing across 158 languages, Hybrid LSP type resolution, single static C binary.
         </p>
         <div>
             <div class="stat"><div class="number">~120x</div><div class="label">fewer tokens</div></div>
-            <div class="stat"><div class="number">159</div><div class="label">languages</div></div>
+            <div class="stat"><div class="number">158</div><div class="label">languages</div></div>
             <div class="stat"><div class="number">3 min</div><div class="label">Linux kernel index</div></div>
             <div class="stat"><div class="number">11</div><div class="label">agents supported</div></div>
         </div>
@@ -483,7 +483,7 @@
             Protocol (MCP)</a> server that indexes a codebase into a persistent knowledge graph of functions,
             classes, call chains, HTTP routes, and cross-service links. Instead of reading files one at a time,
             an AI coding agent queries the graph — answering structural questions with roughly 120x fewer
-            tokens. It parses 159 languages and ships as a single static C binary with zero runtime dependencies.
+            tokens. It parses 158 languages and ships as a single static C binary with zero runtime dependencies.
         </p>
         <p class="muted">
             It is a structural-analysis backend, not a chatbot: there is no embedded LLM and no API key. Your
@@ -566,7 +566,7 @@
             </tbody>
         </table>
         <p class="muted">
-            The two-layer pipeline runs a fast syntactic tree-sitter pass for every one of the 159 languages,
+            The two-layer pipeline runs a fast syntactic tree-sitter pass for every one of the 158 languages,
             then a type-aware Hybrid LSP pass on top for the families above. Languages without a Hybrid LSP pass
             yet fall back to textual resolution, so you always get an answer.
         </p>
@@ -659,7 +659,7 @@
         <h2>Features</h2>
         <div class="features">
             <div class="feature">
-                <h3>159 languages</h3>
+                <h3>158 languages</h3>
                 <p>Python, Go, JS, TS, TSX, Rust, Java, C++, C#, C, PHP, Ruby, Kotlin, Scala, Zig, Elixir, Haskell, OCaml, Swift, Dart, Lean 4, and many more via vendored tree-sitter grammars compiled into the binary.</p>
             </div>
             <div class="feature">

docs/llms.txt

@@ -1,11 +1,11 @@
 # codebase-memory-mcp
 
-> An open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query the graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens (~3,400 vs ~412,000 across five structural queries). Parses 159 languages via vendored tree-sitter grammars, with Hybrid LSP semantic type resolution for Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust. It also generates semantic graph edges (SEMANTICALLY_RELATED for vocabulary-mismatch matches, SIMILAR_TO for near-clone detection) and supports semantic vector search via bundled nomic-embed-code embeddings compiled into the binary — no API key, no Ollama, no Docker. Ships as a single static C binary (zero runtime dependencies) for macOS, Linux, and Windows. All processing is local — the embeddings run on-device; there is no embedded LLM and no API key.
+> An open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query the graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens (~3,400 vs ~412,000 across five structural queries). Parses 158 languages via vendored tree-sitter grammars, with Hybrid LSP semantic type resolution for Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust. It also generates semantic graph edges (SEMANTICALLY_RELATED for vocabulary-mismatch matches, SIMILAR_TO for near-clone detection) and supports semantic vector search via bundled nomic-embed-code embeddings compiled into the binary — no API key, no Ollama, no Docker. Ships as a single static C binary (zero runtime dependencies) for macOS, Linux, and Windows. All processing is local — the embeddings run on-device; there is no embedded LLM and no API key.
 
 ## Key facts
 
 - License: MIT, open source.
-- Languages: 159 (159 vendored tree-sitter grammars compiled into the binary).
+- Languages: 158 (158 vendored tree-sitter grammars compiled into the binary).
 - Hybrid LSP type resolution: 9 language families (Python, TypeScript/JavaScript/JSX/TSX, PHP, C#, Go, C/C++, Java, Kotlin, Rust) — a lightweight C implementation of language type-resolution algorithms, structurally inspired by and compatible with major language servers including tsserver, pyright, gopls, Roslyn, Eclipse JDT, and rust-analyzer.
 - MCP tools: 14 (search_graph incl. semantic_query vector search, trace_path (alias: trace_call_path), query_graph (Cypher), detect_changes, get_architecture, get_code_snippet, manage_adr, and more).
 - Semantic search: natural-language code discovery via bundled nomic-embed-code embeddings (768-dim, compiled into the binary); 11-signal combined scoring; fully local, no API key.