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BACH-AI-Tools

Fresh Linkedin Profile Data MCP Server

Get Open Profile Status

get_open_profile_status

Check a LinkedIn profile's public visibility status by URL. Determine if a profile is open and accessible for viewing or data retrieval.

Instructions

1 credit per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkedin_urlYesExample value: https://www.linkedin.com/in/williamhgates/

Schema Changelog

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

  1. First observedv2.0.0

TDQS

D1.6/5.0
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 mentions the credit cost and reveals nothing about what the call does, what 'open profile status' means, what response shape to expect, or any side effects or access requirements.

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 this is under-specification, not effective conciseness. The single sentence about credits is not front-loaded tool semantics; it is the only sentence and contains no useful operational guidance.

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?

There is no output schema and no annotation coverage, so the description must explain return values and behavior. It does neither. For a status-checking tool with a single input and no output schema, an agent lacks essential information about what statuses exist or what the response will contain.

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 linkedin_url property only gives an example value rather than a real semantic explanation, but the tool name and example still convey the expected input format. The description adds nothing beyond the schema.

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

Purpose1/5

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

The description is only '**1 credit per call.**' It never states what the tool does, identifies the resource, or provides a verb. The title and name imply a purpose, but the description itself is semantically empty and does not differentiate from the very similar sibling get_open_to_work_status.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus alternatives such as get_open_to_work_status or enrich_lead. An agent cannot infer prerequisites, typical use cases, or exclusions from the description.

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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