mdwoicke’s gists

mdwoicke / README.md

Created June 2, 2025 02:49 — forked from jherr/README.md

Interacting With An MCP Server In The Terminal

Step 1. Start a server up in the terminal

uvx mcp-server-fetch

Step 2. Initialize it by copy and pasting this JSON-RPC invocation (and hit return)

{"jsonrpc":"2.0","id":0,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{"sampling":{},"roots":{"listChanged":true}},"clientInfo":{"name":"mcp-inspector","version":"0.12.0"}}}

Step 3. Notify the server that it's now initialized

mdwoicke / flight_server.py

Created May 10, 2025 17:20 — forked from janakiramm/flight_server.py

MCP Server for Real-Time Flight Information

	import json
	import os
	import requests
	import pytz
	from datetime import datetime, timedelta
	from typing import Any, Callable, Set, Dict, List, Optional
	from mcp.server.fastmcp import FastMCP

	# Create MCP server
	mcp = FastMCP("Flight Server")

mdwoicke / place-order-payload.txt

Created May 2, 2025 01:43 — forked from gabeodess/place-order-payload.txt

Domino's API

	find: https://order.dominos.com/power/store-locator?s=${line1}&c=${line2}&type=${type}
	method: GET
	s: Street Address
	c: City, State and/or Postal Code
	type: Delivery or Carryout

	store info: https://order.dominos.com/power/store/${storeID}/profile
	method: GET
	storeId: store id number

mdwoicke / eigen-mcp.ts

Created April 11, 2025 19:08 — forked from dabit3/eigen-mcp.ts

EigenLayer MCP Server Example

	import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
	import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
	import { z } from "zod";
	import 'dotenv/config'

	const server = new McpServer({
	name: "EigenLayer AVS service",
	version: "1.0.0",
	});

mdwoicke / contemplative-llms.txt

Created January 13, 2025 14:52 — forked from Maharshi-Pandya/contemplative-llms.txt

"Contemplative reasoning" response style for LLMs like Claude and GPT-4o

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference

mdwoicke / gist:d2b7050036a363d66578472d60b3acbb

Created September 9, 2024 03:26 — forked from KennyVaneetvelde/gist:4f672cea1aae9feae5dafc03f172a546

ChromaDB story ingester

	import os
	import chromadb
	from chromadb.utils import embedding_functions
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	import hashlib
	import logging
	from rich.logging import RichHandler
	from rich.progress import Progress, SpinnerColumn, TextColumn
	from rich.console import Console
	from rich.panel import Panel

mdwoicke / sample.txt

Created August 26, 2024 21:03 — forked from vinodkiran/sample.txt

flowise DocStore public api

	-- routes/documentstore/index.ts

	// document store external routes
	router.post('/load-store/:storeid/:loaderId', documentStoreController.uploadDocumentStoreFile)
	router.post('/check-status/:uploadId', documentStoreController.checkUploadStatus)


	-- constrollers/documentstore/index.ts
	const uploadDocumentStoreFile = async (req: Request, res: Response, next: NextFunction) => {
	try {

mdwoicke / gist:912f430cf49c2335d33a87e85f101e56

Created July 22, 2024 17:13 — forked from mberman84/gist:de81e435d2d10248551106903f8c0ae5

RouteLLM Script

	import os

	os.environ["OPENAI_API_KEY"] = "XXX"
	os.environ["GROQ_API_KEY"] = "YYY"

	from routellm.controller import Controller

	client = Controller(
	routers=["mf"],
	strong_model="gpt-4-1106-preview",

mdwoicke / rag_gemini.py

Created July 20, 2024 14:11 — forked from janakiramm/rag_gemini.py

Python code to implement RAG with Vertex AI Vector Search and Gemini Pro

	# The previous part of this tutorial is at https://gist.github.com/janakiramm/55d2d8ec5d14dd45c7e9127d81cdafcd

	from vertexai.language_models import TextEmbeddingModel
	from google.cloud import aiplatform
	import vertexai
	from vertexai.preview.generative_models import GenerativeModel, Part
	import json
	import os

	project=”YOUR_GCP_PROJECT”

mdwoicke / vector_search.py

Created July 20, 2024 14:10 — forked from janakiramm/vector_search.py

Python Code to create Vertex AI Vector Search Index and Deploying it.

	###Enable Google Cloud APIs and login with your credentials
	#gcloud services enable compute.googleapis.com aiplatform.googleapis.com storage.googleapis.com
	#gcloud auth application-default login

	###Install required Python modules
	#pip install pypdf2
	#pip install google-cloud-storage
	#pip install google-cloud-aiplatform
	#pip install jupyter

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