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Secure Schema MCP

Secure Schema MCP

by Siddharth-coder13
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect_table

Retrieve the full structural layout of a database table, including column names, data types, nullability, primary keys, and foreign key relationships, without accessing row data.

Instructions

Exposes exact column names, types, nullability, primary keys, and foreign key relationships. Strictly constrained to structural layouts. Does not reveal data rows.

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_name": "<table_name>"
    },
    "name": "inspect_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatNoOutput format. Use compact to reduce LLM token usage; markdown for human-readable output.compact
schemaNoOptional schema/catalog namespace. Overrides DATABASE_SCHEMA only outside production.
table_nameYesThe exact case-sensitive name of the table to inspect.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description handles disclosure by stating it only returns structural metadata and no data rows. It could mention it is read-only or has no side effects, but the current text is still adequately transparent.

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 sentences, no fluff. The first sentence captures the essential purpose, and the second clarifies constraints. Very efficient.

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?

Given an output schema exists (not shown), the description does not need to detail return values. It covers the tool's scope adequately, though it could benefit from mentioning permissions or performance.

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 coverage is 100%, and the description adds no extra parameter info beyond what the input schema already provides. Baseline score of 3 is appropriate.

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 exposes column names, types, nullability, primary keys, and foreign key relationships. It is distinct from sibling tools like list_tables and schema_overview by specifying 'structural layouts' and explicitly noting it does not reveal data rows.

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 use for structural inspection only via 'Does not reveal data rows,' but lacks explicit when-to-use or when-not-to-use guidance, nor does it reference sibling tools for data queries.

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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