How do I use simple_mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@simple_mcp Generate a random float between 0 and 1." 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.

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simple_mcp

by oleksii-donets

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

# Simple MCP Server (Python)

This project provides a minimal Model Context Protocol (MCP) server using Python. It exposes a single tool random_float that returns a random float in [0,1). The server supports multiple transport modes: STDIO (default), HTTP, and SSE.

Run locally

Default (STDIO transport)

uv run main.py

Related MCP server: Server MCP

Server Environment Variables

Variable	Purpose	Default
APP_TRANSPORT	Transport type (STDIO, HTTP, SSE)	STDIO
APP_HOST	Server host	0.0.0.0
APP_PORT	Server port	8000

Docker

Build the image and run:

docker build -t simple-mcp .

Docker with different transports

# HTTP transport
docker run --rm -p 8000:8000 -e APP_TRANSPORT=HTTP simple-mcp

# SSE transport
docker run --rm -p 8000:8000 -e APP_TRANSPORT=SSE simple-mcp

# Custom port
docker run --rm -p 9000:9000 -e APP_TRANSPORT=HTTP -e APP_PORT=9000 simple-mcp

Test as a standalone app

Test using Postman

uv --directory <path_to_project_directory> run main.py

Test using Claude Desktop

{
  "mcpServers": {
    "simple_mcp": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "<path_to_project_directory>",
        "main.py"
      ]
    }
  }
}

Test as a docker image

Test using Postman

docker run --rm -i simple-mcp

Test using Claude Desktop

{
  "mcpServers": {
    "simple_mcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "simple-mcp"
      ]
    }
  }
}

Available Tools

2 tools

random_floatA

Return a random float in [0,1).

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of explaining behavior. It clearly states the stochastic nature ('random') and the return interval '[0,1)' though it does not explicitly mention uniformity or other implementation details. This is adequate for a simple, stateless utility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, concise sentence is used—'Return a random float in [0,1).' It is front-loaded, contains no filler or redundant wording, and is exactly as long as needed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with an output schema, the description fully specifies the result with no ambiguities. The interval is explicit, and no additional side effects or requirements exist. It is complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so the input schema has no descriptive gaps. The rubric establishes a baseline of 4 for zero-parameter tools, and the description adds no needed semantics beyond what is obvious.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Return') and a clear resource ('a random float') with an exact range ('[0,1)'). It fully distinguishes the tool's purpose from the sibling tool 'wait', leaving no ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool or mention alternatives. However, the usage is implied by the purpose: use it when a random float in [0,1) is needed. No formal guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

waitA

Wait for the specified time in milliseconds and return a message.

ParametersJSON Schema

Name	Required	Description	Default
`ms`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the transparency burden. It clearly states that the tool waits for the specified time and returns a message, which is the core behavior. However, it does not disclose details such as whether the operation is synchronous, potential error conditions, or the exact content of the returned message.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that conveys the essential purpose without any fluff. It is concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For such a simple tool with one parameter and an output schema, the description is largely complete. It explains the action and the return of a message. It does not detail the message content, but the output schema likely covers that. Minor gaps are acceptable given the simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explicitly clarifies that the parameter is in milliseconds, which adds meaning beyond the schema's property name and type. This is particularly helpful given the schema description coverage is 0%.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Wait' and the resource 'specified time in milliseconds', and specifies that it returns a message. It is specific and distinguishes itself from the sibling tool 'random_float', which has a completely different purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 versus alternatives, nor any preconditions or exclusions. It only states what the tool does without contextualizing it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

2 tool updatesv0.1.0Aug 21, 2026
- First observedrandom_float
- First observedwait

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored 2026-08-21 07:45 across 2 tools

Disambiguation5/5

The two tools, random_float and wait, have completely distinct purposes—one generates a random number and the other introduces a delay. There is no ambiguity whatsoever.

Naming Consistency4/5

Both names are in lowercase snake_case, but random_float is a noun phrase while wait is a verb, creating a minor inconsistency in style. However, with only two tools, the names are clear and identifiable, so the deviation is negligible.

Tool Count3/5

The server exposes only two tools, which feels thin for a general-purpose utility server. However, the scope itself is extremely narrow (random number and delay), so the count is borderline—not excessive, but minimal for broader use cases.

Completeness3/5

The domain appears to be basic utility operations, but with only two tools, there are obvious gaps such as random integer generation or more granular timing controls. The surface is too minimal to consider it complete for any meaningful utility purpose.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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Run locally

Default (STDIO transport)

Server Environment Variables

Docker

Docker with different transports

Test as a standalone app

Test using Postman

Test using Claude Desktop

Test as a docker image

Test using Postman

Test using Claude Desktop

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Math Calculator MCP Server

Server MCP

tiny-mcp-server

MCP