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cloudwallker

PGScope MCP

by cloudwallker

query

Read-only

Execute a read-only SQL SELECT to inspect PostgreSQL data, returning up to 1,000 rows and 1 MiB before truncation.

Instructions

执行受限的单条只读 SELECT;最多 1000 行 / 1 MiB,结果可能截断。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYes
max_rowsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to restate that. It adds concrete behavioral details: a row limit of 1000 and a size limit of 1 MiB, with potential truncation. This is valuable context for agents deciding whether results may be incomplete. Does not contradict 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 concise sentence, front-loaded with the core purpose ('执行受限的单条只读 SELECT') followed by the key constraints. No wasted words; it efficiently conveys the essential behavioral limits. Slightly terse but appropriate for its scope.

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?

For a tool with 2 simple parameters and annotations covering read-only/destructive hints, the description is fairly complete in covering its own behavior with limits and truncation. However, it lacks usage guidance and parameter-level semantics, and since there is no output schema, agents are left to infer the exact return format. Given the complexity, more detail on parameters and expected results would improve completeness.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It does not explain the 'sql' parameter or the 'max_rows' parameter beyond hinting at a row limit indirectly. The description's limit reference could be tied to max_rows, but it doesn't explicitly state the purpose of each parameter. With simple parameter names, some meaning is implicit, but the description adds little value beyond schema types and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it executes a single read-only SELECT query, giving a specific verb and resource. It distinguishes itself from sibling tools like list_tables and describe_table by focusing on query execution, though it doesn't explicitly mention alternatives. The purpose is unambiguous and not a tautology.

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?

No guidance is provided on when to use this tool versus alternatives. Sibling tools exist (list_tables, describe_table, explain, diagnose), but the description gives no context for selection. It implies usage for running read-only queries, but there is no explicit when-to-use or when-not-to-use guidance.

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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