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dbecho

dbecho

by ginkida
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

compare

Execute a SELECT query across multiple databases and view results side by side for comparison.

Instructions

Run the same SQL query across multiple databases and compare results side by side.

Args: sql: SQL query to execute on each database (must be SELECT). databases: List of database names to compare. If omitted, runs on all databases.

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": "compare"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYes
databasesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

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 full burden. It adds critical context beyond the schema: the SQL must be SELECT. It also implies the comparison is side-by-side. Missing details on error handling or result format, but output schema likely covers returns.

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 short and front-loaded with the main purpose. The Args format is not typical JSON but still clear. Every sentence adds value. Could be slightly more formal but overall concise.

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 the tool's complexity (comparison across databases) and presence of an output schema, the description covers the essential behavior: it runs a SELECT query and compares results. It does not detail output structure, but the output schema handles that.

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 coverage is 0%, so the description must explain parameters. It does so thoroughly: 'sql' is a SELECT query, 'databases' is an optional list (default all). This adds significant meaning beyond the schema's type-only definitions.

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 action: 'Run the same SQL query across multiple databases and compare results side by side.' It uses a specific verb ('Run', 'compare') and resource ('SQL query', 'databases'), and distinguishes from siblings like 'query' (single database) and 'analyze' (analysis).

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 explains when to use the tool (to compare across databases) and notes that omitting 'databases' runs on all databases. However, it does not explicitly state when not to use it (e.g., for single-database queries, use 'query'), though this is implied.

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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