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BACH-AI-Tools

Fresh Linkedin Profile Data MCP Server

Get Post Details

get_post_details

Fetch detailed data from a single LinkedIn post using its URN, including content, engagement metrics, and metadata.

Instructions

Scrape details of a single post based on its URN.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urnYesExample value: 7315779816467656705

Schema Changelog

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

  1. First observedv2.0.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It only says 'scrape details', which implies data retrieval but does not reveal read-only status, pagination, returned fields, rate limits, or whether the post must be publicly visible.

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?

A single sentence that efficiently communicates the core purpose and the input requirement without unnecessary words. The key constraint, 'based on its URN', is front-loaded and direct.

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

Completeness3/5

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

For a simple one-parameter lookup, the description is functionally usable, but with no annotations and no output schema, it leaves the return shape, possible failure reasons, and behavior beyond 'scrape' unstated.

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 coverage is 100% with an example value for urn. The description adds little semantic content beyond tying the URN to the post, so the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool scrapes details of one post using its URN, identifying both the action and the resource. It is distinct from search-focused siblings like search_posts, though it does not explicitly contrast against them.

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

Usage Guidelines3/5

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

The usage context is implied: an agent would call this when it has a post URN and needs that post's details. However, there are no explicit when-to-use, when-not-to-use, prerequisite, or alternative tool references.

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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