BrunoKrugel/echo-mcp
by Various
Enable AI assistants to interact with your Echo API
MCP
BrunoKrugel/echo-mcp
Added 1 June 2026
Overview
A Go-based MCP server that allows AI assistants to interact with an Echo API. It implements the Model Context Protocol to expose Echo API endpoints for AI model consumption.
Best for
Best for
Developers who need a minimal MCP server to experiment with AI assistant integration using an Echo API.
Use cases
- Enabling AI assistants to send test requests to an Echo API for debugging
- Building AI-powered applications that require a simple echoing service
- Prototyping MCP integrations with a minimal, reflective API backend
How to use
Install
npx @modelcontextprotocol/inspector http://localhost:8080/mcp Tools exposed
go
Example client config
{\n "mcpServers": {\n "echo-api": {\n "type": "http",\n "url": "http://localhost:8080/mcp",\n "timeout": 120\n }\n }\n} Notes
A Go-based MCP server that allows AI assistants to interact with an Echo API. It implements the Model Context Protocol to expose Echo API endpoints for AI model consumption.
9 stars on GitHub. Last updated 2026-03-26. Licensed GPL-3.0.
Use cases
- Enabling AI assistants to send test requests to an Echo API for debugging
- Building AI-powered applications that require a simple echoing service
- Prototyping MCP integrations with a minimal, reflective API backend
Pros
- Lightweight and simple, focused on a single purpose
- Written in Go, offering good performance and easy deployment
- Minimal dependencies, straightforward to self-host
Cons
- Very limited functionality (only echo endpoints)
- Low community adoption (9 stars) with minimal documentation or support
- Not suitable for production use beyond basic testing or MCP demos
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Lightweight and simple, focused on a single purpose
- Written in Go, offering good performance and easy deployment
- Minimal dependencies, straightforward to self-host
Cons
- Very limited functionality (only echo endpoints)
- Low community adoption (9 stars) with minimal documentation or support
- Not suitable for production use beyond basic testing or MCP demos
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