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Match two tables

match_tables

Match the rows of two tables on one or more pairs of key columns, like a join, and store the matches as JSON for 24 hours. left and right are lists of row objects; keys lists pairs such as {"left":"id","right":"customer_id"}. No API key, no account and no sign up: call it directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysYes
leftYes
rightYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false and idempotentHint=false, but give no hint about persistence. The description adds genuinely new behavior: matches are stored as JSON and kept for 24 hours, plus the no-auth requirement. It stops short of saying how the stored result is later retrieved, but the TTL disclosure is valuable context beyond the annotations.

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?

Purpose is front-loaded, followed by the parameter format and the storage caveat. The closing 'No API key, no account and no sign up: call it directly' is somewhat redundant but adds operational value. Overall tight with minimal waste.

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 3-required-param tool with no output schema and 0% schema coverage, the description covers purpose, input shape, TTL, and auth. It would be fully complete if it explained how the stored 24-hour result can be read back, since no output schema exists to document the return value.

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?

Schema coverage is 0%, so the description carries the full burden. It defines left/right as lists of row objects and explains keys as pairs with a concrete example ({"left":"id","right":"customer_id"}), which compensates well. It omits the maxItems=100 limit and does not fully describe the keys object shape, keeping it from a 5.

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?

States a specific verb and resource ('match the rows of two tables on pairs of key columns, like a join') and names the storage side effect. An agent immediately understands this is a table-join operation, which is clearly distinguishable from the surrounding utility siblings.

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?

'call it directly' with 'no API key, no account and no sign up' tells the agent it can be invoked without setup, which is useful operating context. However, there is no explicit when-to-use vs when-not guidance or named alternative, so usage remains implied rather than 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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