MCP Hackathon Server
OfficialGSA MCP Hackathon – Servervorlage
Ein sofort einsatzbereiter Starter zum Erstellen eines Model Context Protocol (MCP)-Servers in Python, plus Bereitstellungskits für IBM Cloud (watsonx Orchestrate) und Databricks.
Erstellt mit FastMCP und uv. Wenn Sie noch nie einen MCP-Server erstellt haben, beginnen Sie mit QUICKSTART.md.
Was ist ein MCP-Server?
Ein MCP-Server stellt Tools (Funktionen, die das Modell aufrufen kann), Prompts (wiederverwendbare Gesprächsstarter) und Ressourcen (Daten, die das Modell lesen kann) für einen KI-Client wie Claude Desktop, Claude Code oder eine Agentenplattform wie watsonx Orchestrate bereit. Sie schreiben die Tools; das Modell des Clients entscheidet, wann es sie aufruft.
Diese Vorlage bietet Ihnen einen funktionierenden Server mit einem Beispiel für jede Art, sodass Sie die Beispiele durch Ihren eigenen Dienst ersetzen und bereitstellen können.
Related MCP server: Python MCP Server Template
Repository-Struktur
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 kitErste Schritte
Voraussetzungen
uv –
pip install uvoderbrew install uv
Installieren und ausführen
cp .env.example .env
uv sync
uv run python main.pyDer Server startet im stdio-Modus – er kommuniziert über stdin/stdout per JSON-RPC, so starten lokale Clients (Claude Desktop, Claude Code) ihn. Siehe QUICKSTART.md, um einen Client zu verbinden.
Überprüfen
uv sync --group dev
uv run pytest tests/ -v # tests
uv run ruff check . # lintDas Muster „Ein Tool pro Datei“
Jedes Tool lebt in einer eigenen Datei unter src/example_server/tools/ und stellt eine register(mcp)-Funktion bereit. tools/__init__.py ruft jede aus einem einzigen register_tools(mcp) auf. Dadurch bleibt die Tool-Liste überschaubar und Sie können eine Integration hinzufügen oder entfernen, indem Sie zwei Dateien anfassen.
Schritt 1 – erstellen Sie 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}")Schritt 2 – binden Sie es in tools/__init__.py ein:
from example_server.tools import example_tool, my_tool
def register_tools(mcp) -> None:
example_tool.register(mcp)
my_tool.register(mcp) # ← add this lineSchritt 3 – fügen Sie einen beliebigen API-Schlüssel als typisiertes Feld in config.py hinzu und dokumentieren Sie die Umgebungsvariable in .env.example.
Prompts (prompts/) und Ressourcen (resources/) folgen dem exakt gleichen register(mcp)- + Aggregator-Muster.
Paket umbenennen
Bevor Sie Ihren Server veröffentlichen, benennen Sie example_server in Ihren Dienst um (z. B. census_mcp):
Benennen Sie den Ordner
src/example_server/insrc/<your_name>/um.Aktualisieren Sie
pyproject.toml: den[project].name, die[project].scriptsund die[tool.hatch.build.targets.wheel].packages.Suchen-und-ersetzen Sie
example_serverinsrc/,tests/,main.py,Dockerfileundmanifest.yaml.
Tipps zur Tool-Entwicklung (Bundesdaten)
Geben Sie strukturierte Daten zurück, keinen Prosa. Geben Sie Dictionaries/Listen mit konsistenten Schlüsseln zurück und lassen Sie das Modell erzählen.
Dokumentieren Sie die Aktualität. Bundesdatensätze hinken hinterher; geben Sie die Aktualisierungshäufigkeit und das „Stand“-Datum im Docstring an.
Stellen Sie Paginierung bereit. Verwenden Sie
PaginationParams/paginate()ausutils.pyund geben Siehas_more/next_offsetzurück.Verwenden Sie explizite Timeouts.
utils.fetch_jsonhat standardmäßig 30s.Umsetzbare Fehler. Geben Sie ein Fehler-Dictionary mit einem
hintzurück, keinen rohen Stack-Trace.
Bereitstellung
Die lokale Entwicklung verwendet stdio. Um Ihren Server mit einer Agentenplattform zu teilen, stellen Sie ihn bereit und registrieren Sie ihn. Siehe deploy/README.md für eine Auswahlhilfe, dann:
IBM watsonx Orchestrate – deploy/ibm/ (drei Kits: lokales stdio-Toolkit, Code Engine build-from-Git und vorgefertigtes Image).
Databricks Apps – deploy/databricks/.
Beide lesen denselben Servercode; app.py bedient automatisch HTTP, wenn die Plattform einen Port injiziert.
Evaluierungen
Zu messen, wie gut ein LLM Ihre Tools nutzen kann, ist der wahre Test der Serverqualität. Diese Vorlage enthält absichtlich kein Evaluierungs-Framework – siehe eval/README.md für den Bau eines solchen mit der mcp-eval-Fähigkeit.
Lizenz
MIT. Siehe SECURITY.md für die Richtlinie zur Offenlegung von Schwachstellen und die Sicherheitshinweise zum Hackathon.
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.
1 tool update
v0.1.0- First observed
example_search_datasets
TDQS
Scored across 1 tool
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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