DataSearcher MCP

by MarkIvor

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

get_schema

Retrieve table structure including column names, data types, row count, and a preview of data.

Instructions

Структура таблицы: колонки, типы, число строк, превью.

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": "get_schema"
  }
}

Input Schema

Name	Required	Description	Default
`table_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

B3.1/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses what the result covers (columns, types, row count, preview), implying a read-only metadata operation, but does not explicitly state side-effect-free behavior, access requirements, or limitations.

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 compact phrase that front-loads the essential output components without filler. Every word contributes to understanding what the tool returns.

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 tool is simple and has an output schema, so return details may be covered there. However, the description omits usage context and parameter clarification, leaving an agent to infer how to invoke it correctly.

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 description does not mention table_name or how it is used. The parameter name is self-explanatory, but the description adds no semantic value beyond the bare schema definition.

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

Purpose4/5

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

The description states the tool returns table structure components: columns, types, row count, and preview. This makes the core purpose clear and distinguishes it from value-oriented siblings like sample_data or profile_data, though it lacks an explicit verb.

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?

No guidance is given about when to use this tool versus alternatives like refresh_schema, profile_data, or sample_data. The description implies schema inspection but does not state conditions, exclusions, or preferred sibling routes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.