AI Dev Jobs

Compile a Job-Specific Resume Draft

compile_job_specific_resume

Compile a $0 candidate-controlled ATS-oriented plain-text Markdown resume for one active job using only candidate-verified evidence. Without identity, returns a transient non-persisted preview that cannot be approved or submitted. With a verified candidate key, saves an immutable draft hash plus a machine-readable candidate_review handoff and requires the agent to pause for explicit human review. Never submits or authorizes an application.

Input Schema

TableJSON Schema

Name	Required	Description
`job_id`	Yes	Active job UUID or slug returned by search_jobs or get_job.
`candidate_skills`	No	Candidate-declared skills. The draft lists a skill as evidence-backed only when verified evidence supports it.
`evidence_bullets`	Yes	Candidate-owned factual evidence. Raw text is processed transiently and is not retained in MCP analytics.

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations provided, the description fully discloses behavior: it explains the transient preview without identity, the persistent draft hash with a verified key, the requirement to pause for human review, and the prohibition on submission. This is exceptionally transparent about side effects and conditions.

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, front-loaded with the primary purpose, then covering conditional behavior and limitations. Every sentence adds essential information without redundancy. It is concise given the tool's complexity.

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?

No output schema exists, but the description explains the return behavior: transient preview without identity, immutable draft hash plus candidate_review handoff with a verified key. It also mentions the pause for human review. For a tool with authentication-dependent behavior, the description is complete.

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 description coverage is 100%, so the schema already explains the parameters. The description adds some semantic context (e.g., 'candidate-verified evidence' maps to the verified flag), but it does not provide per-parameter syntax or format details. It meets the baseline for covered schemas.

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 uses a specific verb ('Compile') and identifies the resource ('job-specific resume') with clear constraints: candidate-controlled, ATS-oriented, plain-text Markdown, one active job, candidate-verified evidence only. It effectively distinguishes itself from siblings like apply_to_job and approve_compiled_application by stating it only compiles a draft and never submits.

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?

The description provides clear context: it's for compiling a draft from verified evidence, not for submission. It explicitly states 'Never submits or authorizes an application,' which is a when-not. However, it does not name specific alternative tools or explicit exclusions, so it falls short of a 5.

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

A3.9/5.0

Disambiguation4/5

Most tools have clear, distinct purposes (e.g., search_jobs vs get_job vs get_similar_jobs). The main ambiguity is between match_jobs and analyze_application_readiness, both of which assess candidate-job fit, though one ranks multiple jobs and the other evaluates readiness for a specific role. The application flow steps are well-separated.

Naming Consistency5/5

All tool names consistently follow the snake_case verb_noun pattern (e.g., get_company, list_companies, apply_to_job). Even longer names like analyze_application_readiness and compile_job_specific_resume adhere to this convention, with no mixed casing or inconsistent verb styles.

Tool Count3/5

With 20 tools, the server is on the heavier side for a job board, though the breadth of features (search, company info, salary, application, interview tracking, and product sales) partially justifies the count. Some redundancy exists (e.g., get_trending_companies vs list_companies, get_stats vs get_salary_data), making the set feel slightly bloated.

Completeness3/5

The toolset covers core job search and application workflows, but there is no way to list or track submitted applications, view application status, or withdraw an application. Post-application features are limited to interview outcomes, leaving obvious lifecycle gaps for a candidate-facing job platform.

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