mcp-ts-core and mcp-server-python-template

mcp-ts-core
65
Established
Maintenance 13/25
Adoption 10/25
Maturity 25/25
Community 17/25
Maintenance 0/25
Adoption 6/25
Maturity 16/25
Community 18/25
Stars: 119
Forks: 20
Downloads:
Commits (30d): 0
Language: TypeScript
License: Apache-2.0
Stars: 15
Forks: 13
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No risk flags
Stale 6m No Package No Dependents

About mcp-ts-core

cyanheads/mcp-ts-core

TypeScript template for building Model Context Protocol (MCP) servers. Ships with declarative tools/resources, pluggable auth, multi-backend storage, OpenTelemetry observability, and first-class support for both local and edge (Cloudflare Workers) runtimes.

This is a framework for developers to quickly build and deploy specialized AI agent servers that perform specific tasks. It takes declarative definitions of 'tools' and 'resources' (like searching a database or greeting a user) and produces a ready-to-use server, handling common backend complexities like authentication, storage, and logging. Developers who need to create custom, task-specific AI agents without building server infrastructure from scratch would use this.

AI-agent-development backend-development developer-tools cloud-native-applications server-side-logic

About mcp-server-python-template

sontallive/mcp-server-python-template

This template provides a streamlined foundation for building Model Context Protocol (MCP) servers in Python. It's designed to make AI-assisted development of MCP tools easier and more efficient.

This is a foundational template for software developers creating applications that allow AI models to interact with external tools and data. It provides a structured Python project that takes in an AI model's request and allows the developer to easily define functions that the AI can call, producing responses or actions from external resources. This is intended for backend developers building AI-powered services.

AI-powered services backend development API integration developer tools tooling for LLMs

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