Skip to content
Knowledge MCP

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.

1

Define a schema

In the dashboard or via the Management API, model your content: Collections, fields, vector-enabled text columns. Publish.

2

The MCP appears

FoxNose exposes /<prefix>/_mcp over Streamable HTTP. Five tools live. Auth inherits from your Flux API key.

3

Plug in your agent

Drop the URL into Claude Desktop, Cursor, OpenAI Responses, or any MCP client. The agent discovers and uses the tools.

Connect in one config

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>"
      }
    }
  }
}

Connect Claude guide →  ·  Connect ChatGPT & OpenAI →

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_resources

List the resources available to this agent — Collections, schemas, capabilities.

describe_resource

Return the live JSON Schema of a Collection. The agent validates calls against the current schema, not a stale copy.

query_records

List records with structured filters, sort, and pagination.

get_record

Fetch a single record by id, with optional locale and population.

search_records

Hybrid 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.
Client compatibility

Works with MCP‑aware clients

Streamable HTTP 2025-03-26. Authorization header carries through.

ClientNotes
Claude CodeDirect, Streamable HTTP
Claude DesktopPublic direct; private via mcp-proxy
Claude APIDirect via mcp_servers parameter
OpenAI Responses APIDirect via tools: [{type: "mcp"}]
CursorDirect, HTTP + custom headers
Cline · Continue · Codex CLI · Goose · ZedDirect, most modern MCP-aware clients

Full compatibility notes →

Define a schema.
Get a working MCP.