Jobero job search

Start a job search

start_job_search

Spend one credit to run a full search: 7 roles matched to the profile, each with a tailored CV and cover letter returned as markdown by get_search_status and emailed as PDFs. Requires an API key. With no credit left it returns an x402 PaymentRequired; retry the same call with the signed payment in _meta["x402/payment"] to pay in USDC and run immediately.

Input Schema

TableJSON Schema

Name	Required	Description
`cv_json`	No	The job seeker's existing CV as structured data, in the JSON Resume schema (jsonresume.org): basics, work, education, skills. Prefer this when you hold the CV as data — you do not need to render a file. Supply either cv_json or cv_base64
`cv_type`	No	Format of cv_base64: "pdf", "docx" or "txt". Ignored when cv_json is supplied
`profile`	Yes	Who the job seeker is and what they want. Every field shapes matching, so guessing is worse than asking
`cv_base64`	No	The job seeker's existing CV as a file, base64 encoded. PDF, DOCX or plain text, up to 10MB. Use cv_json instead if you have the CV as data rather than a file

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Present only on failure, e.g. "unauthorized" or "payment_required"
`run_id`	No	Pass this to get_search_status to poll
`status`	No	Run state at the moment of starting, normally "running"
`message`	No	What happens next and roughly when, or what to do about the error
`credits_remaining`	No	Credits left for this product after the spend

TDQS

A3.8/5.0

Behavior4/5

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

The description adds significant behavioral context beyond the annotations: it consumes a credit, emails PDFs, requires an API key, and returns an x402 PaymentRequired when no credit is left, with a retry payment mechanism. This complements the annotations (readOnlyHint false, openWorldHint true) and explains side effects clearly.

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 two sentences, front-loaded with the core action ('Spend one credit to run a full search'), and efficiently conveys cost, output, delivery method, authentication, and error handling. Every phrase contributes information, with no filler.

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?

For a tool with four parameters (including a complex nested profile object) and an output schema, the description covers the essential aspects: what it does, cost, delivery, API key requirement, and payment retry. It doesn't detail the response structure but defers to get_search_status, which is acceptable. The description is complete enough for an agent to use the tool correctly in most scenarios.

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 already provides detailed descriptions for all four parameters (cv_json, cv_type, profile, cv_base64), covering semantics thoroughly. The description adds a small amount of value by mentioning '7 roles matched to the profile' and 'tailored CV and cover letter,' which ties the parameters to expected outcomes, but this is marginal. Baseline 3 is appropriate given 100% schema coverage.

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's function: 'run a full search: 7 roles matched to the profile, each with a tailored CV and cover letter.' It uses a specific verb ('run') and identifies the resource (profile, roles). It doesn't explicitly contrast with sibling tools like quick_job_search, but the phrase 'full search' hints at differentiation.

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

Usage Guidelines3/5

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

The description provides context on when to use the tool: it requires one credit and an API key, and it describes the payment flow when credits are exhausted. However, it does not explicitly state when to prefer this tool over alternatives like quick_job_search or get_search_status. The guidance is implied through the 'full search' wording but not directly stated.

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

A4.1/5.0

Disambiguation5/5

Each tool serves a distinct purpose: pricing, search initiation (quick vs full), status retrieval, and agent registration. The two search tools are explicitly differentiated by speed and output size, so there is no ambiguity.

Naming Consistency4/5

Most tools follow a verb_noun pattern (get_pricing, get_search_status, register_agent, start_job_search), but 'quick_job_search' uses an adjective_noun construction. The naming is consistent in style (lowercase snake_case) and generally predictable, with only one minor deviation.

Tool Count5/5

Five tools is well-scoped for a focused job search service, covering core operations without unnecessary redundancy. Each tool earns its place.

Completeness4/5

The tool set covers the essential workflow: registration, pricing, starting searches, and retrieving results. Minor gaps such as profile management or search history are not critical for the core purpose, making it reasonably complete.

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