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gapup-mcp

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds useful context about what is tracked (likes, shares, comments) and what outputs are produced (engagement metrics, velocity trends, insights), going beyond the annotations.

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?

The description is a single paragraph with useful sentences covering purpose, inputs, outputs, and use cases. The final 'Keywords:' sentence is redundant and adds minimal value for an AI agent, which is a minor structural flaw.

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?

With an output schema present and rich annotations, the description is complete enough: it explains purpose, inputs, outputs, and use cases. It does not mention async behavior or hour constraints, but these are visible in the schema, so the description is 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 only 33%, so the description compensates by mentioning 'platform handles/subreddits' (clarifying the platforms array) and 'time range' (mapping to hours). It does not detail the async parameter, but that is fully described in the 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 starts with 'Tracks hourly social engagement velocity...' which is a specific verb + resource + scope (across Twitter, LinkedIn, and Reddit). It also clearly states inputs and outputs, distinguishing it from unrelated sibling tools.

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?

The description provides clear use-case context: 'Ideal for real-time marketing performance monitoring and competitive benchmarking.' However, it does not explicitly mention when not to use the tool or name alternative tools, so it lacks exclusions.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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