@takeokunn/metabase-mcp

generate_sql

Convert natural language questions into SQL queries using AI, requiring a database ID and Metabase Pro subscription.

Instructions

[Requires Metabase Pro] Generate SQL from a natural language question using AI

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": {
      "input": "<input>"
    },
    "name": "generate_sql"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 7, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It mentions AI usage and a licensing requirement, but fails to disclose whether the tool is read-only, what the output format is (e.g., SQL string), or if it executes the query. Behavioral traits beyond the basic premise are lacking.

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?

The description is a single sentence with a prerequisite tag, making it concise and front-loaded. However, it could be slightly more structured (e.g., separate the prerequisite) but is still efficient.

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?

Given the nested input schema and lack of output schema/annotations, the description is incomplete. It does not specify what the tool returns (e.g., SQL query string), whether it executes, or dependencies like user permissions. The prerequisite is noted, but essential behavioral and output details are missing.

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?

Context reports 0% schema description coverage, yet sub-properties of 'input' have descriptions ('Natural language question to convert to SQL' and 'ID of the database to query'). The tool description adds no additional parameter meaning beyond these; it does not explain the nested structure or clarify the role of the top-level 'input' parameter.

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 'Generate SQL from a natural language question using AI', specifying the verb (generate), resource (SQL), and input type (natural language question). It distinguishes this tool from all sibling tools, none of which offer NL-to-SQL conversion.

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 notes a prerequisite ('Requires Metabase Pro'), but provides no guidance on when to use this tool versus alternatives like execute_query or run_dashcard_query. Usage context is implied but not explicitly differentiated.

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

Deploy Server