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mcp-bd-readonly

mcp-bd-readonly

by noemartinezoptima
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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describe_table

Inspect a table's columns and data types via read-only SQL DESCRIBE, so you can understand schema before querying.

Instructions

Columnas y tipos de una tabla (DESCRIBE).

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
tableYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only metadata operation via '(DESCRIBE)', but never states that it is non-mutating, what permissions are required, or whether it errors on missing tables. For a tool with zero annotation coverage this is a notable gap.

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?

A single terse sentence with no waste; the resource is front-loaded. It is efficient, though the brevity tips toward under-specification given the 0% schema coverage.

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 simple one-parameter read operation with no output schema, the description minimally covers intent and return content. It stops short of naming the table identifier format or distinguishing itself from list_tables, which an agent working among many SQL siblings would need.

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% and the single required parameter 'table' is undocumented in both schema and description. The word 'tabla' in the description loosely implies the subject, but no format (qualified name, schema.table) or identifier expectations are given.

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

Purpose3/5

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

The description states the tool's output (columns and types of a table) and mirrors the name's verb via '(DESCRIBE)', so the resource and result are identifiable. However, it does not differentiate from the sibling list_tables, leaving the agent to infer that this describes structure rather than enumerating names. Purpose is discernible but vague on scope.

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?

There is no guidance on when to use this versus list_tables or run_query. No preconditions, no mention of whether it works on views or requires prior database selection. The agent must guess from the name alone.

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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