Enterprise Threads MCP Server

get_engagement_trends

Analyze engagement trends by tracking views, likes, replies, reposts, quotes, shares, and clicks over daily or weekly intervals for week, month, or quarter periods.

Instructions

Analyze engagement patterns and trends over time

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_engagement_trends"
  }
}

Input Schema

Name	Required	Description
`metrics`	No	Engagement metrics to track
`timeframe`	No	Analysis timeframe
`granularity`	No	Data point frequency

Schema Changelog

Changes observed during successful MCP inspections.

First observedv5.1.0Aug 14, 2026

TDQS

C2.7/5.0

Behavior1/5

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It merely states the tool analyzes engagement patterns and does not mention any traits such as output format, aggregation behavior, data scope, or side effects. This is a significant gap for a tool in a crowded analytics space.

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 a single concise sentence with no wasted words. It is front-loaded with the verb and clearly states the tool's purpose. It is appropriately sized for a tool with a well-defined schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is vague and does not explain what 'trends over time' means in practice—whether it returns a time series, aggregates across posts, or how to interpret results. With no output schema and no annotations, the agent lacks enough context to understand the tool's full behavior or distinguish it from similar analytics tools in the large sibling group.

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?

The input schema has 100% description coverage for its three parameters (metrics, timeframe, granularity), each with clear enum values and descriptions. The description adds no parameter-specific 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 'Analyze engagement patterns and trends over time' clearly states a specific verb ('analyze') and resource ('engagement patterns and trends') with a temporal scope. However, it does not differentiate this tool from many sibling analytics tools such as get_enhanced_insights or get_content_performance_report, so it falls short 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 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 alternatives. It does not mention exclusions, prerequisites, or alternative tools, leaving the agent to infer usage solely from the name and schema.

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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