DataSearcher MCP

Overview Schema Source Related Servers Discussions Admin

generate_sql

Generate DuckDB SQL from plain-language descriptions for any table. Optionally execute the generated query to get results directly.

Instructions

Генерация DuckDB SQL из описания на естественном языке (требует LLM).

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>",
      "description": "<description>"
    },
    "name": "generate_sql"
  }
}

Input Schema

Name	Required	Description	Default
`execute`	No
`table_name`	Yes
`description`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

C2.8/5.0

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 burden. It mentions that the tool requires an LLM, which hints at cost or latency, but it does not disclose the meaning or side effects of the execute parameter, whether generated SQL is run against the database, or any read/write implications.

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?

One short sentence with no filler or repetition. The core behavior is front-loaded, and the LLM requirement is a meaningful additional note. Every word contributes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even though an output schema exists, the definition omits essential operational context: what 'execute' does by default, whether a database must be attached first, and whether the result is SQL text, query results, or both. For a tool with three parameters and overlapping siblings, this is incomplete.

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%, so the description must compensate for undeclared parameter meanings. It clarifies that 'description' is a natural-language spec, but it says nothing about 'table_name' or 'execute', leaving two of three parameters semantically ambiguous.

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 a specific verb ('generation') and resource ('DuckDB SQL'), and identifies the input as a natural-language description. This clearly conveys the tool's core function, though it does not explicitly distinguish itself from closely related siblings like sql_query or query_explain.

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 about when to use generate_sql versus alternatives such as sql_query, transform_data, or query_explain. The description only says what the tool does, not the conditions or workflow context that should lead an agent to choose it.

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