An MCP server for your data — auto‑generated from your schema
Define a content model. FoxNose generates an MCP server at the API endpoint instantly — five fixed tools, live JSON Schema introspection, same auth as your REST surface. Plug Claude, Cursor, OpenAI Responses, or any MCP-aware agent into your data. No glue code.
From schema to a working agent in three steps
Hand-writing an MCP server means defining tools, wiring HTTP calls, handling auth, and keeping the schema in sync. Knowledge MCP is generated from the schema that already powers your REST API — define once, the MCP tracks it.
Define a schema
In the dashboard or via the Management API, model your content: Collections, fields, vector-enabled text columns. Publish.
The MCP appears
FoxNose exposes /<prefix>/_mcp over Streamable HTTP. Five tools live. Auth inherits from your Flux API key.
Plug in your agent
Drop the URL into Claude Desktop, Cursor, OpenAI Responses, or any MCP client. The agent discovers and uses the tools.
One MCP config that lights up all five tools
No SDK install, no @tool wrappers, no schema hand-writing.
# mcp config — Claude Code, Cursor & other HTTP MCP clients
# (stdio-only clients such as Claude Desktop connect via mcp-proxy)
{
"mcpServers": {
"foxnose-support": {
"type": "http",
"url": "https://<env_key>.fxns.io/support/_mcp",
"headers": {
"Authorization": "Simple <public_key>:<private_key>"
}
}
}
}Five tools, every Knowledge MCP
The catalog is fixed and works across every connected Collection via a resource_id argument. Add a tenth Collection or a hundredth — the catalog stays the same size.
discover_resourcesList the resources available to this agent — Collections, schemas, capabilities.
describe_resourceReturn the live JSON Schema of a Collection. The agent validates calls against the current schema, not a stale copy.
query_recordsList records with structured filters, sort, and pagination.
get_recordFetch a single record by id, with optional locale and population.
search_recordsHybrid search — vector + full-text + filters — fused server-side. One call, ranked results.
What makes this different
Plenty of platforms bolt MCP onto an existing API surface. Four properties make FoxNose specifically built for agents rather than retrofitted for them.
- Same auth as REST
- If a Collection is read-only over REST, it's read-only over MCP. If a Flux key can't see Collection X over REST, it can't see Collection X over MCP. One permission system, not two.
- Schema, not a copy
- The describe_resource tool returns the live JSON Schema. The agent validates its calls against the current shape before issuing them. Hand-written MCP servers maintain a copy that drifts.
- Constant catalog size
- Five tools regardless of how many Collections or tenants you have. Hand-rolled MCP servers inflate one-tool-per-resource and overflow the model's tool budget.
- Hybrid search in one tool
- Vector similarity, full-text matching, and structured filters fused server-side as a single search_records tool. The model doesn't choose between three narrow tools and pick poorly.
Works with MCP‑aware clients
Streamable HTTP 2025-03-26. Authorization header carries through.
| Client | Notes |
|---|---|
| Claude Code | Direct, Streamable HTTP |
| Claude Desktop | Public direct; private via mcp-proxy |
| Claude API | Direct via mcp_servers parameter |
| OpenAI Responses API | Direct via tools: [{type: "mcp"}] |
| Cursor | Direct, HTTP + custom headers |
| Cline · Continue · Codex CLI · Goose · Zed | Direct, most modern MCP-aware clients |
Define a schema.
Get a working MCP.
Explore more
Hybrid Search API
Semantic search, full-text, and pre-filter vector search in one query.
Learn more →Knowledge Base API
Schema-first, auto-generated REST API with built-in search.
Learn more →AI Knowledge Governance
Audit trail, schema versioning, RBAC built into the knowledge base.
Learn more →AI Agent Memory
Persistent knowledge base for AI agents. Read-write API with LangChain integration.
Learn more →