MCP server

Hypertune MCP by usefulapi

io.usefulapi/hypertune

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Evaluate and inspect your Hypertune feature flags from your AI assistant.

Website Source

At a glance

Type

MCP server

Publisher

usefulapi.io (verified by the MCP Registry)

Status

Working at last check

Tools

5

Token cost

About 1,060 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http hypertune https://hypertune.usefulapi.io/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.hypertune]
url = "https://hypertune.usefulapi.io/mcp"

Gemini CLI

{"mcpServers":{"hypertune":{"httpUrl":"https://hypertune.usefulapi.io/mcp"}}}

Cursor

{"mcpServers":{"hypertune":{"url":"https://hypertune.usefulapi.io/mcp"}}}

VS Code

{"servers":{"hypertune":{"type":"http","url":"https://hypertune.usefulapi.io/mcp"}}}

Antigravity

agy mcp add hypertune https://hypertune.usefulapi.io/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"hypertune","server_url":"https://hypertune.usefulapi.io/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"hypertune","server_url":"https://hypertune.usefulapi.io/mcp"}

Tools

• hypertune_evaluate ~325 tokens
  Evaluate one or more feature flags for a given context and get their resolved values. Builds `query { root(context: <context>) { <selection> } }`. Provide `selection` = the flag fields to read (e.g. `exampleFlag anotherFlag { nestedField }`) and `context` = the evaluation context (environment + user
• hypertune_get_logic ~231 tokens
  Fetch the underlying LOGIC of flags without reducing them — i.e. `query { root { <selection> } }` with no context. Returns the flag's conditional expression / rules rather than a single resolved value, useful for inspecting how a flag is configured. If the schema requires a context arg you can still
• hypertune_introspect ~134 tokens
  Run a GraphQL introspection query to discover the project's schema — all types and fields, including the available flags and the shape of the evaluation context. Best-effort: some edge deployments disable introspection, in which case this returns a clear error (use hypertune_query with a known flag
• hypertune_list_flags ~125 tokens
  List the project's top-level feature flags (the fields of the query root type) with their return types and descriptions, derived from GraphQL introspection. A convenient first call to see what flags exist before evaluating. Requires introspection to be enabled on the edge endpoint.
• hypertune_query ~245 tokens
  Run an arbitrary GraphQL query (and optional variables) against the Hypertune edge endpoint. Power-user escape hatch for full control — each Hypertune project has its own generated schema, so this handles any flag shape, nested selections, inline enum literals, or variables the convenience tools can

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