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BACH-AI-Tools

LinkedIn Data API MCP Server

get_given_recommendations

Scrape all recommendations given by a LinkedIn profile by paginating through results with a start offset incremented by 100 per request until the total count is reached.

Instructions

To scrape all recommendations from a profile, increase the start value to +100 for each request until you reach the total recommendations count. You can find the total recommendations count in the response

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesExample value: ryanroslansky
startNoExample value: 0
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It discloses the pagination requirement and that total count is in the response, but omits potential issues like invalid start values, rate limits, or empty responses.

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?

Single sentence, direct and informative. No wasted words, but could be slightly reorganized to front-load the purpose. Still, efficient for the content.

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?

Missing output schema and no description of the response structure beyond total count. For a scraping tool, an agent needs to know how to extract data from the response. Also, no comparison to similar siblings like 'get_received_recommendations'.

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

Parameters4/5

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

Schema coverage is 100% but only provides example values. The description adds meaning by explaining that 'start' is used for pagination with increments of 100, which is essential for usage. 'username' is self-explanatory.

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?

The description clearly states the action ('scrape all recommendations') and resource ('from a profile'). The tool name includes 'given', distinguishing it from the sibling 'get_received_recommendations', so the purpose is unambiguous.

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

Usage Guidelines5/5

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

Provides explicit pagination instructions: increment start by 100 until total count, which is found in the response. This tells the agent exactly how to iterate through all recommendations.

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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