mcp-ts-template and boilerplate-mcp-server

Both are TypeScript server boilerplates/templates for the Model Context Protocol (MCP), making them direct competitors in providing a starting point for building MCP servers.

mcp-ts-template
62
Established
boilerplate-mcp-server
54
Established
Maintenance 10/25
Adoption 10/25
Maturity 25/25
Community 17/25
Maintenance 10/25
Adoption 8/25
Maturity 17/25
Community 19/25
Stars: 119
Forks: 20
Downloads:
Commits (30d): 0
Language: TypeScript
License: Apache-2.0
Stars: 69
Forks: 22
Downloads:
Commits (30d): 0
Language: TypeScript
License:
No risk flags
No License

About mcp-ts-template

cyanheads/mcp-ts-template

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 project helps developers build specialized backend servers that integrate with AI agents. You provide descriptions of 'tools' that an agent can use, along with their inputs and outputs. The project then generates a fully functional server, handling all the underlying infrastructure like data storage, authentication, and logging. It's designed for developers creating custom AI agent capabilities, rather than end-users interacting with AI directly.

backend-development AI-agent-integration server-framework developer-tools microservices

About boilerplate-mcp-server

aashari/boilerplate-mcp-server

TypeScript Model Context Protocol (MCP) server boilerplate providing IP lookup tools/resources. Includes CLI support and extensible structure for connecting AI systems (LLMs) to external data sources like ip-api.com. Ideal template for creating new MCP integrations via Node.js.

This project provides a secure, ready-to-use template for connecting AI assistants (like Claude Desktop or Cursor AI) to external data sources. It takes requests from an AI for external information, retrieves that data from an API (like IP geolocation services), and returns the results to the AI in a format it can easily understand and use. This is designed for developers who want to build custom integrations that allow their AI applications to access real-world information.

AI integration development API connection Large Language Model (LLM) tooling External data fetching AI context augmentation

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