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LinkedIn: Get job posting budget

linkedin_get_job_posting_budget
Read-onlyIdempotent

Get budget/pricing information for a LinkedIn Classic job posting owned by the connected account. Resolve job_id with linkedin_list_job_postings first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesExact Classic job ID returned by linkedin_list_job_postings; not title or URL.
account_idNoOptional Nilyo connection ID (unipile_account_id from list_connected_accounts). Omit when the user has one account for this provider. When several exist, Nilyo never guesses: list them (display name, identifier, provider user ID), choose the one the user named or ask, and pass its ID here.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds only the prerequisite resolution step, which is more usage context than behavioral trait disclosure. It does not contradict annotations and does not need to repeat them, but it also does not add extra behavioral detail beyond what annotations provide.

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 with zero filler. The primary purpose is front-loaded in the first sentence, and the second sentence gives the only necessary usage note. Every word contributes value.

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 simple read-only getter with full schema coverage and annotations, the description is nearly complete. It includes the key prerequisite for job_id and the owned-by-connected-account scoping. The only minor gap is not describing the return value, but with no output schema and the tool name indicating budget info, this is acceptable for the complexity level.

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?

Schema description coverage is 100%, so the baseline is 3. The schema already explains job_id as 'Exact Classic job ID returned by linkedin_list_job_postings; not title or URL' and provides detailed instructions for account_id. The description's mention of resolving job_id with linkedin_list_job_postings is redundant with the schema, adding no new parameter meaning.

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 states a specific verb+resource: 'Get budget/pricing information for a LinkedIn Classic job posting.' It also names the prerequisite tool linkedin_list_job_postings, which differentiates it from sibling tools like linkedin_get_job_posting. An agent can clearly identify what this tool does and how it relates to nearby functions.

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?

The description gives explicit prerequisite guidance: 'Resolve job_id with linkedin_list_job_postings first.' This tells agents when to use this tool (after listing job postings) and establishes the correct input source. It does not explicitly mention when not to use it, but the name and purpose make the use case clear enough.

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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