About Post For Me
Post For Me is a TypeScript MCP server for accessing the Post For Me API through an agent. It uses a docs-search tool and a sandboxed code tool that runs TypeScript SDK code without web or filesystem access.
Key features
- 01Search API and SDK documentation
- 02Run TypeScript SDK code in a sandbox
- 03Support API-key authentication
- 04Use over Streamable HTTP
Use cases
- 01Look up API or SDK usage
- 02Run repeatable social-media API operations
MCP support
Exposes documentation search and sandboxed code execution as MCP tools for agents. It can run locally or remotely over Streamable HTTP, with API-key authentication.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
2Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| code | string | Yes | Code to execute. |
| intent | string | No | Task you are trying to perform. Used for improving the service. |
Details
From the project READMEPost For Me TypeScript MCP Server
It is generated with Stainless.
Installation
Direct invocation
You can run the MCP Server directly via npx:
export POST_FOR_ME_API_KEY="My API Key"
npx -y post-for-me-mcp@latest
Via MCP Client
There is a partial list of existing clients at modelcontextprotocol.io. If you already have a client, consult their documentation to install the MCP server.
For clients with a configuration JSON, it might look something like this:
{
"mcpServers": {
"post_for_me_api": {
"command": "npx",
"args": ["-y", "post-for-me-mcp"],
"env": {
"POST_FOR_ME_API_KEY": "My API Key"
}
}
}
}
Cursor
If you use Cursor, you can install the MCP server by using the button below. You will need to set your environment variables
in Cursor's mcp.json, which can be found in Cursor Settings > Tools & MCP > New MCP Server.
VS Code
If you use MCP, you can install the MCP server by clicking the link below. You will need to set your environment variables
in VS Code's mcp.json, which can be found via Command Palette > MCP: Open User Configuration.
Claude Code
If you use Claude Code, you can install the MCP server by running the command below in your terminal. You will need to set your
environment variables in Claude Code's .claude.json, which can be found in your home directory.
claude mcp add post_for_me_mcp_api --header "x-post-for-me-api-key: My API Key" --transport http https://post-for-me.stlmcp.com
Code Mode
This MCP server is built on the "Code Mode" tool scheme. In this MCP Server, your agent will write code against the TypeScript SDK, which will then be executed in an isolated sandbox. To accomplish this, the server will expose two tools to your agent:
-
The first tool is a docs search tool, which can be used to generically query for documentation about your API/SDK.
-
The second tool is a code tool, where the agent can write code against the TypeScript SDK. The code will be executed in a sandbox environment without web or filesystem access. Then, anything the code returns or prints will be returned to the agent as the result of the tool call.
Using this scheme, agents are capable of performing very complex tasks deterministically and repeatably.
Running remotely
Launching the client with --transport=http launches the server as a remote server using Streamable HTTP transport. The --port setting can choose the port it will run on, and the --socket setting allows it to run on a Unix socket.
Authorization can be provided via the Authorization header using the Bearer scheme.
Additionally, authorization can be provided via the following headers:
| Header | Equivalent client option | Security scheme |
|---|---|---|
x-post-for-me-api-key | apiKey | bearer |
A configuration JSON for this server might look like this, assuming the server is hosted at http://localhost:3000:
{
"mcpServers": {
"post_for_me_api": {
"url": "http://localhost:3000",
"headers": {
"Authorization": "Bearer <auth value>"
}
}
}
}
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.