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foundrole

FoundRole Jobs - AI Job Search & Application Tracker

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Save a job from another site to the tracker

tracker_add_external

Save any job posting found on the open web directly to your tracker using its URL and details, with no prior search required.

Instructions

Saves a job posting found anywhere on the open web into the user's tracker. For jobs that came from jobs_search results, tracker_add (which takes a job_id) is the right tool instead. A job seen elsewhere in the conversation needs no prior jobs_search call — its URL and details from the conversation are sufficient input.

url, company_name, title_name, location_name, and description identify the posting and are the only required fields. Every structured fact field (salary, dates, employment type, education, experience) is optional: a fact the source does not state is simply omitted (or null), and FoundRole's own extractors derive missing salary, employment, work-arrangement, education, experience, skills, benefits, and bonuses from the description. A save never waits on facts the source did not provide. The optional client_extraction object carries evidence-backed skills, technology, benefits, bonuses, seniority, industry, management, clearance, visa, and remote-scope labels when source excerpts for them exist; FoundRole validates and stores those labels separately.

Fields:

  • url: the job posting's direct URL (required; not a company homepage)

  • company_name: company name (required)

  • title_name: job title (required)

  • location_name: location, e.g. "New York, NY" (required)

  • description: the posting's description from the source result; a short summary is acceptable (required)

  • salary_min_value / salary_max_value: salary range bounds (numbers)

  • salary_value: a single salary figure when there is no range (number)

  • posted_at: ISO 8601 posting date

  • salary_currency: ISO 4217 currency code

  • salary_type: one of year, month, week, day, hour

  • employment_type: array of full_time, part_time, contractor, temporary, intern, volunteer, per_diem, other

  • work_location_type: one of on_site, remote, hybrid

  • education_requirements: array of no_requirements, high_school, associate_degree, bachelor_degree, professional_certificate, postgraduate_degree

  • experience_months: minimum required experience in months (number)

  • client_extraction: evidence-backed extraction object; fields without source evidence are omitted

  • status: initial tracking status (saved, applied, interviewing, offered, archived); defaults to "saved"

  • sub_status: sub-status within the main status: saved: interested, researching_company, preparing_application, ready_to_apply; applied: application_submitted, followed_up; interviewing: interview_scheduled, phone_screen, technical, onsite, final_round, pending_feedback; offered: negotiating, considering, offer_received, accepted; archived: ghosted, rejected_by_company, withdrawn_by_candidate, not_interested, employed_by_this_company, employed_by_another_company

  • notes: notes about the job

Returns the tracked job. Repeated saves return the existing tracked job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesDirect URL of the specific job posting; a company homepage is invalid
notesNoNotes about this job
statusNoInitial tracking status: saved, applied, interviewing, offered, archived
posted_atNoPosting date as ISO 8601, when the source states it
sub_statusNoSub-status within the main status, valid only for that status — saved: interested, researching_company, preparing_application, ready_to_apply; applied: application_submitted, followed_up; interviewing: interview_scheduled, phone_screen, technical, onsite, final_round, pending_feedback; offered: negotiating, considering, offer_received, accepted; archived: ghosted, rejected_by_company, withdrawn_by_candidate, not_interested, employed_by_this_company, employed_by_another_company
title_nameYesJob title from the posting
descriptionYesComplete posting text available in the conversation; a source summary is valid only when no fuller posting text is available
salary_typeNoSalary period: year, month, week, day, hour
company_nameYesCompany name from the posting
salary_valueNoSingle salary amount, when the posting gives one figure instead of a range
location_nameYesLocation text from the posting, including Remote when stated
employment_typeNoEmployment types: full_time, part_time, contractor, temporary, intern, volunteer, per_diem, other
salary_currencyNoISO 4217 salary currency code, when stated
salary_max_valueNoSalary range maximum, when stated
salary_min_valueNoSalary range minimum, when stated
client_extractionNoEvidence-backed facts extracted by the client model from the posting; non-null evidence is a short source excerpt rather than an inference. Fields absent from the source are omitted or null.
experience_monthsNoMinimum required experience in months, when stated
work_location_typeNoWork arrangement: on_site, remote, hybrid
education_requirementsNoEducation requirements: no_requirements, high_school, associate_degree, bachelor_degree, professional_certificate, postgraduate_degree

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hasMoreNo
totalCountNo
trackedJobNo
statusOrderNo
trackedJobsNo
trackerWebUrlNo
subStatusOrderNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.9

TDQS

A4.7/5.0
Behavior5/5

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

Annotations establish the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false), and the description adds important behavior: repeated saves return the existing tracked job, missing facts are omitted rather than blocking, extractors derive missing structured facts, and client_extraction labels are validated and stored separately. These are non-obvious operational traits that go well beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening usage guidance is strong and front-loaded, but the long bulleted enumeration of fields largely duplicates the schema descriptions, including enum values already present in the schema. For a 19-parameter tool some length is warranted, but the redundancy makes it less concise than it could be.

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?

Given the complex schema, existing output schema, and annotations, the description covers what an agent needs: required vs optional fields, default status, idempotent repeated saves, derivation of missing facts, and the evidence-backed client_extraction contract. Nothing critical is missing for correct invocation.

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 coverage is already 100%, so the baseline is 3, but the description adds useful semantics beyond the schema: it identifies the five required identifying fields, states that status defaults to 'saved', and explains that omitted facts are derived by extractors rather than requiring values. The field-by-field list mostly restates schema descriptions, limiting this to a 4.

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 and resource ('Saves a job posting ... into the user's tracker') and explicitly distinguishes itself from the sibling tracker_add by source and input type. An agent can tell when this tool applies without opening the schema.

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

Usage Guidelines5/5

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

Explicitly says to use tracker_add instead for jobs that came from jobs_search results, and clarifies that jobs seen elsewhere need no prior jobs_search call. This is precise when-to-use and when-not-to-use guidance tied to a named alternative.

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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