@Jayanth-MKV
Basic information
Dump of all the MCP (model context protocol) servers
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MCP-dump": {
"command": "npx",
"args": [
"wrangler",
"dev"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
MCP-dump is a monorepo hosting multiple Model Context Protocol (MCP) server and client implementations. It explores different tooling stacks (Python + uv, TypeScript + Cloudflare Workers) and agent patterns (LLM-backed analysis, reactive agents, tool invocation), intended as a playground and reference for learning, prototyping, and comparing implementation styles.
To use the Cloudflare Workers MCP server, navigate to mcp-hello, install dependencies with npm install, run tests with npm test, and optionally start with npx wrangler dev. For the Python MCP workspace (mcp-files), use uv run mcp-server to start the server, or run client examples like uv run examples/clients/react_agent.py. The Postgres AI agent (ai-agent-postgres-mcp) can be started with uv run main.py after reviewing its local README for database setup. The uv tool is required for Python projects.
MCP standardizes how tools, agents, and large language models exchange contextual information, resources, and tool invocation results. It aims to provide predictable, typed exchanges and reduce ad‑hoc prompt engineering glue.
Cloudflare Workers examples require Node.js and npm (or pnpm/yarn). Python projects use `uv` (fast package manager) and are organized with pyproject.toml. The `uv` tool must be installed separately (see docs.astral.sh/uv).
The monorepo contains subdirectories: `mcp-hello` (Cloudflare Workers server), `mcp-files` (Python workspace with a server, clients, and tools), `ai-agent-postgres-mcp` (AI agent for Postgres), and experimental examples. Each project has its own README.
No. The disclaimer states these implementations are experimental, not guaranteed production‑grade, and may simplify security, robustness, and performance for clarity.
Contributions are welcome, including new MCP tool modules, additional runtime adapters (Rust, Go, etc.), agent strategy examples, and documentation improvements. You should fork, branch from `main`, implement, add minimal docs/tests, and open a PR with a clear description.
A search tool helps dev to solve the naming things problem.
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A curated list of references for MLOps
Model Context Protocol Servers
🚀 The fast, Pythonic way to build MCP servers and clients.
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