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linkedin-local-mcp

linkedin-local-mcp

by condr-at
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

linkedin_list_comments

Retrieve comment threads for a LinkedIn post by providing its post URN. Use this to read existing comments and replies within granted permissions.

Instructions

Read comment threads for a LinkedIn post, subject to granted read permissions.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "post_urn": "<post_urn>"
    },
    "name": "linkedin_list_comments"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNo
post_urnYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

There are no annotations, so the description carries the behavioral burden. 'Read' conveys a non-mutating operation, and 'subject to granted read permissions' adds permission context. Yet it omits pagination behavior, error cases, or what happens when permissions are insufficient, leaving meaningful gaps.

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?

The description is one focused sentence with no filler. The verb and resource are front-loaded, and the permission caveat is appended without bloating the text.

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?

The tool is simple and has an output schema, but with no annotations and 0% parameter coverage, the description leaves the agent to infer count semantics and when to choose this over reply or publish. It is minimally viable but not thorough.

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%, and the description adds no explanation for post_urn or count. The schema titles are somewhat self-explanatory, but the description fails to compensate for the missing parameter documentation, so it stays below the baseline.

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 a clear verb ('Read') and resource ('comment threads for a LinkedIn post'), and the read-only nature distinguishes it from siblings like linkedin_publish_post and linkedin_reply_to_comment. It doesn't explicitly name any sibling, so it misses the full differentiation of a 5.

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?

Usage is implied: an agent would use this when it wants to read comments on a post. However, the description gives no explicit when-to-use or when-not-to-use guidance, and it does not mention the alternative siblings that perform related actions like publishing or replying.

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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