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schema-guard-mcp

schema-guard-mcp

by idk-arsh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_tables

Browse warehouse tables in a schema snapshot, filter by table or schema name, and confirm names before writing SQL to avoid invented or stale references.

Instructions

Tables in the warehouse snapshot. filter: part of a table or schema name.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault
filterNo

Output Schema

NameRequiredDescriptionDefault
resultYes

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?

With no annotations, the description carries the full behavioral burden, yet says nothing about read-only semantics, ordering, pagination, or whether unfiltered calls return everything. The read-only nature is only implied by the word 'list'/'Tables'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short front-loaded fragments are efficient but tip into under-specification rather than crisp conciseness. Nothing is wasted, but too little is said for the space available.

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?

An output schema exists, so return values need not be explained, and the single optional parameter is described. However, for a tool with no annotations, the absence of any read-only or scoping/behavioral context leaves the definition only minimally complete.

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 0%, so the description must compensate, and it partially does: 'filter: part of a table or schema name' explains what the single optional parameter matches, including that it spans both table and schema names. That is genuinely more than the bare schema provides, though it lacks format or matching-behavior specifics.

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 fragment 'Tables in the warehouse snapshot' identifies the resource but states no verb, relying on the tool name list_tables to convey the action. It does not distinguish this listing tool from siblings describe_table or search_columns, so an agent gets the gist but no explicit differentiation.

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 when-to-use guidance, no mention of alternatives (e.g., describe_table for a single table's detail, search_columns for column lookups), and no exclusions. The agent must infer placement among the siblings entirely on its own.

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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