MCP Hackathon Server
OfficialGSA MCP Hackathon — Шаблон сервера
Готовый стартовый набор для создания сервера Model Context Protocol (MCP) на Python, а также комплекты для развертывания на IBM Cloud (watsonx Orchestrate) и Databricks.
Создано с помощью FastMCP и uv. Если вы никогда не создавали MCP-сервер, начните с QUICKSTART.md.
Что такое MCP-сервер?
MCP-сервер предоставляет инструменты (функции, которые может вызывать модель), подсказки (многоразовые стартовые фразы для разговора) и ресурсы (данные, которые модель может читать) ИИ-клиенту, такому как Claude Desktop, Claude Code или агентной платформе, например watsonx Orchestrate. Вы пишете инструменты; модель клиента решает, когда их вызывать.
Этот шаблон дает вам работающий сервер с одним примером каждого типа, так что вы можете заменить примеры на свой собственный сервис и развернуть его.
Related MCP server: Python MCP Server Template
Структура репозитория
mcp-hackathon-template/
├── README.md # This file
├── QUICKSTART.md # 5-minute clone → run → connect walkthrough
├── main.py # Local entry point (uv run python main.py)
├── pyproject.toml # Package + dependencies (uv)
├── requirements.txt # Mirror of runtime deps (for buildpack hosts)
├── Dockerfile # Container image (streamable-HTTP, port 8080)
├── manifest.yaml # cloud.gov (Cloud Foundry) deploy
├── server.json # MCP registry metadata
├── .env.example # Copy to .env for local dev
├── .github/workflows/ci.yml # Lint + test on push/PR
├── src/
│ └── example_server/ # ← rename to your service
│ ├── app.py # Thin entry point: builds FastMCP, picks transport
│ ├── config.py # Settings from env vars / .env
│ ├── models.py # Pydantic models & enums for tool params
│ ├── utils.py # Shared helpers (HTTP client, pagination)
│ ├── routes.py # HTTP-only routes (/health, /version)
│ ├── tools/ # ONE FILE PER TOOL
│ │ ├── __init__.py # register_tools(mcp) aggregator
│ │ └── example_tool.py
│ ├── prompts/
│ │ ├── __init__.py # register_prompts(mcp) aggregator
│ │ └── example.py
│ └── resources/
│ ├── __init__.py # register_resources(mcp) aggregator
│ └── example.py
├── tests/ # Import + registration smoke tests
├── eval/ # Stub → build a Phoenix eval harness (see mcp-eval skill)
└── deploy/
├── README.md # Which deployment kit to use
├── ibm/ # watsonx Orchestrate: 3 kits (see below)
└── databricks/ # Databricks Apps kitНачало работы
Предварительные требования
uv —
pip install uvилиbrew install uv
Установка и запуск
cp .env.example .env
uv sync
uv run python main.pyСервер запускается в режиме stdio — он общается через JSON-RPC по stdin/stdout, именно так локальные клиенты (Claude Desktop, Claude Code) запускают его. См. QUICKSTART.md, чтобы подключить клиента.
Проверка
uv sync --group dev
uv run pytest tests/ -v # tests
uv run ruff check . # lintШаблон «один инструмент на файл»
Каждый инструмент находится в собственном файле в каталоге src/example_server/tools/ и предоставляет функцию register(mcp). tools/__init__.py вызывает каждый из них из единой функции register_tools(mcp). Это позволяет легко просматривать список инструментов и добавлять или удалять интеграцию, изменяя всего два файла.
Шаг 1 — создайте src/example_server/tools/my_tool.py:
from typing import Annotated
from fastmcp import FastMCP
from example_server.utils import fetch_json
def register(mcp: FastMCP) -> None:
@mcp.tool(
name="example_get_thing",
annotations={
"title": "Get a thing",
"readOnlyHint": True,
"destructiveHint": False,
"idempotentHint": True,
"openWorldHint": True,
},
)
async def get_thing(thing_id: Annotated[str, "The ID to fetch."]) -> dict:
"""One-line summary. Document the data source, its update cadence,
and the return shape here — the model reads this docstring."""
return await fetch_json(f"https://api.example.gov/things/{thing_id}")Шаг 2 — подключите его в tools/__init__.py:
from example_server.tools import example_tool, my_tool
def register_tools(mcp) -> None:
example_tool.register(mcp)
my_tool.register(mcp) # ← add this lineШаг 3 — добавьте любой API-ключ как типизированное поле в config.py и задокументируйте переменную окружения в .env.example.
Подсказки (prompts/) и ресурсы (resources/) следуют точно такому же шаблону register(mcp) + агрегатор.
Переименование пакета
Перед публикацией сервера переименуйте example_server в ваш сервис (например, census_mcp):
Переименуйте папку
src/example_server/→src/<your_name>/.Обновите
pyproject.toml:[project].name,[project.scripts]и[tool.hatch.build.targets.wheel].packages.Найдите и замените
example_serverво всех файлахsrc/,tests/,main.py,Dockerfileиmanifest.yaml.
Советы по дизайну инструментов (федеральные данные)
Возвращайте структурированные данные, а не текст. Возвращайте словари/списки с согласованными ключами и позвольте модели описывать их.
Документируйте актуальность. Федеральные наборы данных отстают; укажите частоту обновления и дату "по состоянию на" в docstring.
Предоставляйте пагинацию. Используйте
PaginationParams/paginate()изutils.pyи возвращайтеhas_more/next_offset.Используйте явные тайм-ауты.
utils.fetch_jsonпо умолчанию 30 секунд.Полезные ошибки. Возвращайте словарь ошибок с
hint, а не сырой стек вызовов.
Развертывание
Локальная разработка использует stdio. Чтобы поделиться сервером с агентной платформой, разверните его и зарегистрируйте. См. deploy/README.md для выбора варианта, затем:
IBM watsonx Orchestrate — deploy/ibm/ (три комплекта: локальный инструментарий stdio, Code Engine сборка из Git и готовый образ).
Databricks Apps — deploy/databricks/.
Оба используют один и тот же код сервера; app.py автоматически обслуживает HTTP, когда платформа предоставляет порт.
Оценка
Измерение того, насколько хорошо LLM может использовать ваши инструменты, — это настоящая проверка качества сервера. Этот шаблон намеренно не включает средство оценки — см. eval/README.md о том, как создать его с помощью навыка mcp-eval.
Лицензия
MIT. См. SECURITY.md о политике раскрытия уязвимостей и примечаниях по безопасности хакатона.
Available Tools
1 toolexample_search_datasetsSearch Datasets (example)BRead-onlyIdempotent
Search federal datasets matching a query string. (STUB — replace me.)
This stub shows the shape of a real tool without calling a live API.
Swap the body for an actual request using fetch_json(...); a worked
pattern is included below in a comment.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keywords to search dataset titles and descriptions. | |
| pagination | No | Optional limit/offset (defaults to limit=20, offset=0). | |
| response_format | No | Return machine-readable JSON (default) or human-readable Markdown. | json |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint: false) already establish the safety profile. The description adds one meaningful behavioral fact—'This stub... without calling a live API'—which is honest and useful context. However, it doesn't elaborate on return behavior, result ordering, or error conditions, leaving most of the behavioral burden on the annotations. No contradiction with the annotations is present.
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 text is short and front-loads the meaningful description, which is good. However, roughly three of four lines are implementer-facing stub notes ('(STUB — replace me.)', 'Swap the body...', 'a worked pattern is included below in a comment') that add no value to an agent selecting or invoking the tool. It's not verbose, but those sentences could earn their place better with functional detail.
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?
Given the presence of an output schema, return values don't need explanation. The schema plus annotations cover the mechanical calling contract well. However, the description leaves open real-world questions an agent might face, such as what 'federal datasets' covers, and pagination behavior. For an explicitly stubbed example tool, this is adequate, but not complete for a production tool.
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?
Schema description coverage is 100%, and the schema thoroughly documents all three parameters: the required 'query', the pagination object with limits and defaults, and the response_format enum with its default. Per calibration rules, the baseline is 3 when the schema covers everything. The description itself adds no parameter-level insight, which is acceptable at this coverage level.
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 opening sentence 'Search federal datasets matching a query string' uses a specific verb and resource, so an agent immediately knows what the tool does. The remaining stub text is implementer-oriented but does not obscure the purpose. It earns a 4 because it's clear on function, though the nonsensical stub placeholder text prevents a 5.
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?
There is no guidance on when to use this tool versus alternatives, what input it expects conceptually (beyond schema), or any prerequisites or limitations. The stub text discusses implementation ('Swap the body for an actual request') rather than agent-facing usage. While the absence of sibling tools lowers the need for differentiation, the description still provides no real usage context.
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. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
example_search_datasets
TDQS
Only one tool exists, so there is no possibility of confusion or overlap. The sole tool has a clear, singular purpose.
With a single tool, there is no inconsistency in naming conventions. The name follows a verb_noun pattern (search_datasets).
A single stub tool is a trivial surface for a server, far below the typical 3-15 tool range. It does not constitute a meaningful tool set.
The server is explicitly a stub with no real functionality, offering only a placeholder search. It has no coverage of any actual domain or workflow.
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