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oliverhruby

LinkedIn MCP Server

by oliverhruby

linkedin_get

Fetch data from LinkedIn API endpoints via authenticated GET requests. Specify a path and optional query parameters to retrieve profiles, organizations, and other resources.

Instructions

Raw authenticated GET helper.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
query_jsonNo{}
api_versionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations, the description must carry the full burden of behavioral disclosure. It only notes that the tool is 'authenticated', implying an auth token is required, but it does not disclose rate limits, error handling, pagination, response format, or whether the GET is read-only. This is minimal behavioral context for a tool that could hit arbitrary endpoints.

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 extremely concise at five words, with the key differentiator 'raw' and the HTTP verb 'GET' front-loaded. Every word contributes to the core purpose, but the word 'helper' is somewhat vague and could be replaced with more specific guidance. Overall it is appropriately sized for its limited scope.

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?

Despite the presence of an output schema, the description leaves crucial gaps: it does not state the base URL, how path should be formatted, how query_json maps to request parameters, or how authentication is supplied. For a generic raw helper with three parameters and no annotations, this is incomplete and would require an agent to guess or inspect other tools.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for parameter meaning. It does not explain 'path', 'query_json', or 'api_version' beyond what their names imply. The 'GET' context hints that path is an endpoint and query_json may be query parameters, but no details are provided about formats, defaults, or how the API version is used.

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 states 'Raw authenticated GET helper', which clearly identifies the tool as a low-level helper that performs an authenticated GET request. This differentiates it from the specific sibling tools like list_campaigns or get_post, which are higher-level wrappers. However, it does not mention the LinkedIn API resource scope explicitly, relying on the 'path' parameter to imply that.

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 provided about when to use this raw helper versus the many specific sibling tools. The word 'raw' implicitly suggests using it when no higher-level tool exists, but this is not stated. There are no exclusions, prerequisites, or alternative routing instructions.

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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