MCP Installation

The kinetk MCP runs over stdio — clients launch it as a subprocess and speak JSON-RPC on its stdin/stdout. You’ll point each client at the built dist/index.js and pass two env vars: the API base URL and your API key.

1. Get the server

$
git clone https://github.com/kinetk/api-docs-knowledge-graph.git
$
cd api-docs-knowledge-graph/mcp-server
$
npm install
$
npm run build
$
cp .env.example .env
$
# fill in GRAPH_SERVICE_URL and GRAPH_SERVICE_API_KEY

After build, the entry point is <repo>/mcp-server/dist/index.js. Note the absolute path — every client config below needs it.

2. Find your node binary

The MCP launches as a subprocess via the node binary. Clients can’t always find it on PATH, so use the absolute path:

$
which node
$
# e.g. /usr/local/bin/node  or  /opt/homebrew/bin/node

Use this path verbatim in every config below.

Claude Code (CLI)

One command:

$
claude mcp add kinetk \
>
  -e GRAPH_SERVICE_URL=https://api.kinetk.ai/graph \
>
  -e GRAPH_SERVICE_API_KEY=<key> \
>
  -- /usr/local/bin/node /absolute/path/to/api-docs-knowledge-graph/mcp-server/dist/index.js

Start a new Claude Code session and run /mcp — kinetk should show as connected with three tools. If anything fails, run claude --debug and /mcp to see the spawn error.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

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{
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  "mcpServers": {
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    "kinetk": {
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      "command": "/usr/local/bin/node",
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      "args": ["/absolute/path/to/api-docs-knowledge-graph/mcp-server/dist/index.js"],
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      "env": {
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        "GRAPH_SERVICE_URL": "https://api.kinetk.ai/graph",
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        "GRAPH_SERVICE_API_KEY": "<key>"
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      }
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    }
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  }
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}

Fully quit and relaunch Claude Desktop. The hammer icon on the prompt bar should show three new tools.

Cursor

Cursor supports MCP via either a project-scoped file (.cursor/mcp.json at the repo root) or a user-scoped one (~/.cursor/mcp.json). Both use the same shape:

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{
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  "mcpServers": {
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    "kinetk": {
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      "command": "/usr/local/bin/node",
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      "args": ["/absolute/path/to/api-docs-knowledge-graph/mcp-server/dist/index.js"],
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      "env": {
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        "GRAPH_SERVICE_URL": "https://api.kinetk.ai/graph",
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        "GRAPH_SERVICE_API_KEY": "<key>"
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      }
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    }
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  }
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}

Restart Cursor (or use Cursor → Settings → MCP → Refresh). The three tools appear in the Composer / Agent panel.

Tip: drop the file at .cursor/mcp.json inside your project repo if you only want the MCP active when working there. Use ~/.cursor/mcp.json to make it global.

Windsurf (Codeium)

Edit ~/.codeium/windsurf/mcp_config.json:

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{
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  "mcpServers": {
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    "kinetk": {
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      "command": "/usr/local/bin/node",
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      "args": ["/absolute/path/to/api-docs-knowledge-graph/mcp-server/dist/index.js"],
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      "env": {
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        "GRAPH_SERVICE_URL": "https://api.kinetk.ai/graph",
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        "GRAPH_SERVICE_API_KEY": "<key>"
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      }
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    }
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  }
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}

Open Windsurf → Settings → Cascade → Plugins / MCP and click Refresh.

Gemini CLI

Edit ~/.gemini/settings.json:

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{
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  "mcpServers": {
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    "kinetk": {
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      "command": "/usr/local/bin/node",
5
      "args": ["/absolute/path/to/api-docs-knowledge-graph/mcp-server/dist/index.js"],
6
      "env": {
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        "GRAPH_SERVICE_URL": "https://api.kinetk.ai/graph",
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        "GRAPH_SERVICE_API_KEY": "<key>"
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      }
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    }
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  }
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}

Start a new gemini session — /mcp list shows registered servers, /tools lists create_context_job, get_context_job_status, get_context_job_result.

Note: only the Gemini CLI (google-gemini/gemini-cli) supports custom MCP servers today. Gemini in the web UI / AI Studio does not expose an install path for local stdio MCPs.

ChatGPT

ChatGPT’s MCP support targets remote (HTTP/SSE) MCP servers via the Connectors interface — not local stdio. This MCP is stdio-only in v1, so you have two options:

1. ChatGPT Desktop with developer mode (where supported): if your ChatGPT Desktop build exposes a local MCP config (some recent builds do — check Settings → Connectors → “Add local server”), point it at /usr/local/bin/node with the same args/env as the Claude Desktop block above.
2. Bridge to remote MCP: wrap this server with a stdio→HTTP shim (e.g. mcp-remote or supergateway) and register the resulting URL as a Connector.

Adding native HTTP/SSE transport is a planned v2 — open a ticket if you need it.

Verify it’s working

Once installed, ask your client something the MCP can answer:

Use kinetk to find trending content about smartwatches in the last 7 days.

The client should call create_context_job with kind: "intelligence_discover", then get_context_job_status until succeeded, then get_context_job_result for the slim envelope. If it answers from general knowledge instead, you didn’t cue the MCP — say Use kinetk explicitly.

Troubleshooting