Fronting a large API catalogue behind discover-inspect-call keeps the context cost flat no matter how many operations exist, and the inspect step is what stops the model guessing parameters on a call that bills. Responses carrying billing metadata alongside the data is the detail worth noticing — an agent can see what a call cost in the same turn it reads the result.
A hosted MCP server that fronts a catalogue of public-data APIs for creator research, social listening, competitor monitoring and KOL pricing. Rather than shipping one tool per data source, it exposes three: search the catalogue, inspect an operation, call it.
- `list_operations` — search the public-data operation catalogue for what is available
- `get_operation` — the parameters, schemas and call examples for one operation, so the model constructs the call correctly before spending anything
- `call_api` — runs a concrete operation and returns the data plus billing metadata
- Coverage aimed at X/Twitter search and creator lookup, TikTok, YouTube, Instagram, Reddit and LinkedIn-style public data, influencer discovery, and competitor monitoring across posts, comments, profiles and trends
Nothing installed locally — it is a remote server at `https://mcp.unifapi.com` over Streamable HTTP, added with `claude mcp add --transport http unifapi https://mcp.unifapi.com` or by pasting the URL. OAuth is the recommended path and compatible clients discover it from the protected-resource metadata at `/.well-known/oauth-protected-resource`; clients that cannot complete OAuth send a UnifAPI API key as a bearer token instead. Registry name `com.unifapi/mcp`, 0.1.2 in server.json.
One command plus a key — claude mcp add --transport http unifapi https://mcp.unifapi.com, then supply credentials