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Maestro Career Studio

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by seinun-ai
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Save Extracted Job

store_extracted_jd
Idempotent

Save an already-extracted job description as structured JSON in Maestro Career Studio to track and score job postings without another LLM call.

Instructions

Store a job description the caller already extracted into JSON (the backend makes no LLM call).

extracted_json must match the app's JobExtraction schema: company, title,
role_category, level, employment_type, work_mode, city/state/country,
location_raw, salary_min/max, salary_period,
work_authorization: "sponsorship_available"|"no_sponsorship"|"citizen_or_gc_required"|"unstated",
opt_accepted: "yes"|"stem_opt_ok"|"no"|"unstated",
years_experience_min/max, skills[{skill_name, skill_category,
requirement_level: "required"|"preferred"|"mentioned"}],
responsibilities[], qualifications[]. ANY other work_authorization or
opt_accepted value silently normalizes to "unstated" with no error.
source is "user" (default) or "agent"; source="agent" marks agent-hunted jobs,
which need an open proposal before the apply helpers act on them. A job
stored with source="user" is not proposal-gated: the server skips the
open-proposal check for any non-agent source. Returns the stored job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNouser
raw_textNo
source_urlNo
extracted_jsonYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

A4.7/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing a silent-failure behavior ('ANY other work_authorization or opt_accepted value silently normalizes to "unstated" with no error'), the proposal-gating consequence of source='agent' versus 'user', and that no LLM call is made on the backend. These are consequences an agent could not infer from readOnlyHint=false / idempotentHint=true / destructiveHint=false.

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

Conciseness4/5

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

It is long, but the length is largely forced by the 0% schema coverage and the purpose constraint is front-loaded. The mid-paragraph enum dump is dense but each clause is load-bearing; only the trailing 'Returns the stored job' is near-redundant.

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 write tool with no output schema, no parameter descriptions, and a nested object parameter, the description supplies the schema contract, the enum fallback semantics, the gating behavior, and the return statement. Nothing material is missing for an agent to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden, and it does: it enumerates the extracted_json fields (company, title, salary_min/max, skills[{skill_name, skill_category, requirement_level}], etc.) plus exact enum vocabularies for work_authorization, opt_accepted, and requirement_level, and explains source semantics. Only raw_text and source_url go unmentioned, which are minor given their self-evident names and null defaults.

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+resource ('Store a job description') and immediately bounds scope with 'the caller already extracted into JSON (the backend makes no LLM call)', which distinguishes it from any extraction-oriented sibling. An agent knows exactly what this does and what it does not do.

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 real usage context: source='agent' jobs are proposal-gated before apply helpers act on them, while source='user' skips the open-proposal check. It does not explicitly compare against alternative storage/ingest paths (e.g. kb_ingest_resume, sync_now), so the when-to-use-vs-alternative framing is implied rather than 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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