The Echo MCP Server is a simple utility that allows you to:
Echo Messages: Send messages using the echo_tool and receive the same message back from the server
Test MCP Clients: Ideal for integration testing by simulating server responses
Develop New Servers: Use it as a template for building custom MCP Servers
Configure as Needed: Customize server settings via environment variables like SECRET_KEY
It supports development workflows with formatting, linting, and version management capabilities.
Supports version control and release management through Git tags that follow semantic versioning
Provides integration with GitHub for releasing new versions of the package by pushing Git tags, which trigger automated workflows
Enables automated build and publishing workflows that are triggered when a new Git tag is pushed to release new versions of the package
Facilitates automated package publishing to PyPI when new versions are tagged, supporting Python package distribution
Used for code formatting and linting to maintain code quality in the MCP server implementation
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Echo MCP Server echo back 'Hello, how are you today?'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A simple echo MCP (Model Context Protocol) Server with a simple echo_tool for testing MCP Clients.
It is also great as a template for new MCP Servers.
Install uv and add the server to an MCP config using uvx:
{
"name": "echo-mcp-server-for-testing",
"command": "uvx",
"args": [
"echo-mcp-server-for-testing"
],
"env": {
"SECRET_KEY": "123456789"
}
}or clone the repo and use uv with a directory:
{
"name": "echo-mcp-server-for-testing",
"command": "uv",
"args": [
"--directory",
"path/to/root/dir/",
"run",
"main.py"
],
"env": {
"SECRET_KEY": "123456789"
}
}Related MCP server: @profullstack/mcp-server
Clone the repo and use mcp-client-for-testing to test the tools of the server.
uvx mcp-client-for-testing \
--config '
[
{
"name": "echo-mcp-server-for-testing",
"command": "uv",
"args": [
"--directory",
"path/to/root/dir/",
"run",
"main.py"
],
"env": {
"SECRET_KEY": "123456789"
}
}
]
' \
--tool_call '{"name": "echo_tool", "arguments": {"message": "Hello, world!"}}'The code is formatted and linted with ruff:
uv run ruff format
uv run ruff check --fixBuild the package using uv:
uv buildTo release a new version of the package to PyPI, create and push a new Git tag:
Checkout the main branch and get the current version:
git checkout main
git pull origin main
git describe --tagsCreate and push a new Git tag:
git tag v0.2.0
git push origin v0.2.0The GitHub Actions workflow will automatically build and publish the package to PyPI when a new tag is pushed. The python package version number will be derived directly from the Git tag.
This project is licensed under the MIT License. See the LICENSE file for details.
Echo a message as a tool
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states 'Echo a message as a tool' which reveals nothing about what the tool actually does behaviorally - whether it's a read operation, a transformation, a test utility, or something else. It doesn't mention any side effects, permissions needed, rate limits, or what 'echo' means in practical terms.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words, which is appropriate for a simple tool. There's no wasted language or unnecessary elaboration. However, this conciseness comes at the cost of meaningful content - it's brief but under-specified rather than efficiently informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 1 parameter, no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain what the tool does, how to use it, what the parameter means, or what to expect as output. The description fails to provide the minimal context needed for an agent to understand and use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 1 parameter with 0% description coverage, meaning the schema provides no semantic information about the 'message' parameter. The description doesn't mention parameters at all, offering no compensation for the schema's lack of documentation. The description should explain what kind of message is expected or what the parameter represents, but it remains completely silent on this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Echo a message as a tool' is a tautology that essentially restates the tool name 'echo_tool' without adding meaningful specificity. It uses the verb 'echo' which matches the name, but doesn't explain what 'echo' means in this context or what resource is being acted upon. No sibling tools exist to differentiate from, but the description remains vague about the actual function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool, what scenarios it's designed for, or any prerequisites. With no sibling tools, there's no need to differentiate from alternatives, but the description doesn't indicate any context for usage. It's a minimal statement that offers no practical guidance to an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
echo_toolWith only one tool, there is no possibility of confusion or overlap between tools. The single tool 'echo_tool' has a clear and distinct purpose that cannot be mistaken for any other tool in this set.
The naming is perfectly consistent as there is only one tool following the verb_noun pattern 'echo_tool'. No other naming conventions exist to create inconsistency.
A single tool is too few for most practical server purposes, making the set feel thin and limited. While it might be appropriate for a minimal demo or test server, it lacks the depth expected for typical MCP server functionality.
For a simple echo functionality, the single tool covers the basic operation. However, there are no related operations (e.g., variations like echo_reverse or echo_log) that might be expected in a more complete echo service, leaving minor gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A Model Context Protocol (MCP) server for Selise Blocks Cloud integration
Minimal streamable HTTP MCP server used for owned-account registry connectivity tests.
MCP server to assist with JxBrowser development.
A simple MCP server built with FastMCP and python
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/piebro/echo-mcp-server-for-testing'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
