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Shop MCP Server

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Shop MCP Server

Lokaler MCP-Server für die Analyse der SQLite-Datenbank eines Online-Shops. Der KI-Agent ruft ein MCP-Tool auf, der Server wandelt die Anfrage in ein sicheres SQL SELECT um, liest direkt shop.db und gibt ein strukturiertes Ergebnis zurück.

Host-приложение
├── AI-агент / LLM
└── MCP-клиент
        │ JSON-RPC / stdio
        ▼
    Shop MCP Server
        │ read-only SQLite
        ▼
    data/shop.db

Funktionen

  • direkte Verbindung zu SQLite ohne HTTP und separaten DB-Server;

  • MCP-Transport stdio;

  • Erkennung von query_shop und inspect_database über tools/list;

  • acht Beispielaufgaben in der Beschreibung und im JSON-Schema des Tools;

  • Klartext, JSON-Intent oder fertiges sicheres SELECT;

  • Ausschluss von Bestellungen mit Status cancelled aus der gesamten Analyse;

  • Paginierung und Begrenzung des Ergebnisses;

  • mehrstufiger Read-only-Schutz.

Related MCP server: Read-Only SQLite Shop Database MCP Server

Installation

Erforderlich ist Python 3.11 oder neuer.

python3 -m venv .venv
.venv/bin/python -m pip install -e '.[dev]'

Die vorbereitete Datenbank befindet sich bereits in data/shop.db. Sie enthält 150 Kunden, das Feld customers.country ist bei allen Kunden ausgefüllt, und 45 Kunden gehören zu Germany.

Erneute Vorbereitung der Datenbank

Die ursprüngliche Datenbank wird nicht verändert. Der Befehl erstellt eine neue Arbeitskopie und fügt country TEXT NOT NULL mit reproduzierbaren synthetischen Werten hinzu:

.venv/bin/shop-prepare-db /path/to/original/shop.db ./data/shop.db

Die Verteilung hängt nur von customers.id ab, daher liefert die erneute Vorbereitung dieselben Werte. Die Länder sind Testdaten und beschreiben nicht den tatsächlichen Standort der Kunden.

Pfadkonfiguration

Der Pfad wird in folgender Reihenfolge bestimmt:

  1. SHOP_DB_PATH;

  2. JSON-Datei aus SHOP_MCP_CONFIG oder config/shop_mcp.json;

  3. data/shop.db relativ zum Arbeitsverzeichnis.

Ein Beispiel für die Umgebungsvariable befindet sich in .env.example, und die MCP-Client-Konfiguration in config/mcp.example.json. Ersetzen Sie /absolute/path/to/MCPDeveloper durch den absoluten Pfad zum Projekt.

Start

Normalerweise startet der MCP-Client den Server aus seiner Konfiguration. Für den manuellen Start des Prozesses:

SHOP_DB_PATH=./data/shop.db .venv/bin/python -m shop_mcp.server

stdout ist für MCP JSON-RPC reserviert. Die Diagnose wird nicht in den Protokollstrom ausgegeben.

Werkzeuge

inspect_database

Argumente:

{"action": "list_tables"}

oder:

{"action": "describe_table", "table": "customers"}

query_shop

Argumente:

{
  "request": "Сколько клиентов из Германии?",
  "limit": 100,
  "offset": 0
}

Beispiele, die über tools/list deklariert werden:

  1. Welche Tabellen gibt es in der Datenbank und welche Felder enthalten sie?

  2. Wie viele Kunden kommen aus Deutschland?

  3. In welchem Land gibt es die meisten Kunden?

  4. Welcher Kunde hat am meisten ausgegeben? Gib Name, E-Mail und Gesamtsumme zurück.

  5. Zeige die Top-5-Produkte nach verkaufter Menge und Umsatz.

  6. Zeige die Top-3-Kategorien nach Umsatz.

  7. Wie hoch war der Umsatz im Jahr 2025?

  8. Welcher Kunde hat die meisten Bestellungen aufgegeben?

Ein strukturierter Intent kann übergeben werden:

{
  "request": "{\"intent\": \"revenue_by_year\", \"year\": 2025}"
}

Oder eine fertige Abfrage:

{
  "request": "SELECT name, category FROM products ORDER BY name"
}

Geschäftsregeln

  • orders.status = 'cancelled' fließt nicht in die Kennzahlen ein.

  • Kundenausgaben und Jahresumsatz werden anhand von orders.total_amount berechnet.

  • Der Umsatz von Produkten und Kategorien wird als quantity * unit_price berechnet.

  • Das Jahresintervall ist halboffen: vom 1. Januar einschließlich bis zum 1. Januar des Folgejahres ausschließlich.

  • In der mitgelieferten Datenbank gehören alle Bestellungen zum Jahr 2026, daher beträgt der Umsatz für 2025 0.

Sicherheit

Der Server öffnet SQLite über mode=ro, aktiviert query_only, richtet einen SQLite-Authorizer ein, verbietet mehrere Anweisungen, DML, DDL, ATTACH, verändernde PRAGMA, das Laden von Erweiterungen und begrenzt die Ausführungszeit.

Jeder Versuch, eine andere Operation als SELECT auszuführen, gibt eine präzise Antwort zurück:

Не доступный вариант запроса

Interne SQLite-Fehler, Stack-Traces und lokale Pfade werden nicht an den Client zurückgegeben.

Prüfung

.venv/bin/pytest
.venv/bin/ruff check .
.venv/bin/ruff format --check .

Die Tests umfassen den realen Start des MCP-Servers als Subprozess, die Verbindung von ClientSession über stdio, tools/list, den Aufruf beider Tools, analytische Szenarien und Versuche, die Datenbank zu verändern.

Die vollständige genehmigte Spezifikation befindet sich in docs/specification.md.

Available Tools

2 tools
inspect_databaseПросмотр структуры базы интернет-магазинаB

Возвращает список таблиц или поля выбранной таблицы. Используйте action=list_tables либо action=describe_table.

NameRequiredDescriptionDefault
tableNoНазвание таблицы для describe_table
actionYesДействие: list_tables или describe_table

Output Schema

NameRequiredDescription
resultYes

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It only states that it returns tables or fields, which is basic. It does not disclose any side effects, permission requirements, error behaviors, or what happens when the table does not exist. For a read-only inspection tool this is relatively benign, but the coverage is minimal.

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 sentence that fully conveys the essential information. It is front-loaded with the primary function and immediately outlines the two actions. No redundancy or filler.

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?

Given that an output schema exists, the return format is likely covered elsewhere. The description is sufficient for a simple schema-inspection tool, but it lacks any mention of error handling or the distinction from query_shop. It is adequate but not comprehensive.

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 already provides 100% coverage: 'action' is described as 'list_tables or describe_table' and 'table' as 'table name for describe_table'. The description repeats this mapping without adding new information, so it meets the baseline but does not exceed it.

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 tool's purpose: it returns a list of tables or fields of a selected table. This is a specific verb-resource combination and distinguishes it from the sibling query_shop, which presumably queries data rather than schema. The two modes (list_tables, describe_table) are explicitly mentioned.

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 instructions on how to use the tool (choose action) but does not explain when to use this tool versus the sibling query_shop. It offers no context about typical scenarios, prerequisites, or exclusions. The usage guidance is limited to internal action selection, not selection among alternatives.

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

query_shopЗапрос к базе интернет-магазинаA

Выполняет аналитический запрос к базе интернет-магазина только для чтения. MCP преобразует запрос в безопасный SQL SELECT. Отменённые заказы не учитываются. Примеры: 1) Какие таблицы есть в базе и какие в них поля? 2) Сколько клиентов из Германии? 3) В какой стране больше всего клиентов? 4) Какой клиент потратил больше всего? Верни имя, email и общую сумму. 5) Покажи топ-5 товаров по проданному количеству и выручке. 6) Покажи топ-3 категории по выручке. 7) Какая выручка была в 2025 году? 8) Какой клиент сделал больше всего заказов?

NameRequiredDescriptionDefault
limitNoМаксимальное число строк
offsetNoСмещение для пагинации
requestYesАналитический запрос обычным текстом, JSON intent или SQL SELECT.

Output Schema

NameRequiredDescription
resultYes

TDQS

A3.9/5.0
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 behavioral disclosure. It explicitly states the tool is read-only, that MCP converts queries into safe SQL SELECT, and that canceled orders are excluded from results. This is useful and goes beyond minimal. It does not mention error handling or response format, but the core behavior is well disclosed.

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

Conciseness4/5

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

The description is fairly long because it contains eight examples, but those examples are practical and illustrate valid request formats that are directly relevant to the tool. The purpose statement is front-loaded, and each example adds value, so the length is justified. It is well-structured and not overly verbose.

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 a natural-language query tool that has an output schema (indicated by the schema signals), the description covers essential context: what the tool does, how it handles queries, and what data is excluded (canceled orders). It could add more about limitations or error behavior, but given the output schema exists and the description is already thorough, it is largely complete.

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?

Schema description coverage is 100% — every parameter has a clear description. The description itself adds little beyond the schema: it repeats the request examples but does not elaborate on limit or offset semantics, which the schema already covers adequately. The baseline of 3 is appropriate because the schema handles the heavy lifting.

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 and resource: 'performs a read-only analytical query to the online store database' and explains the MCP converts queries to safe SQL SELECT. It is specific about being read-only and analytical, which differentiates it from a generic database tool and gives a clear sense of its function, though it does not explicitly name the sibling tool.

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?

Usage is implied through a set of concrete examples that show typical analytical questions (e.g., 'which country has most customers?'). However, the description does not explicitly state when to use this tool versus the sibling 'inspect_database', nor does it provide any exclusions or when-not-to-use guidance. It relies on the reader to infer that this is for read-only analytical queries.

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. 2 tool updatesv0.1.0
    • First observedinspect_database
    • First observedquery_shop
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

A3.8/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one handles database schema inspection (list tables/describe fields), the other executes analytical queries. There is no overlap in their functional boundaries, making selection unambiguous for an agent.

Naming Consistency5/5

Both tools follow a consistent verb_noun snake_case pattern ('inspect_database', 'query_shop'), using clear, descriptive verbs. Naming style is uniform and predictable.

Tool Count3/5

With only 2 tools, the server feels minimal for a shop analytics domain. While the query tool is versatile, a more granular set (e.g., get_customers, get_orders) might be expected. The count is borderline, not excessive but thin.

Completeness5/5

The tool surface fully covers the stated purpose: schema discovery via inspect_database and arbitrary read-only analytical queries via query_shop. There are no obvious gaps for a read-only analytics server, as the query tool can address any data retrieval need.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Shop Analytics MCP Server

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  • Read-Only SQLite Shop Database

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  • shop-mcp

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