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Salesbot - B2B outreach LinkedIn MCP Server with CRM and email

search_job_postings

Read-only

Use to find companies that are hiring (a buying signal); it returns job postings, not people. Search LinkedIn job postings by keywords, location, seniority, job type, and more. Uses the connected LinkedIn account and the same protected Classic provider-search budget as people discovery. Respect quota_protection and retry_after_seconds; job search must never be used to bypass a people-search cooldown.

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (1-50, default 10)
job_typeNoJob type filter. Optional.
keywordsYesJob title or keywords to search for (e.g. 'Sales Manager', 'frontend developer')
locationNoHuman-readable location (e.g. 'Praha', 'Brno', 'Berlin'). Resolved to LinkedIn geo ID automatically.
presenceNoWorkplace type filter. Optional.
seniorityNoExperience level filter. Optional.
easy_applyNoFilter to Easy Apply jobs only. Optional.
locationIdNoRaw LinkedIn location ID. Use this OR location.
date_postedNoOnly jobs posted within this many days (e.g. 7 for last week). Optional.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/non-destructive, and the description adds substantial operational context: it uses the connected LinkedIn account, shares the protected Classic provider-search budget with people discovery, and requires honoring quota_protection and retry_after_seconds. These rate-limit and auth/budget details 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.

Conciseness4/5

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

Front-loaded with the key disambiguation before getting into mechanics, and three sentences carry real information (scope, budget, quota). The final sentence is dense with two concepts but earns its place. Minor compression possible.

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 read-only search with no output schema, the description covers scope, return nature (postings, not people), and the quota/cooldown constraints an agent must respect. Adequate; a note on pagination or result shape would fully close the gap, but annotations and schema carry the rest.

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 all 9 parameters are already documented in the schema. The description references the filter dimensions (keywords, location, seniority, job type) but adds no format or default details beyond what the schema provides, so 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+resource (search LinkedIn job postings) and immediately distinguishes it from the adjacent people-search tools by clarifying 'it returns job postings, not people.' An agent can place it relative to search_linkedin_people and get_job_posting_details without opening a schema.

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 use case ('find companies that are hiring (a buying signal)') and an exclusion ('not people'), plus an operational rule against bypassing a people-search cooldown. It stops short of naming the sibling alternative (e.g. search_linkedin_people) explicitly, but the context is strong.

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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