gapup-mcp

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

Name	Required	Description
`async`	No	If 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.
`hours`	No
`platforms`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`trends`	No
`sources`	No
`warnings`	No
`engagement`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate readOnly and idempotent hints. The description adds context by specifying tracked metrics (likes, shares, comments) and outputs (velocity trends, platform insights), consistent with annotations. No contradictions.

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 concise sentences with front-loaded action. The keywords line at the end is slightly repetitive but does not detract significantly. Could be slightly more focused avoiding keyword stuffing.

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 tool has an output schema, the description adequately summarizes outputs as engagement metrics, velocity trends, and insights. For a 3-parameter tool, this is sufficient.

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 33% with descriptions on 'async' only. The tool description mentions time range and platform handles/subreddits, adding some value beyond the schema but not fully compensating for low coverage. Parameter details remain basic.

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 it tracks hourly social engagement velocity across specific platforms (Twitter, LinkedIn, Reddit) for CMOs, with a specific verb and resource. It distinguishes itself from siblings by focusing on engagement metrics and target audience.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It indicates ideal use for real-time marketing monitoring and competitive benchmarking, providing context. However, it does not explicitly state when not to use or compare to alternative tools in the sibling list, such as brand_builder or sentiment_news_pulse.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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