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Shop MCP Server

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Python
Local

Shop MCP Server

Servidor MCP local para el análisis de la base de datos SQLite de una tienda en línea. El agente de IA invoca la herramienta MCP, el servidor convierte la consulta en SQL SELECT seguro, lee directamente shop.db y devuelve un resultado estructurado.

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

Características

  • conexión directa a SQLite sin HTTP ni servidor de base de datos independiente;

  • transporte MCP stdio;

  • detección de query_shop e inspect_database mediante tools/list;

  • ocho ejemplos de tareas en la descripción y en el JSON Schema de la herramienta;

  • texto plano, intent JSON o SELECT seguro preparado;

  • exclusión de los pedidos cancelled de todo el análisis;

  • paginación y limitación del resultado;

  • protección de solo lectura en varios niveles.

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

Instalación

Se requiere Python 3.11 o superior.

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

La base de datos preparada ya se encuentra en data/shop.db. Contiene 150 clientes, el campo customers.country está relleno en todos los clientes y 45 clientes pertenecen a Germany.

Re-preparación de la base de datos

La base de datos original no se modifica. El comando crea una nueva copia de trabajo y añade country TEXT NOT NULL con valores sintéticos reproducibles:

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

La distribución depende únicamente de customers.id, por lo que la re-preparación produce los mismos valores. Los países son datos de prueba y no describen la ubicación real de los clientes.

Configuración de la ruta

La ruta se determina en el siguiente orden:

  1. SHOP_DB_PATH;

  2. archivo JSON de SHOP_MCP_CONFIG o config/shop_mcp.json;

  3. data/shop.db relativo al directorio de trabajo.

Un ejemplo de variable de entorno se encuentra en .env.example, y la configuración del cliente MCP, en config/mcp.example.json. Reemplace /absolute/path/to/MCPDeveloper por la ruta absoluta al proyecto.

Ejecución

Normalmente, el servidor lo inicia el cliente MCP desde su configuración. Para iniciar el proceso manualmente:

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

stdout está reservado para MCP JSON-RPC. La diagnosis no se envía al flujo del protocolo.

Herramientas

inspect_database

Argumentos:

{"action": "list_tables"}

o:

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

query_shop

Argumentos:

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

Ejemplos declarados mediante tools/list:

  1. ¿Qué tablas hay en la base de datos y qué campos tienen?

  2. ¿Cuántos clientes hay de Alemania?

  3. ¿En qué país hay más clientes?

  4. ¿Qué cliente gastó más? Devuelve el nombre, el email y el importe total.

  5. Muestra el top-5 de productos por cantidad vendida e ingresos.

  6. Muestra el top-3 de categorías por ingresos.

  7. ¿Qué ingresos hubo en 2025?

  8. ¿Qué cliente realizó más pedidos?

Se puede pasar un intent estructurado:

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

O una consulta preparada:

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

Reglas de negocio

  • orders.status = 'cancelled' no participa en las métricas.

  • Los gastos de los clientes y los ingresos anuales se calculan según orders.total_amount.

  • Los ingresos de productos y categorías se calculan como quantity * unit_price.

  • El intervalo del año es semiabierto: desde el 1 de enero inclusive hasta el 1 de enero del año siguiente exclusivo.

  • En la base de datos suministrada, todos los pedidos pertenecen a 2026, por lo que los ingresos de 2025 son 0.

Seguridad

El servidor abre SQLite mediante mode=ro, activa query_only, establece el authorizer de SQLite, prohíbe múltiples sentencias, DML, DDL, ATTACH, PRAGMA que modifiquen, la carga de extensiones y limita el tiempo de ejecución.

Cualquier intento de ejecutar una operación distinta de SELECT devuelve una respuesta exacta:

Не доступный вариант запроса

Los errores internos de SQLite, el stack trace y las rutas locales no se devuelven al cliente.

Verificación

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

Las pruebas incluyen la ejecución real del servidor MCP como subprocess, la conexión de ClientSession mediante stdio, tools/list, la invocación de ambas herramientas, escenarios analíticos e intentos de modificación de la base de datos.

La especificación completa aprobada se encuentra en 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

    bogdaamn
    F
    license
    A
    quality
    C
    maintenance
    Enables AI agents to answer analytical questions about an online store's SQLite database through specialized read-only tools, without any risk of modifying the underlying data.
    8
    -

  • Read-Only SQLite Shop Database

    alimbux
    F
    license
    A
    quality
    C
    maintenance
    Enables AI agents to safely inspect and query an SQLite e-commerce database with tools for listing tables, describing schemas, and running read-only SQL queries while blocking destructive operations.
    4
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  • shop-db

    aleksei-antipin
    F
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    A
    quality
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    maintenance
    Enables AI agents to read-only query an online store's SQLite database, listing tables, inspecting schemas, and running SELECT queries over customers, products, orders, and order items.
    3
    -

  • shop-mcp

    AndrewKonst
    F
    license
    Not graded
    quality
    C
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    Enables AI agents to read-only analyze a SQLite e-commerce database, exploring schema and running analytical SQL queries over stdio.
    -