claw-mcp-toolkit

social_engagement_analyzer

Analyze social media engagement patterns to optimize posting strategies based on follower count, likes, and replies data.

Instructions

Analyze engagement patterns for optimal posting strategy

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": "social_engagement_analyzer"
  }
}

Input Schema

Name	Required	Description
`followers`	No	Your follower count
`avg_likes`	No	Average likes per post
`avg_replies`	No	Average replies per post

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Mar 23, 2026

TDQS

C2.5/5.0

Behavior2/5

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

With no annotations provided, the description carries the full transparency burden but fails to disclose key behavioral traits. It doesn't clarify that this tool requires manually provided statistics (followers, avg_likes) rather than fetching historical data, doesn't describe the output format (recommendations vs. raw metrics), and omits safety/read-only status.

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?

While the single-sentence structure avoids verbosity, the description suffers from under-specification that limits its utility. Every sentence should earn its place by conveying specific actionable information; this description is too vague to fully justify its inclusion without additional context from other fields.

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?

Given the presence of three related sibling tools ('social_content_calendar', 'social_generate_tweet', 'social_hashtag_research') and a 100%-documented but parameter-heavy schema with no output schema, the description should explain the tool's specific role in the workflow and what analysis it produces. It currently lacks this contextual completeness.

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 description coverage is 100%, establishing a baseline score of 3. The description adds no specific guidance on parameter semantics, expected data formats, or contextual meaning beyond what the schema already provides (e.g., 'Your follower count'). The baseline applies since the schema fully documents the three optional parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description provides a general verb ('Analyze') and resource ('engagement patterns') but remains vague about the analysis method and scope. It implies the output is strategic recommendations ('optimal posting strategy'), but doesn't clearly distinguish from sibling tools like 'social_hashtag_research' or 'social_content_calendar' in terms of when to use which analytical tool.

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?

The description provides no guidance on when to use this tool versus alternative methods or sibling tools. It does not mention prerequisites (e.g., needing engagement statistics ready) or exclusion criteria (e.g., not suitable for real-time data).

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