AIGigLab MCP Server

get_linkedin_post_history

Retrieve recent LinkedIn posts generated by AI. Use this to access your AI-generated post history for review or repurposing.

Instructions

Retrieve the history of recent LinkedIn posts generated by the AI.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_linkedin_post_history"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Number of recent posts to retrieve

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.3.0Jul 15, 2026

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations, the description carries full burden. It only states the tool retrieves history, without disclosing whether it is read-only, rate limits, or what exactly constitutes 'history' (e.g., all posts vs. only AI-generated). Minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words, but it is overly brief and lacks important details that could be added without harming conciseness. It is not front-loaded with critical context.

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?

Without annotations or output schema, the description should provide richer context about what the history includes (e.g., content, dates, engagement). It leaves significant gaps for the agent to infer.

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 one parameter whose description already explains it. The tool description does not add extra meaning beyond the schema, 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 retrieves history of LinkedIn posts generated by AI, which is a specific verb and resource. It is distinguishable from sibling tools like get_linkedin_config or trigger_linkedin_post_now, though no explicit differentiation is provided.

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 given on when to use this tool versus alternatives, nor any context about prerequisites or exclusions. The usage is implied but not clarified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.