I craft unique cereal names, stories, and ridiculously cute Cereal Baby images.

MCP-Arbeiter-AI
MCP -Server SDK für Cloudflare -Mitarbeiter
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MCP Workers AI
MCP servers sdk for Cloudflare Workers
Usage
Install:
yarn add mcp-workers-ai
# or
npm install -S mcp-workers-ai
Load the MCP server tools:
import { loadTools } from "mcp-workers-ai"
const tools = await loadTools([
import("@modelcontextprotocol/server-gitlab"),
import("@modelcontextprotocol/server-slack"),
...
]);
// Pass `tools` to the LLM inference request.
Call a tool:
import { callTool } from "mcp-workers-ai"
// Typically the LLM selects a tool to use.
const selected_tool = {
arguments: {
project_id: 'svensauleau/test',
branch: 'main',
files: [ ... ],
commit_message: 'added unit tests'
},
name: 'push_files'
};
const res = await callTool(selected_tool)
// Pass `res` back into a LLM inference request.
Demo
wrangler configuration:
name = "test"
main = "src/index.ts"
[ai]
binding = "AI"
[vars]
GITLAB_PERSONAL_ACCESS_TOKEN = "glpat-aaaaaaaaaaaaaaaaaaaa"
[alias]
"@modelcontextprotocol/sdk/server/index.js" = "mcp-workers-ai/sdk/server/index.js"
"@modelcontextprotocol/sdk/server/stdio.js" = "mcp-workers-ai/sdk/server/stdio.js"
Worker:
import { loadTools, callTool } from "mcp-workers-ai"
export default {
async fetch(request: Request, env: any): Promise<Response> {
// Make sure to set the token before importing the tools
process.env.GITLAB_PERSONAL_ACCESS_TOKEN = env.GITLAB_PERSONAL_ACCESS_TOKEN;
const tools = await loadTools([
import("@modelcontextprotocol/server-gitlab/dist/"),
]);
const prompt = await request.text();
const response = await env.AI.run(
"@hf/nousresearch/hermes-2-pro-mistral-7b",
{
messages: [{ role: "user", content: prompt }],
tools,
},
);
if (response.tool_calls && response.tool_calls.length > 0) {
const selected_tool = response.tool_calls[0];
const res = await callTool(selected_tool)
if (res.content.length > 1) {
throw new Error("too many responses")
}
const finalResponse = await env.AI.run(
"@hf/nousresearch/hermes-2-pro-mistral-7b",
{
messages: [
{
role: "user",
content: prompt,
},
{
role: "assistant",
content: "",
tool_call: selected_tool.name,
},
{
role: "tool",
name: selected_tool.name,
content: res.content[0].text,
},
],
tools,
},
);
return new Response(finalResponse.response);
} else {
return new Response(response.response);
}
}
};
Calling the AI:
$ curl http://example.com \
-d "create a file called 'joke.txt' in my svensauleau/test project with your favorite joke on the main branch. Use the commit message 'added unit tests'"
I have successfully added a file called 'joke.txt' with a joke to your project 'svensauleau/test' on the main branch. The commit message used was 'added unit tests'. You can view the commit and the file in your project's repository.
Result:
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Reviews

user_7w1k3zzc
As a devoted user of QueryPie MCP, I am extremely impressed with its seamless server integration and efficient querying capabilities. The user interface is intuitive, making data management straightforward and stress-free. The support and regular updates from querypie are commendable. Highly recommend checking it out here: https://mcp.so/server/querypie-mcp-server/querypie.