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BACH-AI-Tools

LinkedIn Data API MCP Server

profile_data_u0026_recommendations

Retrieve LinkedIn profile data along with given and received recommendations for any user. Input a username to get detailed professional information and endorsements.

Instructions

Get Profile Data, Given and Received Recommendations. 2 credits per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesExample value: ryanroslansky
Behavior2/5

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It only adds the credit cost (2 credits per call). No mention of error handling, data freshness, or other behaviors.

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?

The description is short and front-loaded with purpose. The credit note is useful but adds minimal overhead. Could be slightly more structured but acceptable.

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?

No output schema and the description does not detail what the returned data contains beyond a high-level list. Given the complexity of profile and recommendations data, this is insufficient for an agent to know how to use the output.

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% (username parameter with example). The description adds no extra meaning beyond the schema, so baseline 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 it gets profile data and both given/received recommendations. However, it doesn't differentiate from sibling tools like get_profile_data, get_given_recommendations, or get_received_recommendations, which each focus on a subset.

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 on when to use this combined tool versus individual siblings. The credit cost is mentioned but not how it compares to using separate 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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