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mirenqinggege

postgres-mcp

explain_query

Read-only

Explains SQL query execution plans with cost estimates. Optionally runs query for real statistics or simulates hypothetical indexes to compare performance.

Instructions

Explains the execution plan for a SQL query, showing how the database will execute it and provides detailed cost estimates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesSQL query to explain
analyzeNoWhen True, actually runs the query to show real execution statistics instead of estimates. Takes longer but provides more accurate information.
hypothetical_indexesNoA list of hypothetical indexes to simulate. Each index must be a dictionary with these keys: - 'table': The table name to add the index to (e.g., 'users') - 'columns': List of column names to include in the index (e.g., ['email'] or ['last_name', 'first_name']) - 'using': Optional index method (default: 'btree', other options include 'hash', 'gist', etc.) Examples: [ {"table": "users", "columns": ["email"], "using": "btree"}, {"table": "orders", "columns": ["user_id", "created_at"]} ] If there is no hypothetical index, you can pass an empty list.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations indicate readOnlyHint=true, and the description confirms it explains plans without modification. It adds behavioral detail about the 'analyze' parameter actually running the query for real statistics, which is not in annotations.

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 tool description is a single, front-loaded sentence that efficiently conveys the core purpose. Parameter descriptions are appropriately detailed without redundancy.

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?

Given the presence of an output schema, the description does not need to detail return values. The combination of tool and parameter descriptions fully covers the tool's behavior and inputs.

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 100%, and the description adds significant value: explains that 'analyze' runs the query, and provides detailed format and examples for 'hypothetical_indexes' beyond the schema.

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: explaining execution plans for SQL queries with cost estimates. It distinguishes this from siblings like 'execute_sql' which runs queries, and 'analyze_query_indexes' which focuses on index analysis.

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?

The description implies usage when execution plan analysis is needed but does not explicitly state when to use over alternatives or when not to use. No mention of prerequisites or exclusions.

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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