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aiapplyd

AI Applyd

Search Jobs

aiapplyd_search_jobs
Read-onlyIdempotent

Retrieve AI-curated job matches by specifying job title, location, and remote preference. Returns match score, company, salary, and application URL.

Instructions

Return the user's AI-curated job matches, filtered by title, location, and remote preference. Each match carries a match score, company, location, salary, extracted skills, and the application URL. Read-only: it never changes which roles AI Applyd hunts for (use aiapplyd_update_job_preferences for that). Requires a connected AI Applyd account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationNoPreferred location (e.g. "San Francisco, CA", "New York", "Remote")
job_titleYesJob title to search for (e.g. "Software Engineer", "Product Manager")
remote_onlyNoIf true, only show remote-friendly positions

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.3.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds concrete context: it never changes which roles AI Applyd hunts for, requires a connected account, and spells out the match fields returned. This goes beyond the structured hints without contradicting them.

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?

Three sentences with no filler: purpose and filters first, then return contents, then safety/alternative/requirement. Every sentence carries useful signal.

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?

Although there is no output schema, the description lists the match fields (score, company, location, salary, skills, application URL), covers the prerequisite, and clarifies the read-only boundary. Nothing an agent needs to invoke or interpret the result is missing.

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% and each parameter already has a clear description. The tool description only echoes the filter dimensions (title, location, remote preference), so it adds no significant semantic detail beyond the schema.

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 names the specific action ('Return'), the resource ('user's AI-curated job matches'), and the filtering dimensions (title, location, remote preference). It also distinguishes itself from aiapplyd_update_job_preferences, so an agent can tell it apart from its sibling.

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

Usage Guidelines4/5

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

It states the read-only scope, explicitly routes preference changes to aiapplyd_update_job_preferences, and notes the connected-account prerequisite. It doesn't enumerate all when-not-to-use cases versus the broader sibling list, but for a search tool the key alternative is addressed.

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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