Read-only SQLite MCP

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sample_rows

Fetch a small set of example rows from a selected SQLite table to inspect data values and table contents before running queries or exporting CSV.

Instructions

Fetch a few example rows from a 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": {
      "database": "<database>",
      "table": "<table>"
    },
    "name": "sample_rows"
  }
}

Input Schema

Name	Required	Description
`limit`	No
`table`	Yes
`database`	Yes	Database alias or path given at server startup

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 9, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full burden and does little: 'fetch' implies read-only but never states it, and there is no mention of how many rows come back, whether sampling is random or first-N, cost on large tables, or error behavior for a missing table. The only behavioral signal is the vague phrase 'a few example rows'.

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?

A single short sentence with the verb and resource front-loaded and no filler. It is efficient, though the brevity edges into under-specification rather than tightness.

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 3-parameter tool with no annotations, no output schema, and two undocumented parameters, the description is too thin — it omits sampling semantics, read-only guarantees, and any routing versus read_query. An agent could invoke it, but would be guessing at the details.

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 only 33% — just 'database' is documented, while 'table' and 'limit' are bare. The description does not compensate: it never explains that 'limit' controls the sample size, its default of 10, or its 1-100 cap, nor what form the 'table' identifier takes relative to the database alias.

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 verb and resource ('Fetch a few example rows from a table'), so the agent knows this is a sampling/preview operation rather than a full read. It does not name or differentiate itself from the obvious sibling read_query, which also returns rows, so the distinction is left implicit.

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?

'Fetch a few example rows' implies a peek/preview use case, which points toward using it before writing a full query. However, nothing says when to prefer it over read_query, describe_table, or table_stats, and no prerequisites or exclusions are stated.

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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