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@guardbee/mcp-db-gateway

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by GuardBee

query_table

Retrieve database rows with optional filters while automatically masking sensitive fields through the KVKK/GDPR gateway.

Instructions

Query rows from a database table with optional filters. All results pass through the KVKK/GDPR gateway — sensitive fields are automatically masked.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows to fetch before gateway limit is applied (default 50)
tableYesTable name to query
filterNoKey-value filter pairs, e.g. { status: 'active' }

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses one significant trait: all results pass through the KVKK/GDPR gateway with sensitive fields auto-masked. However, it omits other relevant behaviors such as the read-only nature of the query, permission requirements, error handling, or what happens to results when the gateway limit is reached.

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?

Two short sentences: the first states the core purpose and the second adds the essential gateway behavior. Purpose is front-loaded, with no filler or redundancy.

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?

The definition is adequate for basic invocation: required table parameter, optional filter, and limit are documented, and the masking behavior is disclosed. But there is no output schema, and the description does not explain the return shape of rows or how the gateway limit affects result truncation, leaving moderate gaps for a tool with no annotation safety net.

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 parameters are already documented: table, filter, and limit. The main description adds little beyond the schema; it echoes 'optional filters' and provides gateway-related context, but no new per-parameter 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 clearly states the operation ('Query rows') and resource ('from a database table'), with optional filters. This verb- and resource-level detail unambiguously separates it from siblings like list_tables, describe_table, and gateway_status.

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?

The description implies use for row retrieval but provides no explicit guidance on when to use it versus the listed siblings, and no exclusions or alternative conditions. There is no mention of when not to use query_table, leaving the agent to infer the boundary from the sibling names.

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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