claw-mcp-toolkit

social_hashtag_research

Research trending hashtags for any topic using public data to identify relevant social media tags for content optimization and audience engagement.

Instructions

Research trending hashtags for a topic (uses public data)

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": {
      "topic": "<topic>"
    },
    "name": "social_hashtag_research"
  }
}

Input Schema

Name	Required	Description	Default
`topic`	Yes	Topic to research hashtags for

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Mar 23, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description bears full responsibility for behavioral disclosure. It mentions 'uses public data' (indicating no authentication barrier) but fails to describe output format, data freshness, rate limits, or what 'research' entails (e.g., top 10 hashtags vs. comprehensive analysis).

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?

Single sentence of nine words with action front-loaded. The parenthetical '(uses public data)' slightly disrupts flow but provides value. No redundant or filler content present.

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 no output schema and no annotations, the description should indicate return value structure (array of hashtags? metadata?). It fails to complete the behavioral picture for an external data-dependent tool.

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 the single 'topic' parameter fully described. The description adds minimal semantic context ('for a topic') but aligns with the schema. Baseline score appropriate since schema carries the documentation burden effectively.

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 ('Research') and resource ('trending hashtags') with scope ('for a topic'), distinguishing it from sibling tools like social_generate_tweet and social_thread_builder. However, 'Research' remains somewhat vague regarding whether it returns rankings, volume metrics, or simply lists.

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 provided on when to use this tool versus alternatives (e.g., when to research hashtags before using social_generate_tweet), or prerequisites for the topic parameter. The parenthetical '(uses public data)' hints at data source but doesn't inform selection logic.

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