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List Outcome-Based Employer Pilot

list_products

Returns AI Dev Board's only agent-readable commercial offer: the employer-funded Verified Interview Pilot. Candidates and agents pay $0; job discovery and the evidence-backed application workflow are free. The pilot is billable only after an owner-approved program, a verified candidate-authorized application, and independent employer and candidate interview confirmation. It never changes organic ranking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries full transparency burden. It clearly states the tool is read-only regarding ranking ('It never changes organic ranking'), discloses the zero-cost nature for candidates/agents, and gives specific conditions under which billing occurs. This is substantial behavioral disclosure, going beyond a simple 'returns the product' statement.

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?

The description is three sentences long, front-loaded with the core function, and every sentence adds distinct value: purpose, pricing/free access, and side-effect/ranking guarantee. It is dense but not bloated, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema and no parameters, the description fully covers what an agent needs to know: what the product is, who pays, when billing applies, and that no side effects (ranking changes) occur. This is complete for a tool that simply returns a single product listing.

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 input schema has zero parameters, so the baseline is 4. The description adds meaning about the return value (the Verified Interview Pilot) and its terms, which is appropriate for a no-parameter tool. No param-level explanation is needed since there are none.

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 opens with a clear verb and resource: 'Returns AI Dev Board's only agent-readable commercial offer.' It names the specific product (the employer-funded Verified Interview Pilot) and distinguishes this tool from siblings like list_companies and list_tags by highlighting its unique role as the only agent-readable offer. This leaves no ambiguity about what the tool does.

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?

The description implies use when an agent needs to retrieve AI Dev Board's commercial offer, but it does not explicitly contrast with alternatives like quote_product or start_checkout. While it explains when the product becomes billable, there is no direct 'use this instead of X' guidance. The uniqueness statement ('only... commercial offer') hints at sole purpose, but leave selection criteria implicit.

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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