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SQLScope

SQLScope

by unfallenwill
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Sample rows

sample_rows

Inspect actual table values by fetching a limited sample of rows, revealing data shape and content before composing queries.

Instructions

Fetch the first N rows of a table (default 10) to see real values — useful for understanding data shape before writing queries.

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": {
      "table": "<table>"
    },
    "name": "sample_rows"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoNumber of rows to return (default 10)
tableYesTable name (optionally schema-qualified)
connectionNoNamed connection to use. Omit when only one connection is configured (or it is named "default").

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It correctly states the action and default, but it omits an important caveat: 'first N rows' has no guaranteed order without an ORDER BY clause. It also does not mention whether this is a simple LIMIT query or the potential performance implications on large tables.

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?

A single sentence that front-loads the action, includes the default, and closes with practical use-case guidance. Every word earns its place, with no redundancy or filler.

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?

For a simple preview tool, the description covers the essence: what it does, the default behavior, and the intended use case. It could be improved by noting that rows are unsorted unless specified and that this is effectively a SELECT * FROM table LIMIT n, but the existing text is sufficient for an agent to select and invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds no parameter-specific meaning beyond restating the default limit already present in the schema, which keeps it at the baseline.

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 a specific action ('Fetch the first N rows of a table') with a concrete resource and a default limit. It also explains the purpose ('to see real values... before writing queries'), which distinguishes it from siblings like describe_table or explain_query without needing to open the schema.

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?

It provides explicit context for when to use the tool: 'useful for understanding data shape before writing queries.' It does not explicitly name alternatives or exclusions, but the use case is clear enough relative to the sibling tools.

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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