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AI Applyd

by io.github.whateverneveranywhere

Optimize Resume with AI

aiapplyd_optimize_resume

Rewrite a resume so it passes ATS screening. Returns the rewritten resume, a projected ATS score, and an itemised summary of every change made. Pass job_description to tailor the rewrite to one specific posting, or omit it for general ATS optimization. Requires a connected AI Applyd account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resume_textYesFull text of the resume to optimize
job_descriptionNoFull text of the target job description (optional, omit for general ATS optimization)

Schema Changelog

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

  1. Changed1 schema field changed
    • removedInput schema / properties / locale
      Removed value: -{
-  "description": "Language for the human-readable parts of the answer: en, es, de, fr or pt-BR. Defaults to the language this connection was opened in. Machine fields (ids, URLs, scores) never change.",
-  "enum": [
-    "en",
-    "es",
-    "de",
-    "fr",
-    "pt-BR"
-  ],
-  "type": "string"
-}
  2. Changed2 schema fields changed
    • changedInput schema / properties / job_description / description
      Previous value: -"Full text of the target job description (optional -- omit for general ATS optimization)"New value: +"Full text of the target job description (optional, omit for general ATS optimization)"
    • addedInput schema / properties / locale
      Added value: +{
+  "description": "Language for the human-readable parts of the answer: en, es, de, fr or pt-BR. Defaults to the language this connection was opened in. Machine fields (ids, URLs, scores) never change.",
+  "enum": [
+    "en",
+    "es",
+    "de",
+    "fr",
+    "pt-BR"
+  ],
+  "type": "string"
+}
  3. Added

TDQS

A4.4/5.0
Behavior4/5

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

The description adds behavioral context beyond annotations: it reveals the three return components (rewritten resume, projected ATS score, itemized change summary) and the connected-account requirement. Annotations already indicate a non-read-only mutation, so the description meaningfully supplements them without contradiction.

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, all information-dense: main purpose and return values first, then optional-parameter behavior, then prerequisite. No filler or repetition of schema details.

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?

With no output schema, the description appropriately explains what the tool returns. It also covers the optional parameter behavior and requirement for a connected account. It could be slightly stronger by distinguishing itself from sibling tools, but nothing critical is missing for a correct call.

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 description coverage is 100%, so the schema already documents both parameters. The description adds value by explaining the conditional semantics of job_description: include it for a tailored rewrite or omit it for general optimization.

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 specifies a clear verb and resource: 'Rewrite a resume so it passes ATS screening.' It also differentiates from siblings like aiapplyd_score_resume by stating it returns a rewritten resume plus an itemized change summary, not just a score.

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 gives explicit conditional guidance: pass job_description to tailor to a specific posting, or omit it for general ATS optimization. It also states the account prerequisite. It does not explicitly name sibling alternatives, but the usage context is clear.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps cleanly to a distinct job-search action: analyze a posting, apply, build/optimize/score/translate resumes, generate cover letters/interview prep, search jobs, and update preferences. The only adjacent pair, score_resume and optimize_resume, is clearly separated by assessment versus rewrite.

Naming Consistency5/5

All tools share a consistent aiapplyd_ prefix and follow an actionable verb_noun pattern: analyze_job_description, generate_cover_letter, score_resume, update_job_preferences. No style mixing or vague generic names are present.

Tool Count5/5

Ten tools is well-scoped for a job-application assistant, covering the main user journeys without redundancy or bloat. Each tool has a clear, non-overlapping responsibility.

Completeness4/5

The tool surface covers the core lifecycle: preferences, job search, resume analysis and optimization, PDF generation, cover letters, auto-apply, and interview prep. Minor gaps exist around listing saved resumes/cover letters and tracking submitted applications, but these are workable via the connected account.

Resources