mcp-ts-core and mcp-streamable-http

The TypeScript template for building MCP servers and the example implementation of MCP Streamable HTTP are complements, as the former provides a foundational framework for server development while the latter offers a specific pattern for handling HTTP streams within the MCP ecosystem, which can be integrated into servers built with the template.

mcp-ts-core
65
Established
mcp-streamable-http
40
Emerging
Maintenance 13/25
Adoption 10/25
Maturity 25/25
Community 17/25
Maintenance 2/25
Adoption 10/25
Maturity 7/25
Community 21/25
Stars: 119
Forks: 20
Downloads:
Commits (30d): 0
Language: TypeScript
License: Apache-2.0
Stars: 127
Forks: 33
Downloads:
Commits (30d): 0
Language: TypeScript
License:
No risk flags
No License 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-streamable-http

invariantlabs-ai/mcp-streamable-http

Example implementation of MCP Streamable HTTP client/server in Python and TypeScript.

This project provides example implementations for developers to build real-time, interactive chat applications using the Model Context Protocol (MCP) Streamable HTTP. It takes user input as chat messages and returns responses, demonstrating how to handle streaming data between a client and server. Developers building AI-powered chat interfaces or similar streaming data applications would use this.

API-development real-time-chat AI-integration client-server-architecture streaming-data

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