How do I use hello-mcp-server?

1. Click on "Install 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., "@hello-mcp-server what is 0.1 plus 0.2?" 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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hello-mcp-server

by kuldeepcodes

Overview Schema Related Servers Score Discussions

Python

Hybrid

hello-mcp-python

Python 3.14 licence: MIT

A hello-world Model Context Protocol server in Python, plus a console chat client that drives its tools with a small local LLM.

It is deliberately small, but it is not a toy. It uses the official Python MCP SDK, serves both transports (stdio and streamable HTTP), is covered by 29 automated tests including real protocol round-trips over a real pipe, and handles the things that actually break MCP servers in practice.

New to MCP? Start with GETTING-STARTED.md — it builds this entire project from an empty directory, one step at a time, explaining every dependency and every file.

Quick start

Prerequisites: Python 3.14 or newer.

git clone https://github.com/kuldeepcodes/hello-mcp-python.git
cd hello-mcp-python

python -m venv .venv
source .venv/bin/activate        # Windows: .\.venv\Scripts\Activate.ps1

python -m pip install -e ".[dev]"
python -m pytest                 # 29 tests

# Works with no model at all, using deterministic keyword routing
python -m hello_mcp.chat --provider none --ask "hello Kuldeep"

For real conversation, install Ollama and pull a small model:

ollama pull phi3          # ~2.2 GB, works with the prompt planner
python -m hello_mcp.chat --ask "what is 17.5 plus 24.25?"

Related MCP server: Pistachio MCP Server

Server tools

Tool	Description
`say_hello`	Greets someone by name in 10 languages: `en`, `es`, `fr`, `de`, `it`, `pt`, `hi`, `ja`, `zh`, `ar`.
`echo`	Returns a message verbatim; useful for connection checks.
`get_server_time`	Returns structured `utc`, `local`, `timeZone`, `utcOffset` and `human` fields.
`add`	Adds two numbers with decimal formatting, so `0.1 + 0.2` is `0.3`.

The server also exposes prompts (friendly_greeting, summarize_capabilities) and resources (hello://server/info, plus templated hello://greetings/{language}).

Running the server

# stdio, for local MCP clients
.\.venv\Scripts\python.exe -m hello_mcp.server

# streamable HTTP, endpoint /mcp and liveness /healthz
.\.venv\Scripts\python.exe -m hello_mcp.server --http --port 5099

In stdio mode, stdout is reserved for JSON-RPC. All logging is deliberately sent to stderr.

MCP client configuration

VS Code or Claude Desktop-style stdio config. Use absolute paths — the client does not run from your project directory:

{
  "mcpServers": {
    "hello-mcp-python": {
      "command": "/absolute/path/to/hello-mcp-python/.venv/bin/python",
      "args": ["-m", "hello_mcp.server"],
      "cwd": "/absolute/path/to/hello-mcp-python"
    }
  }
}

On Windows the interpreter is ...\\.venv\\Scripts\\python.exe, and backslashes must be escaped in JSON.

HTTP clients can connect to http://127.0.0.1:5099/mcp after starting the server with --http.

Chat strategies

Strategy	When chosen	How it works
Native tool calling	The model accepts a probe request with a `tools` array.	The model emits tool calls directly.
Prompt planner	The model is reachable but rejects tools, as `phi3` does in Ollama.	The app shows tool names, descriptions, and JSON schemas, asks for one JSON decision, executes it, then asks the model to phrase the result.
Offline routing	No model is reachable, or `--provider none` is used.	Deterministic keyword rules support `hello NAME`, `what time is it`, `add 2 and 3`, and `echo ...`.

The selected strategy and reason are printed at startup.

Real transcript

  hello-mcp-chat v1.0.0
  a Model Context Protocol client for Python

Connected to hello-mcp-server (4 tools)
Model strategy: prompt planner - Ollama says this model does not support tools

  [tool] add {"a": 17.5, "b": 24.25} -> 41.75
bot> The sum of 17.5 and 24.25 is 41.75.

Tests and linting

.\.venv\Scripts\python.exe -m ruff check .
.\.venv\Scripts\python.exe -m pytest

The integration tests spawn the real server over stdio, perform a real MCP handshake, list tools, call tools, list prompts, read resources, and assert stdout contains only JSON-RPC.

Limitations

The prompt planner is intentionally conservative and less reliable than native tool calling.
HTTP transport has no authentication; this is a local teaching project.
Windows needs the tzdata package for IANA time zones such as Asia/Kolkata.

Built with

mcp==2.0.0 — official Python MCP SDK. In this version the ergonomic API is mcp.server.mcpserver.MCPServer; older examples may call this style FastMCP.
httpx — Ollama and OpenAI-compatible HTTP calls.
pytest — unit and integration tests.
ruff — linting and formatting.

The same project in other languages

This is one of three parallel implementations, same tools, same behaviour, same lessons:

hello-mcp-dotnet — C# / .NET 10
hello-mcp-java — Java 17 / Spring Boot
hello-mcp-python — Python 3.14+ (you are here)

Licence

MIT — see LICENSE.

Available Tools

4 tools

addAdd two numbersA

Adds two numbers and returns their sum. Prefer this over doing arithmetic yourself.

ParametersJSON Schema

Name	Required	Description	Default
`a`	Yes
`b`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It states that this is a pure computation: it adds the two numbers and returns the sum, with no mention of side effects or external state. It does not discuss numeric edge cases, but none are particularly relevant for a simple addition tool.

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 two short sentences with no wasted text. The first sentence states the complete behavior and return value, and the second adds a useful usage directive. It is front-loaded and easy to parse.

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 two-parameter arithmetic tool, the description covers the operation, the inputs, and the return value. No output schema exists, but 'returns their sum' is enough to describe the successful outcome. The tool is simple enough that nothing essential is missing.

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

Parameters3/5

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

The input schema provides only names and number types, with no descriptive text. The description says 'two numbers' and 'their sum,' which maps to the a and b parameters and clarifies that both are operands in the addition. This is adequate for such a simple case, though it does not add deeper individual-parameter details.

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 operation ('Adds two numbers') and the result ('returns their sum'), using a specific verb-resource form. It is immediately distinguishable from the sibling tools, which are unrelated (say_hello, echo, get_server_time).

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

Usage Guidelines4/5

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

It gives an explicit usage directive: 'Prefer this over doing arithmetic yourself.' It does not name any alternative tool, but none of the siblings are arithmetic-related, so there is no real alternative to distinguish. The guidance is sufficient for such a simple operation.

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

echoEcho a messageA

Echoes a message back verbatim. Useful for verifying that the connection between the client and this MCP server is healthy.

ParametersJSON Schema

Name	Required	Description	Default
`message`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the burden of disclosure. It clearly conveys that the tool performs no transformation and returns the message exactly as provided, implying a safe, stateless operation. It does not mention error cases or side effects, but there is no indication any exist.

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?

Two sentences, no filler: the first states behavior, the second gives practical context. The important verb-and-echo concept is front-loaded.

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 tool this simple, the description is complete. It defines the sole parameter, the behavior, and the use case, and the lack of an output schema is acceptable because the tool's output is obvious from 'echoes ... back verbatim.'

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 schema provides zero description coverage, so the description must compensate. It does by clarifying that the `message` parameter is the input that will be echoed back verbatim. This is sufficient for a single-string parameter, though more detail about constraints or format could be added.

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 states a specific verb and resource: 'Echoes a message back verbatim.' This clearly differentiates it from siblings like say_hello, get_server_time, and add, all of which have different behaviors.

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

Usage Guidelines4/5

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

The description explicitly states its use: 'Useful for verifying that the connection between the client and this MCP server is healthy.' It does not describe when not to use it or list alternatives, but the intended context is clear.

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

get_server_timeGet server timeA

Returns the current date and time on the machine hosting this MCP server. Use this whenever the user asks what time or date it is; the answer cannot be known without calling this tool.

ParametersJSON Schema

Name	Required	Description	Default
`time_zone`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden. It clearly conveys that this is a read-only operation that reports server-local time rather than the client's time, and it explains why the tool must actually be invoked. There is no hidden mutation or surprising side effect, though it could optionally mention that time_zone affects the returned representation.

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 exactly two sentences, front-loaded with the main purpose and immediately followed by usage guidance. There is no filler, redundant restating of the title, or unnecessary detail.

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

Completeness3/5

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

This is a simple tool with one optional parameter and an output schema, so the description does not need to explain return values. However, the behavior of the time_zone parameter is not addressed anywhere, so an agent could not confidently know how to request a time in a specific timezone or why the parameter exists.

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

Parameters2/5

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

The input schema provides only a 'time_zone' property with a default of null and no description. The tool description does not explain how time_zone changes the result, whether null means server-local time, or what formats are accepted. Since the description provides zero parameter explanation and schema description coverage is 0%, this is a clear gap.

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 ('Returns') and resource ('current date and time on the machine hosting this MCP server'), making the tool's action and result immediately clear. It also distinguishes this tool from siblings like say_hello, echo, and add by defining its exact purpose.

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

Usage Guidelines5/5

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

The description explicitly states when to use it: 'Use this whenever the user asks what time or date it is.' It also adds a strong practical instruction by noting that the answer cannot be known without calling this tool, helping the agent avoid guessing.

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

say_helloSay helloA

Greets a person by name. Use this whenever the user asks to greet, welcome, or say hello to someone. Supports several languages via an ISO 639-1 code.

ParametersJSON Schema

Name	Required	Description	Default
`name`	Yes
`language`	No		en

TDQS

A4.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool supports multiple languages and requires a person's name, which is useful. However, it does not describe output format, potential side effects, or any limitations/error behaviors—though as a greeting tool, the behavioral surface is small. A score of 3 is appropriate because the description covers core behavior but not edge details.

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?

Three sentences, all essential. First sentence defines action, second establishes usage context, third explains param. No filler or redundant restatement of the title.

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?

The tool is simple with 2 parameters, no nested objects, no output schema, and no annotations. The description covers what the tool does, when to use it, and clarifies parameters. Minor gap: does not list accepted language codes or the greeting format, but the default 'en' is in schema. Adequate for making a correct call.

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?

Schema description coverage is 0%: the schema provides only field names and types, with no descriptions. The tool description compensates by explaining that 'name' is the person to greet and 'language' accepts an ISO 639-1 code. It doesn't document possible values for language beyond default 'en', but it gives enough meaning to infer usage. Since the description adds meaningful semantics beyond the bare schema, a 4 is justified.

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 states a specific verb and resource: 'Greets a person by name.' It clearly distinguishes itself from sibling tools (echo, get_server_time, add) by focusing on greeting functionality. The mention of language support via ISO 639-1 adds specificity.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'Use this whenever the user asks to greet, welcome, or say hello to someone.' This provides clear contextual guidance and implicitly contrasts with sibling tools that serve different purposes.

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

Install Server

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a completely distinct purpose: greeting, echoing, retrieving time, and adding numbers. There is no overlap or ambiguity in what an agent should call.

Naming Consistency4/5

All names are lowercase snake_case and use a verb-first style, but 'echo' and 'add' are bare verbs while 'say_hello' and 'get_server_time' have object/adjective complements. This is a minor inconsistency, not a confusing mix.

Tool Count5/5

Four tools is an appropriate, well-scoped count for a small hello/utility MCP server. Each tool is independently useful and the count is firmly within the ideal range.

Completeness4/5

The set covers its obvious standalone capabilities fully—greetings, echoes, time, and arithmentic are all self-contained. The only minor gap is that it is not a fully powered calculator and has no broader domain expectations, but nothing needed seems missing.

Maintenance

ActivityMaintained

ResponsivenessSyncing

Resources

GitHub Repository

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