Step 1
Start hereOpen the repository and choose the SDK or server example relevant to your tool.
Open SourceMCP Servers
Agent tooling referenceModel Context Protocol references
MCP Servers.
Use this as reference material for building AI-agent tools, not as a finished production trading product.
What the README tells you
The README explicitly describes the servers as reference implementations for MCP features and SDK usage.
Features
What this repo gives you.
A quick practical feature map before you open the source code.
Reference implementations for Model Context Protocol servers.
Examples for how AI agents expose and call tools.
Useful for learning server registration and tool design.
Requires security review before connecting private market systems.
Installation
Step-by-Step Setup.
Follow these steps to get MCP Servers running on your machine.
Setup Path
Run it locally, then inspect the system.
Follow the README flow first. Once the app opens locally, use the AI prompts below to trace the data flow and make focused changes.
CloneInstallRunInspect
Step 2
Read the security note before connecting sensitive systems.
Step 3
Run only the example you need.
Step 4
ProductionAdapt the pattern into your own isolated MCP server.
AI-Powered Setup
Set Up MCP Servers With AI.
Pick your AI tool, copy the prompt, and let it handle the entire setup and codebase walkthrough for you.
Google Antigravity
Best when you want the assistant to operate inside the editor and handle setup end-to-end.
Desktop editorGitNode or PythonModel access
Ready-to-use prompt ↓
Open https://github.com/MrChartist/servers. Clone the repository, inspect the README first, then set up the project locally using the exact setup steps from the README. Before editing anything, explain the stack, folder structure, data flow, required environment variables, and the safest first customization for this project: Treat it as a learning reference, then design your own market-data MCP server.
How it works: Paste the prompt into Antigravity after choosing the repo. Ask it to read the README first, run setup commands, keep changes small, and explain every file it touches before editing.
OpenAI Codex
Best when you want command-by-command setup, debugging, code edits, and verification in a local workspace.
GitTerminalNode or PythonLocal workspace
Ready-to-use prompt ↓
I want to work on https://github.com/MrChartist/servers. First read the README and summarize what the project does. Then give me the exact commands for my operating system to clone, install, configure environment variables, and run it locally. After it runs, inspect the codebase and propose one small change that matches this goal: Treat it as a learning reference, then design your own market-data MCP server.
How it works: Paste the prompt into Codex with the repository URL. Ask it to run the project, identify the stack from files instead of guessing, make scoped edits, and verify with build or browser checks.
Claude
Best when you want architecture explanation, codebase understanding, and a clean implementation plan before edits.
Repo accessREADME contextTerminal if using Claude Code
Ready-to-use prompt ↓
Analyze https://github.com/MrChartist/servers from the README and source structure. Create a clear architecture map, explain the main modules, identify setup risks, and recommend a safe implementation plan for extending it. Focus especially on: Ask the AI to explain MCP concepts: tools, resources, prompts, and server registration. Ask it to create a small local tool before connecting market data. Ask it to add safeguards for credentials, logs, and rate limits.
How it works: Paste the prompt with the GitHub link. Ask Claude to read the README, map modules, list risks, and produce a practical plan before using Claude Code or another agent to implement.
AI Focus
What to Ask the AI.
After setup, use these focused questions to get the most value from your AI assistant.
AI Briefing Mode
Ask for maps, not magic.
Use these prompts after the repo runs locally. The goal is to make the AI trace the system before it changes code.
FlowLogicRiskChange
Prompt 1
Ask the AI to explain MCP concepts: tools, resources, prompts, and server registration.
Prompt 2
Ask it to create a small local tool before connecting market data.
Make one focused change
Ask it to add safeguards for credentials, logs, and rate limits.
Best Next Step
Treat it as a learning reference, then design your own market-data MCP server.