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BACH-AI-Tools

Fresh Linkedin Profile Data MCP Server

Search Jobs

search_jobs

Search LinkedIn for job postings and retrieve matching results from the search output.

Instructions

To scrape all results from each search, change the parameter \

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.0.0

TDQS

D1.3/5.0
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only hints at a scraping/pagination behavior ('scrape all results from each search') but says nothing about side effects, rate limits, required setup, return value, or failure modes. The fragment is not a meaningful behavioral description.

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

Conciseness2/5

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

The text is short, but it is truncated mid-sentence ('change the parameter "') and does not convey a complete idea. This is under-specification, not effective conciseness; brevity without completeness has no value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a job-search tool amid 40+ sibling tools, an effective description must at least state what the tool does, how it relates to search_jobs_v2, and what inputs or outputs are involved. This description provides none of that, and there is no output schema or annotation to fill the gap.

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

Parameters1/5

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

The input schema has zero parameters, yet the description instructs the agent to 'change the parameter' without naming it or explaining its values. This directly contradicts the empty schema and makes parameter understanding actively misleading rather than helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description is a sentence fragment: 'To scrape all results from each search, change the parameter "' — it hints at scraping but never states what the tool does, what resource it operates on, or what output to expect. It is truncated and leaves the agent to infer the actual purpose from the name/title alone.

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

Usage Guidelines1/5

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

No when-to-use or when-not-to-use guidance is provided. The description does not mention alternatives like search_jobs_v2, get_search_results, or check_search_status, nor any condition that would select this tool over them. An agent gets no decision context.

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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