User: Thank you for joing me as operator in this epic MCP Tool Update > right now:<\/p>\n\n\n\n
\/mcp responds<\/p>\n\n\n\n
But no callable tools<\/p>\n\n\n\n
No queryable resources<\/p>\n\n\n\n
No LLM-interoperability layer<\/p>\n\n\n\n
It\u2019s just a JSON-RPC stub. > Consider: ‘\/home\/spectrcyde\/NerfEngine\/assets\/codesurface-master’ > Absolutely \u2014 you\u2019re now at the \u201cturn infrastructure into intelligence plumbing<\/em>\u201d stage, and CodeSurface<\/strong> is an excellent example of how to do exactly that.<\/p>\n\n\n\n Let\u2019s take your MCP \/ hypergraph \/ TAK-ML ecosystem and solidly answer:<\/p>\n\n\n\n \u201cWhat does a proper MCP tool registry<\/em> look like \u2014 one that turns your hypergraph into a real remote execution & orchestration brain<\/strong>?\u201d<\/p>\n<\/blockquote>\n\n\n\n CodeSurface is essentially:<\/p>\n\n\n\n It solves exactly the problems you need:<\/p>\n\n\n\n Define tools cleanly, with parameters and return types Expose them as RPC endpoints Let an LLM drive them with safety, audit logs, and strict schema validation<\/strong><\/p>\n<\/blockquote>\n\n\n\n So it\u2019s highly relevant.<\/p>\n\n\n\n Your MCP bridge is the remote layer connecting:<\/p>\n\n\n\n Instead of an empty toolset, you want a rich, auditable, capability-aware<\/em> registry like:<\/p>\n\n\n\n Each tool has:<\/p>\n\n\n\n This is exactly what CodeSurface does for you \u2014 define these in a declarative registry.<\/p>\n\n\n\n Here\u2019s the transformation path:<\/p>\n\n\n\n Instead of Now you have:<\/p>\n\n\n\n That\u2019s fundamentally different from MCP\u2019s current \u201cempty toolset\u201d.<\/p>\n\n\n\n Instead of falling back to empty handlers:<\/p>\n\n\n\n And execute:<\/p>\n\n\n\n This gives you:<\/p>\n\n\n\n Here\u2019s a concrete set that turns your system from a visualization platform into a live-programmable graph brain:<\/p>\n\n\n\n\n
\n\n\n\n\ud83e\udde0 What CodeSurface<\/em> Is, & Why It Matters<\/h1>\n\n\n\n
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\n\n\n\n\ud83d\udccc What Your MCP Surface Should Look Like<\/h1>\n\n\n\n
LLM <\u2014\u2014 JSON-RPC \u2014\u2014\u2014> Backend tooling<\/code><\/pre>\n\n\n\n{\n \"decay_now\": { \u2026 },\n \"subscribe_scope\": { \u2026 },\n \"unsubscribe_scope\": { \u2026 },\n \"run_tak_ml\": { \u2026 },\n \"scrub_scope_time\": { \u2026 },\n \"ingest_pcap\": { \u2026 },\n \"export_graph_snapshot\": { \u2026 },\n \"query_hot_entities\": { \u2026 }\n}<\/code><\/pre>\n\n\n\n\n
\n\n\n\n\ud83d\udee0 Mapping CodeSurface into MCP<\/h1>\n\n\n\n
Step 1 \u2014 Define the Tool Registry<\/h3>\n\n\n\n
MCPHandler<\/code> having zero tools<\/em>, you define a registry, e.g.:<\/p>\n\n\n\n# mcp_registry.py\n\nfrom codesurface import Tool, Registry\n\nregistry = Registry()\n\nregistry.register(\n Tool(\n name=\"decay_now\",\n description=\"Apply decay pass to the hypergraph\",\n params_schema={\n \"type\": \"object\",\n \"properties\": {\n \"lambda\": {\"type\": \"number\"},\n },\n \"required\": []\n },\n run=hypergraph_control.decay_now\n )\n)\n\nregistry.register(\n Tool(\n name=\"subscribe_scope\",\n description=\"Subscribe to a scope for edge streaming\",\n params_schema={\n \"type\": \"object\",\n \"properties\": {\n \"scope_id\": {\"type\": \"string\"},\n \"min_weight\": {\"type\": \"number\"},\n },\n \"required\": [\"scope_id\"]\n },\n run=hypergraph_control.subscribe_scope\n )\n)<\/code><\/pre>\n\n\n\n\n
\n\n\n\nStep 2 \u2014 Integrate the Registry with MCP<\/h3>\n\n\n\n
class MCPHandler(BaseHandler):\n def _register_tools(self):\n return registry.list_tools()<\/code><\/pre>\n\n\n\ndef handle_invoke(self, tool_name, params):\n return registry.execute(tool_name, params)<\/code><\/pre>\n\n\n\nHTTP POST \/mcp\n{\n \"tool\": \"decay_now\",\n \"params\": { \"lambda\": 0.005 }\n}<\/code><\/pre>\n\n\n\n
\n\n\n\n\ud83d\udccc Tools You Should Expose (Minimum Viable Brain)<\/h1>\n\n\n\n