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database-analytics-mcp

database-analytics-mcp

by khuyentran1401
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

execute_query

Run SQL queries directly against the connected SQLite database to retrieve, filter, and analyze data.

Instructions

Execute a SQL query on the connected database.

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>"
    },
    "name": "execute_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden, but it only states the action without disclosing potential side effects like data mutation, permission requirements, or error behavior. For a SQL execution tool, this is a significant transparency gap.

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, concise sentence with no redundant information. It is appropriately front-loaded, though brevity comes at the expense of critical context.

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?

Despite having an output schema, the description lacks essential usage context: no safety warnings, no connection prerequisites, no parameter guidance, and no mention of side effects. For a tool executing arbitrary SQL, 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?

The only parameter 'sql' has no schema description, and the tool description adds no detail about SQL dialect, query format, or examples. With 0% schema coverage, the description fails to compensate, leaving parameter semantics largely undefined.

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 it executes a SQL query on the connected database, using a specific verb and resource. It distinguishes itself from sibling tools like connect_db, list_tables, and export_to_csv without confusion.

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 provided on when to use this tool versus alternatives, nor any prerequisites such as ensuring a database connection is active. It does not mention whether it is appropriate for read-only vs. write operations.

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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