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BACH-AI-Tools

Fresh Linkedin Profile Data MCP Server

Get Profiles Posts

get_profiles_posts

Fetch posts, comments, or reactions from a LinkedIn profile using its URL. Paginate through results to collect engagement data for analysis.

Instructions

2 credits per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoPossible values: posts: to scrape posts from tab Posts -- posts or posts reshared by the person comments: to scrape posts from tab Comments -- posts the person commented reactions: to scrape posts from tab Reactions -- posts the person reacted
startNoUse this param to fetch posts of the next result page: 0 for page 1, 50 for page 2, etc.0
linkedin_urlYesExample value: https://www.linkedin.com/in/williamhgates/
pagination_tokenNoRequired when fetching the next result page. Please use the token from the result of your previous call.

Schema Changelog

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

  1. First observedv2.0.0

TDQS

C2/5.0
Behavior1/5

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

With no annotations provided, the description must disclose behavioral context, but it only mentions cost. No information is given about data returned, pagination behavior beyond schema hints, rate limits, or any side effects. The cost note is operationally relevant but not behavioral transparency.

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 description is short, but this is under-specification rather than conciseness. It contains only a cost note and fails to carry any functional information that would help an agent select or use the tool.

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

Completeness2/5

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

For a tool with 4 parameters, no annotations, and no output schema, the description provides almost no context. While the schema fully documents parameters, the missing purpose, return structure, and usage context make the tool under-specified for an agent.

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 every parameter is already documented. The description adds nothing beyond the schema, making a baseline score of 3 appropriate.

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 provides no functional statement, only '2 credits per call.' The tool's purpose must be inferred from its name/title, which is ambiguous and does not distinguish it from siblings like get_posts_reactions or get_companys_posts.

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?

There is no guidance on when to use this tool versus alternatives. The description does not specify contexts, exclusions, or conditions that would route an agent to this particular endpoint instead of related post-scraping tools.

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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