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SQLite MCP Server

SQLite MCP Server

by stevefordev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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describe_table

Returns column names, types, and a few sample rows from a given SQLite table.

Instructions

Get the schema (columns) and a few sample rows from the specified table.

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": {
      "db_path": "<db_path>",
      "table_name": "<table_name>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
db_pathYes
table_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must cover behavior. It states outputs (schema and sample rows) but does not specify how many sample rows or any constraints (e.g., performance on large tables). The term 'a few' is vague but does not contradict any structured fields.

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 sentence that front-loads the main action and result. Every word is necessary; no fluff or redundancy.

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?

Given the tool's simplicity (2 parameters) and presence of an output schema, the description sufficiently explains the tool's purpose and output. However, it could clarify the number of sample rows or mention error handling, but these are minor gaps.

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 should add meaning. It mentions 'specified table' but does not explain what db_path or table_name represent, leaving their semantics to the schema (which only provides types and names).

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 retrieves schema and sample rows from a table, using specific verbs 'Get' and identifying the resource 'schema' and 'sample rows'. It distinguishes from siblings like execute_query (which runs arbitrary queries) and list_tables (which only lists names).

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 after selecting a table, but provides no explicit guidance on when to use it versus alternatives like get_schema_summary or execute_query. No when-not-to-use or context are given.

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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