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ATSPrep

Fetch a job post

fetch_job_post
Read-onlyIdempotent

Read a public job posting from its link and return the title, company, location and full description as plain text, ready for match_job, tailor_resume, keyword_scan or interview_questions. Reads LinkedIn, Greenhouse, Lever, Ashby, SmartRecruiters, Workable, Workday and most company careers pages. Some sites block reading; then ask the user to paste the text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe job posting's public link (https).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint and destructiveHint=false, so safety is covered. The description adds genuinely non-obvious behavior: the list of supported ATS platforms, the fact that the result is plain text, and the failure mode where sites block reading and the user must paste text manually.

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 short sentences, front-loaded with the action and returned fields, then supported sites, then the failure fallback. Every sentence carries information an agent would otherwise have to discover by trial and error.

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?

With no output schema, the description compensates by naming the returned fields and their format (plain text). Combined with the supported-site list and the blocking fallback, an agent has everything needed to call it and to handle the likely error path.

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?

Only one parameter and schema description coverage is 100% – the schema already documents 'url' as a public https link with a max length. The description's 'from its link' adds no syntax or format detail beyond that, so the baseline 3 applies.

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?

States a specific verb (read) and resource (public job posting from its link) and enumerates the exact fields returned: title, company, location, full description. It also names the sibling tools that consume its output (match_job, tailor_resume, keyword_scan, interview_questions), so an agent can place it precisely in the workflow.

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?

Gives a clear upstream/downstream context by naming the four sibling tools this feeds, and provides an explicit when-not case with a fallback: 'Some sites block reading; then ask the user to paste the text.' It stops short of stating exclusions versus alternatives for the same job (there are none in the sibling list), so it is strong but not fully exhaustive.

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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