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SQLite Explorer MCP

SQLite Explorer MCP

by msharsha565-create
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_schema_ddl

Retrieve CREATE TABLE statements for the full SQLite database to inspect all tables and columns before writing a valid SELECT query.

Instructions

CREATE TABLE statements for the whole database. Read this first, then write a SELECT for run_query: every table and column you use must appear here.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_schema_ddl"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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 discloses the return content (DDL text) and implicitly that the call is a read, but says nothing about output size or cost despite covering the whole database, nor about schema freshness or failure modes.

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?

Two short sentences, front-loaded with what the tool returns and followed by the workflow constraint. Every sentence earns its place; nothing is repeated from structured fields.

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

Completeness4/5

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

An output schema exists, so return values need not be explained, and the description still names the output form (CREATE TABLE statements). With no parameters and no annotations, the only remaining gap is guidance for very large databases where dumping the whole DDL may be impractical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. The description correctly implies no inputs are needed and points the agent to run_query for the actual parameterized work.

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?

States a specific resource — CREATE TABLE statements for the entire database — which distinguishes it from the per-table siblings list_tables and describe_table by scope. It does not explicitly name those siblings, so the differentiation is implied rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit sequencing ('Read this first, then write a SELECT for run_query') and states a hard constraint that every table and column used must appear here. It does not say when to prefer list_tables or describe_table instead, which would be useful for large schemas.

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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