Hypertune MCP by usefulapi
Evaluate and inspect your Hypertune feature flags from your AI assistant.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"hypertune": {
"url": "https://hypertune.usefulapi.io/mcp"
}
}
}Remote endpoints
https://hypertune.usefulapi.io/mcpstreamable-httpWhat it can do
Tool inventory
Tools (5)
🟢hypertune_evaluate(selection, context)
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/attributes). Every field must supply all of its arguments (Hypertune reduces the flag logic to a JSON result). Use hypertune_list_flags / hypertune_introspect first if you don't know the flag names.
Input Schema
{
"type": "object",
"properties": {
"selection": {
"type": "string",
"description": "GraphQL selection of flag fields under root, e.g. `exampleFlag` or `homePage { showBanner } exampleFlag`."
},
"context": {
"description": "Evaluation context object, inlined as the `context` arg. e.g. {\"environment\":\"production\",\"user\":{\"id\":\"u_123\",\"email\":\"[email protected]\"}}. Omit to return the flag LOGIC instead of a reduced value.",
"type": "object",
"propertyNames": {
"type": "string"
},
"additionalProperties": {}
}
},
"required": [
"selection"
],
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢hypertune_get_logic(selection, context)
Fetch the underlying LOGIC of flags without reducing them — i.e. `query { root { <selection> } }` with no context. Returns the flag's conditional expression / rules rather than a single resolved value, useful for inspecting how a flag is configured. If the schema requires a context arg you can still pass a partial one via `context`.
Input Schema
{
"type": "object",
"properties": {
"selection": {
"type": "string",
"description": "GraphQL selection of flag fields under root, e.g. `exampleFlag`."
},
"context": {
"description": "Optional partial context, inlined as the `context` arg if the schema requires it.",
"type": "object",
"propertyNames": {
"type": "string"
},
"additionalProperties": {}
}
},
"required": [
"selection"
],
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢hypertune_query(query, variables)
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't express. The query root field is `root(context: ...)`.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "A complete GraphQL query string, e.g. `query { root(context: {environment: \"production\"}) { myFlag } }`."
},
"variables": {
"description": "Optional GraphQL variables object matching the query's declared variables.",
"type": "object",
"propertyNames": {
"type": "string"
},
"additionalProperties": {}
}
},
"required": [
"query"
],
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢hypertune_introspect
Run a GraphQL introspection query to discover the project's schema — all types and fields, including the available flags and the shape of the evaluation context. Best-effort: some edge deployments disable introspection, in which case this returns a clear error (use hypertune_query with a known flag name instead).
Input Schema
{
"type": "object",
"properties": {},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢hypertune_list_flags
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.
Input Schema
{
"type": "object",
"properties": {},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}Community
Evidence