database-analytics-mcp

by khuyentran1401

Overview Schema Source Related Servers Discussions Admin

Python

Local

export_to_csv

Export the results of a SQL query to a CSV file, making database data available for external tools and analysis.

Instructions

Execute a SQL query and export results to CSV file

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": {
      "sql": "<sql>",
      "filename": "<filename>"
    },
    "name": "export_to_csv"
  }
}

Input Schema

Name	Required	Description	Default
`sql`	Yes
`filename`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 10, 2026

TDQS

B3.3/5.0

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 of behavioral disclosure. It only states the basic action and does not mention potential side effects like file overwriting, permissions, or dependencies on an existing database connection.

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, front-loaded sentence that directly states the tool's purpose without any superfluous words. It is optimally concise.

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?

For a simple tool, the description is minimal but lacks important context such as file overwrite behavior, whether a database connection is required, and what the output schema entails. The presence of an output schema reduces some burden, but the description still does not adequately guide the agent on prerequisites or side effects.

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?

With 0% schema description coverage, the description must compensate, but it only indirectly references the parameters ('SQL query' and 'CSV file') without adding meaningful detail about the expected format, constraints, or relationship between sql and filename. The parameter names are already suggestive, so the description adds minimal value.

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 executes a SQL query and exports the results to a CSV file. This specific verb+resource distinguishes it from siblings like execute_query, which presumably returns results but does not export to a file.

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 usage for exporting query results to CSV, but it does not explicitly mention when to use this tool versus alternatives such as execute_query. No exclusions or alternative references are provided.

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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