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Income Factory Toll Fabric

linkedin_jobs

Rank public LinkedIn job listings by freshness, competition, relevance and application accessibility.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It states the tool ranks listings but does not explain the output format, how the ranking is computed, whether any data is returned, or what 'application accessibility' entails. This is comparable to the update_drive example, which also lacked permissions and reversibility details.

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 a single sentence with no wasted words, and the core action 'Rank' is front-loaded. Every phrase earns its place by defining the resource and the ranking dimensions.

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?

Given the lack of an output schema and no annotations, the description should explain return values and behavior. It does not state what the tool returns (e.g., a list, scores, or job details) nor any limitations. For a no-parameter tool, the description is the only source of information, and it is incomplete on output and use context.

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 tool has zero parameters, so the baseline is 4. The description adds no parameter-specific info, but that is unnecessary because there are no parameters. The ranking criteria in the description effectively serve as the tool's purpose rather than parameter semantics.

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 clearly states the tool ranks public LinkedIn job listings and specifies the ranking criteria (freshness, competition, relevance, application accessibility). This is a specific verb and resource, but it does not differentiate from the sibling tool 'hiring_demand', which could also relate to job market analysis.

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?

The description offers no guidance on when to use this tool versus alternatives. There is no mention of desired use cases, exclusions, or relationships to siblings like hiring_demand or discover_income_factory.

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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