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shop-db

shop-db

by aleksei-antipin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Run a read-only SQL query

query
Read-onlyIdempotent

Run read-only SQL queries against the shop database to retrieve matching rows as JSON, with pagination and SQL aggregation for totals, top-N, or revenue.

Instructions

Execute a read-only SQL query against the shop database and return the matching rows as JSON objects.

Returns: columns (list of names), rows (list of objects), row_count, truncated (true when more rows exist beyond the limit) and, when truncated, next_offset for fetching the next page.

Use list_tables / describe_table first to learn the schema. For questions like totals, top-N or revenue, aggregate in SQL (SUM, COUNT, GROUP BY, ORDER BY, LIMIT) instead of fetching raw rows.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sql": "<sql>"
    },
    "name": "query"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYesA single SQLite SELECT statement (WITH ... SELECT is allowed). JOINs, GROUP BY and aggregate functions are supported. Any data-modifying statement is rejected — the database is read-only.
limitNoMaximum rows to return, 1-500 (default 50). Prefer aggregating in SQL over fetching many raw rows.
offsetNoRows to skip for pagination (default 0). Use next_offset from a truncated result.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value beyond annotations by disclosing the exact return shape (columns, rows, row_count, truncated, next_offset), truncation behavior, and pagination. It does not contradict the annotations.

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 a single well-organized paragraph that front-loads the purpose before the return format and usage guidance. Every sentence earns its place — no filler or repetition of the title. Slightly longer than strictly necessary but efficiently structured.

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 SQL-execution tool of moderate complexity with rich annotations and full schema coverage, the description is nearly complete. It explains return values and pagination even though an output schema exists (somewhat redundant per rubric, but reinforced here). Nothing critical is missing for correct invocation.

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%, so the schema fully documents sql, limit, and offset. The description adds marginal value by reinforcing preference for SQL aggregation over fetching many raw rows and mentioning next_offset for pagination, but the baseline 3 is appropriate since the schema carries the parameter documentation burden.

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 states a specific verb ('Execute') and resource ('read-only SQL query against the shop database'), with the intent to return matching rows as JSON objects. It clearly distinguishes this from the sibling tools list_tables and describe_table, which are about schema discovery rather than query execution.

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 gives explicit when-to-use guidance: use list_tables/describe_table first to learn the schema, and tells the agent to aggregate in SQL (SUM, COUNT, GROUP BY, ORDER BY, LIMIT) for totals, top-N, or revenue questions instead of fetching raw rows. This directly routes the agent to the right tool and strategy.

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

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