de en es ja ko ru zh

shop

Name: shop
Author: DrZeD-13

by DrZeD-13

Overview Schema Source Related Servers Discussions Admin

Python

Local

読み取り専用 MCP ストアサーバー

Python MCP サーバー（stdio 経由）で、既存のオンラインストア SQLite データベース（data/shop.db）を読み取ります。ファイルは読み取り専用で開かれ、変更を伴う SQL は拒否され、実行されません。

インストール

Python 3.11 以上:

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Related MCP server: Shop SQLite MCP

起動

プロジェクトのルートから（データベースへのパスはデフォルトで ./data/shop.db）:

python -m shop_mcp
# или
shop-mcp

別のデータベースパス:

SHOP_DB_PATH=~/my/test-mcp-db/data/shop.db python -m shop_mcp

このプロセスは MCP 経由で stdin/stdout を介して通信します。MCP クライアントが接続されるまで、このターミナルにコマンドを入力しないでください。

Cursor での MCP 設定

mcpServers エントリを追加します（Cursor Settings → MCP、またはプロジェクト内の .cursor/mcp.json）。トランスポートは stdio です。

例の中のパス ~/my/test-mcp-db は、このリポジトリを指しています。サーバーは SHOP_DB_PATH を自身で展開します（~ → ホームディレクトリ）。Cursor が command / cwd で ~ を展開しない場合は、そこに /Users/<вы>/my/test-mcp-db/... を指定してください。

ローカル (venv)

{
  "mcpServers": {
    "shop": {
      "command": "~/my/test-mcp-db/.venv/bin/python",
      "args": ["-m", "shop_mcp"],
      "cwd": "~/my/test-mcp-db",
      "env": {
        "SHOP_DB_PATH": "~/my/test-mcp-db/data/shop.db"
      }
    }
  }
}

pip install -e . の後、コンソールスクリプトの方が便利な場合:

{
  "mcpServers": {
    "shop": {
      "command": "~/my/test-mcp-db/.venv/bin/shop-mcp",
      "cwd": "~/my/test-mcp-db",
      "env": {
        "SHOP_DB_PATH": "~/my/test-mcp-db/data/shop.db"
      }
    }
  }
}

Docker

イメージを一度ビルドします:

docker build -t shop-mcp .

{
  "mcpServers": {
    "shop": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "~/my/test-mcp-db/data/shop.db:/data/shop.db:ro",
        "-e",
        "SHOP_DB_PATH=/data/shop.db",
        "shop-mcp"
      ]
    }
  }
}

-i フラグは stdin io を接続したままにするために必須です。shop.db のみをマウントしてください。コンテナがホスト上のファイルを上書きしないようにしてください。

ツール

Tool	説明
`list_tables`	ユーザーテーブルとその役割の簡単な説明（`sqlite_sequence` は表示されません）
`describe_table`	実際のカラム、型、PK/FK
`execute_readonly_sql`	単一の `SELECT` / `WITH` / `EXPLAIN`； `limit`（デフォルト 100、最大 1000）と `offset`
`shop_analytics`	手動 SQL なしで年間ランキングと売上を取得

合計、数量、売上は order_items.quantity * order_items.unit_price として計算され、ステータスが cancelled の注文は除外されます。暦年は strftime('%Y',orders.order_date) から取得されます。

セキュリティ

SQLite URI file:<abs>?mode=ro
コメントは除去され、クエリチェーン（SELECT 1; DELETE ...）は拒否されます
INSERT / UPDATE / DELETE / DROP / ALTER / CREATE / ATTACH / PRAGMA / … → 明示的な「許可されていません」エラー
SQLite の構文エラーと不明なカラムは、構造化して返されます。プロセスは終了しません

評価に関する注記

customers には country カラムがありません。 これは describe_table で確認できます。地理情報を勝手に推定してはいけません。SELECT country FROM customers は SQLite エラーを返します。
現在のダンプの注文日は 2026 年です。 2025 年の売上は 0 になることがあります。これはフィルターの故障ではなく、正しい結果です。

テスト

pytest

テストは、data/shop.db を一時ファイルにコピーし、拒否された書き込み後もコピーのハッシュが変更されないことを検証します。

実行済みクエリの例

Available Tools

4 tools

describe_tableA

Describe columns, types, primary keys, and foreign keys for one table.

Use before writing joins. Returns actual SQLite metadata only — never fabricates fields such as country. Parameters: table (name from list_tables). Returns {table, columns: [{name, type, primary_key, ...}], foreign_keys}.

ParametersJSON Schema

Name	Required	Description	Default
`table`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds an important guarantee: 'Returns actual SQLite metadata only — never fabricates fields such as country', which communicates trustworthiness and avoids hallucinated results. It also describes the return shape. It does not explicitly state that the operation is read-only, but 'returns metadata' strongly conveys that.

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 compact and front-loaded: purpose first, then when to use it, then parameter provenance, then output shape. Every sentence earns its place, with no filler or repeated structural information beyond what is useful.

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 (one string parameter, an output schema exists, no annotations), and the description gives enough to invoke it correctly: what it returns, where the parameter value comes from, and why to use it before joins. It could add explicit note about error behavior for invalid table names, but that is not essential for a straightforward describe/metadata tool.

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 only a type string and a title 'table', so the description adds crucial meaning: table is 'the name from list_tables'. This tells the agent where valid values come from, effectively linking describe_table to list_tables. That is exactly the kind of semantic enrichment the schema lacks.

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 ('Describe') and precise resources (columns, types, primary keys, foreign keys) for 'one table', clearly distinguishing it from list_tables (listing) and execute_readonly_sql (querying). The phrase 'one table' prevents confusion with table-listing siblings, and the mention of metadata-only output further clarifies its exact scope.

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 says 'Use before writing joins', which gives a clear context and timing for using this tool. It also states that the table name should come from list_tables, effectively providing a prerequisite. However, it does not explicitly name alternatives or say when not to use this tool (e.g., when listing tables or running arbitrary SQL).

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

execute_readonly_sqlA

Run a single read-only SQL statement against shop.db.

Allowed: one SELECT, WITH (CTE), or EXPLAIN. Writes and DDL (INSERT, UPDATE, DELETE, REPLACE, DROP, ALTER, CREATE, TRUNCATE, ATTACH, VACUUM, REINDEX, PRAGMA) are rejected with an explicit not-allowed error. Stacked statements (semicolons) are rejected. Prefer shop_analytics for rankings and yearly revenue.

Pagination: default limit 100, maximum 1000 (error if exceeded), offset default 0. Returns {columns, rows, truncated, row_count, next_offset} or {error, sqlite_error?}. The database is opened read-only; refused mutations leave the file unchanged.

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes
`limit`	No
`offset`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A5/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses that the database is opened read-only, that refused mutations leave the file unchanged, states that stacked statements are rejected, and explains pagination defaults and the response format. This goes well beyond what the schema or annotations alone would reveal.

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 dense but structured: it front-loads the primary action, then lists allowed/rejected statements, pagination behavior, and return contract. Every sentence contributes a distinct constraint with no filler.

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 3-parameter tool with an output schema and no annotations, the description covers the SQL grammar constraints, safety guarantees, pagination limits, error behaviors, and guidance for choosing a sibling tool. Nothing essential to correctly invoking it is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description compensates by defining the 'sql' parameter in terms of allowed statement types and by providing concrete limits for 'limit' (default 100, max 1000) and 'offset' (default 0). All three parameters gain usable meaning.

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 opens with an exact verb and resource: 'Run a single read-only SQL statement against shop.db.' It further specifies the allowed statement forms (SELECT, WITH/CTE, EXPLAIN) and explicitly distinguishes itself from the shop_analytics sibling for rankings/revenue, making the tool's purpose unambiguous.

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 says to 'Prefer shop_analytics for rankings and yearly revenue,' naming an alternative and the scenario where it should be used. It also sets clear boundaries for this tool by enumerating what is allowed (read-only SELECT, WITH, EXPLAIN) and rejecting writes/DDL and stacked statements.

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

list_tablesA

List user tables in shop.db with a one-line purpose for each.

Use this first to discover the real schema. Internal SQLite tables such as sqlite_sequence are omitted. Does not invent columns (customers has no country). Returns {tables: [{name, purpose}, ...]}.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations present, the description carries the behavioral burden well: it mentions internal SQLite tables are omitted and that columns are not invented. This guards against false expectations, though it does not address side effects or access requirements, which are minor for a read-only listing 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?

Each sentence earns its place: purpose, usage timing, exclusions, anti-invention behavior, and return shape. The description is front-loaded and compact.

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 zero-parameter listing tool with an output schema, this description is complete. It tells the agent when to use it, what it will not include, and what the response looks like.

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 tool has zero parameters, so detailed parameter descriptions are unnecessary. The description usefully documents the output contract instead: a table list of names and purposes.

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 names a specific verb and resource: 'List user tables in shop.db', and clarifies the output is a one-line purpose per table. It also differentiates itself by explicitly targeting user tables rather than internal ones, which separates it from schema/query tools.

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?

'Use this first to discover the real schema' gives a clear and explicit usage signal. It does not, however, explicitly state when it should not be used or name alternatives like describe_table or execute_readonly_sql.

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

shop_analyticsA

Shop aggregations without hand-written SQL.

Operations: top_customers_by_spend (name, email, total_amount; default limit 5), customer_most_orders (name, email, order_count), top_products (product_name, units_sold, revenue; default limit 5), top_categories (category, revenue; default limit 3), revenue_by_year (requires year). Spend/units/revenue use quantity * unit_price and exclude cancelled orders. Year is taken from order_date.

ParametersJSON Schema

Name	Required	Description	Default
`year`	No
`limit`	No
`operation`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations present, the description carries the full behavioral burden. It reveals important behavior beyond a naive reading: spend, units, and revenue are computed as quantity * unit_price, cancelled orders are excluded, and year is taken from order_date. It also implies a read-only aggregation nature, though it doesn't explicitly state side-effect safety, cancellation behavior, or error conditions.

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 compact, structured, and front-loaded with the main purpose. It lists operations in a scannable format, then adds only the necessary computation caveats. Every sentence carries substantive information with no filler.

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?

Given an output schema exists, the description doesn't need to formally define return fields, and it does cover operation options, defaults, a special requirement, and calculation semantics. Remaining minor gaps are ambiguous behavior such as how customer_most_orders handles the limit parameter and what happens when an invalid combination of operation and year is given, but these do not block correct use in common cases.

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

Parameters5/5

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

The schema provides 0% description coverage, so the description must explain the parameters, and it does: operation values map to concrete aggregations, limit has per-operation defaults, year is required for revenue_by_year, and all calculation semantics are explained. This goes well beyond the bare enum/schema and provides enough detail to invoke each operation correctly.

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 that this tool provides shop aggregations without hand-written SQL, and it enumerates exactly five named operations with their output fields. This distinguishes it from the SQL-oriented sibling tools (execute_readonly_sql) while making the tool's exact scope immediately evident.

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 phrase 'without hand-written SQL' effectively tells the agent to use this tool for shop aggregation reporting instead of writing SQL, and it names concrete operations such as top_customers_by_spend and revenue_by_year. It provides explicit per-operation constraints like the year requirement and default limits, though it does not explicitly state when to use a sibling tool like describe_table or execute_readonly_sql.

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.

4 tool updatesv0.1.0Aug 23, 2026
- First observeddescribe_table
- First observedexecute_readonly_sql
- First observedlist_tables
- First observedshop_analytics

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

A4.5/5.0

Scored 2026-08-23 07:30 across 4 tools

Disambiguation4/5

The schema-discovery tools are clearly separated from the querying/analytics tools. There is some overlap between execute_readonly_sql and shop_analytics, but the descriptions explicitly steer agents toward shop_analytics for rankings and revenue, reducing confusion.

Naming Consistency4/5

Three tools follow a clear verb-first snake_case pattern: list_tables, describe_table, execute_readonly_sql. shop_analytics breaks that pattern as a nouny resource name, though it is still understandable and not chaotic.

Tool Count5/5

Four tools is a well-scoped set for a read-only shop database. Each tool fills a distinct role: schema discovery, table metadata, raw SQL execution, and prebuilt analytics, with no redundancy.

Completeness5/5

For a read-only database exploration server, the workflow is complete: list the tables, describe one, run arbitrary safe SQL, and get common analytics. There are no missing mutations or write operations because the server is explicitly read-only.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Related MCP Connectors

Dataassist-IOOAuth
io.github.DataAssistIO
Query your org's data in natural language — read-only MCP access to SQL, NoSQL, files & warehouses.
Search Databases
2
dataOAuth
co.thinair
PostgreSQL, MySQL, and SQL Server in one session. 26 read-only MCP tools for AI agents.
Databases Developer Tools
2
AI2SQLOAuth
io.ai2sql
Text-to-SQL MCP server: read-only queries on PostgreSQL, MySQL and SQL Server with your real schema
Databases Developer Tools
1
UsageMax public observability A
io.github.SYMBaiEX
Read-only MCP for AI usage profiles, leaderboards, stats, and docs; no writes or private data.
AI & Machine Learning Observability
41

Related MCP Servers

AI Sales Query Agent MCP
Databases Search
Kusubhavani
F
license
Not graded
quality
C
maintenance
Enables natural-language sales queries against a SQLite database, generating and executing read-only SQL through a secure MCP server with table listing, schema description, and query execution.
Updated 2 months ago (2026-08-08 08:54 UTC)
-
Shop SQLite MCP
E-commerce & Retail Autonomous Agents
mixe-deep
F
license
Not graded
quality
C
maintenance
Enables safe, read-only analysis of an online store's SQLite database, providing schema introspection, restricted SELECT queries, and specialized analytics tools through MCP.
Updated 2 months ago (2026-08-21 19:58 UTC)
-
shop-db
Databases E-commerce & Retail
alser15
F
license
Not graded
quality
C
maintenance
This MCP server lets an AI agent securely connect to a read-only SQLite store database, inspect its tables and schema, and run analytical SQL queries without modifying any data.
Updated 2 months ago (2026-08-23 09:03 UTC)
-
shop-mcp
Databases E-commerce & Retail
AndrewKonst
F
license
Not graded
quality
C
maintenance
Enables AI agents to read-only analyze a SQLite e-commerce database, exploring schema and running analytical SQL queries over stdio.
Updated 2 months ago (2026-08-24 06:24 UTC)
-