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@guardbee/mcp-db-gateway

@guardbee/mcp-db-gateway

Official
by GuardBee
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TypeScript
Hybrid

describe_table

Retrieve column names and gateway masking policy for a table to determine which data the LLM can safely access.

Instructions

Get the column names and gateway masking policy for a table — helps the LLM understand what data it can access.

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": {
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYesTable name

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. 'Get' strongly implies a read-only metadata operation, and 'gateway masking policy' clarifies the nature of the returned information. However, it does not disclose permission behavior, error cases, or any side effects, leaving some ambiguity.

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 description is a single, efficient sentence that front-loads the action and resource, then adds the purpose. Every word contributes meaning, with no fluff or repetition.

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 simple one-parameter metadata tool with no output schema, the description adequately conveys what is returned (column names and gateway masking policy) and why it is useful. It could be more complete by explicitly addressing permissions or differentiating from siblings, but those are minor gaps given the tool's simplicity.

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 already fully documents the single 'table' parameter as 'Table name'. The description adds little beyond restating that the operation is for a table, so it does not significantly improve parameter understanding.

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 uses a specific verb ('Get') and clearly identifies the resource: column names and gateway masking policy for a table. It also states the intended purpose—helping the LLM understand data access—which distinguishes it from siblings like query_table and list_tables.

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 provides clear context for when to use the tool: when the LLM needs to understand what data it can access, including column names and masking policy. It does not explicitly name alternatives or state when not to use it, but the intended use case is evident.

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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