Airframe MCP Client
OfficialEnables AI agents to discover practitioners with specific product expertise, such as Figma users, via Airframe's expert network.
Enables AI agents to search, retrieve details, and compare HubSpot with other products using Airframe's product intelligence.
Enables AI agents to search, retrieve details, and compare Salesforce with other products using Airframe's product intelligence.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Airframe MCP ClientCompare Salesforce vs HubSpot"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Airframe MCP client
@airframeai/mcp-client connects a desktop assistant to Airframe when the
assistant launches tools as local programs using the Model Context Protocol (MCP).
The assistant starts this Node.js
process and talks to it through standard input/output (stdio); the process sends
requests to the remote Airframe server over HTTP using an API key.
The remote server supplies product information, research, and expert tools. This client translates the connection; it does not contain that intelligence or perform OAuth sign-in. An assistant with native remote HTTP/OAuth support can connect directly without this bridge.
The server lives in the separate
Airframe repository.
The bridge forwards tools/list and tools/call; the connected server determines
available tools, permissions, quotas, and results. A copied tool list here would
drift from the server.
Start here
Customer setup: stdio-host configuration, credentials, and troubleshooting.
Architecture: source map, protocol behavior, tests, and build/release paths.
src/index.ts: CLI parser, URL checks, bridge, and executable entrypoint.
package.json: package name, binary, engine declaration, dependencies, and scripts.
The guide and source links open the repository. The npm package includes the built client and this README; the separate guides and TypeScript source are not packaged alongside it.
Related MCP server: Offer Discovery MCP Server
Configuration example
For a host that accepts a mcpServers stdio configuration:
{
"mcpServers": {
"airframe": {
"command": "npx",
"args": ["-y", "@airframeai/mcp-client", "--api-key", "YOUR_API_KEY_HERE"]
}
}
}Replace the placeholder with a key for the target environment and keep the local
configuration private. The default URL in the source is
https://mcp.airframe.ai/mcp. API-key availability depends on server configuration;
an OAuth-only endpoint rejects this bridge's authentication method. Native remote
HTTP/OAuth connections do not require this bridge.
CLI contract
Argument | Checked-in behavior |
| Required nonempty value; a missing |
| Optional override; accepts HTTP/HTTPS syntax subject to the hostname checks in |
| Prints help and exits without starting the bridge; no API key is needed. |
The CLI does not read an API-key environment variable. Supply the supported argument through the host configuration. See the architecture guide for the URL check's scope, JSON/SSE handling, and error behavior.
Development
From the repository root, using the checked-in lockfile:
npm ci
npm test
npm run build
npm run bundleThe package declares Node >=18.0.0; the release workflow selects Node 24.
npm test runs Vitest once with mocked transport/fetch paths; it does not prove
live server access. build emits dist/index.js and adds a shebang. bundle
separately creates dist/airframe-mcp-client.js; it is not part of the release
workflow. No fixed bundle size or published asset availability is implied.
A documentation-only edit needs link, Markdown, and source/manifest validation; it does not need dependency installation, an authenticated connection, publishing, or a release tag. License: MIT.
This server cannot be deployed
Maintenance
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