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yugui923

db-connect-mcp

by yugui923

sample_data

Retrieve a random sample of rows from any table to preview data structure and values. Ideal for exploratory analysis without full table scans.

Instructions

Sample data from a table efficiently

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYesTable name
schemaNoSchema name (optional)
limitNoNumber of rows to sample (default: 100)
Behavior2/5

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

Without annotations, the description bears full responsibility for behavioral disclosure. It only mentions 'efficiently' which is vague, and omits details like whether it returns rows, uses random sampling, or requires any permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise in one sentence, but lacks structure and fails to front-load key details like return type or typical use case.

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?

The description is insufficient given no output schema and sibling tools. It should explain what the output is (e.g., rows of data) and how sampling works.

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 coverage is 100%, so the baseline is 3. The description does not add any additional meaning beyond the schema's parameter descriptions.

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?

The description states the tool samples data from a table, which is clear and specific. However, it does not distinguish from siblings like describe_table which focuses on metadata.

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 on when to use this tool versus alternatives like execute_query or describe_table. No scenarios or exclusions provided.

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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