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Explain how matching works

explain_matching
Read-onlyIdempotent

Explains how PreShiftIQ computes matches: disqualification first, then measured-fit scoring by a payment-blind engine, with every match recorded immutably. Includes the live tier thresholds and the fit-index note. Use this when a user asks how matches are computed or what a match score means.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vertical_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tiersYes
summaryYes
how_it_works_urlYes
boundary_statementYes
additive_index_noteYes
immutable_record_noteYes
disqualification_firstYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so the safety profile is fully covered by structured data. The description adds valuable domain context (payment-blind engine, immutable match recording, live tier thresholds) which goes beyond annotations. However, it doesn't describe the response shape despite having an output schema, which is acceptable. A 3 is appropriate because annotations carry the behavioral burden and the description's additions are mostly domain flavor rather than invocation-critical behavior.

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?

Two sentences, front-loaded with the core mechanic explanation followed by precise usage guidance. No filler or repetition; every clause adds substance.

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 read-only informational tool with full annotation coverage and an output schema, the description is nearly complete. It covers what is explained and when to use it. The only missing piece is guidance on the vertical_id parameter, but with an output schema and rich annotations, the definition is sufficient for correct invocation.

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?

The single optional parameter vertical_id has 0% schema description coverage, meaning the schema provides no explanation of what the enum values mean or that the parameter is optional. The description doesn't mention vertical_id at all. However, with one parameter and zero required parameters, the baseline is 4. The description could have explained that omitting vertical_id gives general matching info, but this is a minor gap.

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?

Specific verb (explains) plus the precise resource (how PreShiftIQ computes matches), naming the exact mechanics covered: disqualification, measured-fit scoring, payment-blind engine, immutable records. Clearly distinguishable from siblings like my_matches (which returns matches) and describe_platform (general platform info).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit trigger: 'Use this when a user asks how matches are computed or what a match score means.' This directly routes the agent away from siblings that fetch actual match data. No ambiguity about when to pick this tool.

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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