AI Dev Jobs

Search AI/ML Jobs

search_jobs

Search curated AI/ML engineering roles for free. Filter by tags (e.g. llm, pytorch), workplace (remote/hybrid/onsite), global remote eligibility, experience level, salary range, or keyword. After selecting a role, candidates may use compile_job_specific_resume for a $0 transient ATS-oriented preview without identity; a verified key is required only to save an approval-bound artifact. The employer outcome pilot never affects organic results.

Input Schema

TableJSON Schema

Name	Required	Description
`tags`	No	Tag filter (e.g. ['llm', 'pytorch']). Returns jobs with ANY of these tags.
`level`	No	Experience level
`limit`	No	Max results (default 10, max 25)
`query`	No	Keyword query — matches title, description, company name, and tags
`company`	No	Company slug (e.g. 'openai', 'anthropic') — returns only that company's jobs
`job_type`	No	Employment type
`location`	No	Location substring (e.g. 'San Francisco')
`workplace`	No	Filter by workplace type
`salary_min`	No	Minimum salary in USD. Matches if job's max salary >= this value (range overlap).
`global_remote`	No	Only roles explicitly open to candidates worldwide. Ambiguous or region-restricted remote jobs are excluded.
`posted_within_days`	No	Only return jobs posted within the last N days. Useful for polling agents that check for new listings — try 1 for daily, 7 for weekly.

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool is free, results are curated, and the employer outcome pilot never affects organic results. It also explains identity and key requirements for saving approval-bound artifacts, offering important context about privacy and authorization.

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: purpose and cost, filter list, and downstream behavior. Every sentence carries meaningful information and the main action is front-loaded. No redundant or vague wording.

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?

For a tool with 11 optional parameters and no output schema, the description is impressively complete. It covers purpose, filter scope, cost, privacy/identity, and result integrity (pilot). While it lacks an explicit return format, the extensive behavioral disclosures make it sufficient for an agent to decide and invoke correctly.

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?

The schema covers 100% of parameters with detailed descriptions (e.g., salary_min range overlap, posted_within_days polling suggestion). The description adds high-level filter categories like tags, workplace, and keyword, but does not add per-parameter details beyond the schema, so the baseline of 3 is appropriate.

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 clearly states 'Search curated AI/ML engineering roles for free' with a specific verb and resource. It distinguishes from sibling tools like match_jobs and get_similar_jobs by focusing on search and filtering rather than matching or similarity.

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?

Provides clear context on when to use the tool—to search and filter job listings across multiple dimensions (tags, workplace, level, salary). It references compile_job_specific_resume as a downstream step, but does not explicitly state when not to use it or name alternatives like match_jobs for matching use cases.

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.

Resources

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