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mcp-knowledge

by io.github.getgapup

social_engagement_velocity_tracker

Read-onlyIdempotent

Tracks hourly social engagement velocity (likes, shares, comments) across Twitter, LinkedIn, and Reddit for CMOs. Inputs include platform handles/subreddits and time range. Outputs engagement metrics, velocity trends, and platform-specific insights. Ideal for real-time marketing performance monitoring and competitive benchmarking. Keywords: social media analytics, engagement tracking, marketing KPIs, CMO dashboard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
hoursNo
platformsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
trendsNo
sourcesNo
warningsNo
engagementNo

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. Description adds output context (metrics, trends, insights) but does not disclose potential limitations, rate limits, or data freshness.

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?

Three sentences front-loading purpose and inputs/outputs. The keywords list is slightly superfluous but does not harm clarity. Efficient for the complexity.

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

Completeness4/5

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

Given the existence of an output schema (not shown but mentioned), the description adequately covers the tool's functionality. Could mention pagination or real-time aspects more explicitly, but sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is low (33%) but description explains the inputs: platform handles/subreddits and time range, adding meaning beyond the schema. The async parameter is already explained in schema.

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

Purpose5/5

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

The description clearly states the tool tracks hourly social engagement velocity across three specific platforms, with inputs and outputs enumerated. It distinguishes itself from sibling marketing tools by specifying the exact metrics and use case.

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?

Provides ideal use cases ('real-time marketing performance monitoring and competitive benchmarking') but lacks explicit when-not-to-use or alternatives. No comparison to sibling tools.

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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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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