gapup-mcp

social_influencer_fake_follower_detector

Read-onlyIdempotent

Analyzes up to 10 social media influencers for fake followers by checking engagement velocity patterns (Trends24) and RSS feed anomalies. Returns authenticity scores, follower growth spikes, and suspicious activity flags. Optimized for CMOs evaluating influencer partnerships. Includes keywords: influencer marketing, fake follower detection, engagement analysis, social media audit.

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.
`platform`	Yes	Social media platform of the influencers
`influencerHandles`	Yes	Array of up to 10 social media handles (e.g., ['@influencer1', 'user2'])

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`results`	Yes
`sources`	Yes
`summary`	No
`warnings`	Yes

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description doesn't need to repeat them. It adds context about the analysis methods (Trends24, RSS anomalies) and outputs, but does not disclose limitations or edge cases.

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 concise, front-loaded with the main action, and includes only necessary details. Each sentence adds value, including the keywords section which can aid searchability.

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 provides a good overview of what it returns. It is complete enough for a simple parameter set, though it could mention potential limitations or accuracy notes.

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 description coverage is 100%, so the schema already documents parameters adequately. The description does not add significant meaning beyond the schema, so baseline 3 is appropriate.

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 analyzes up to 10 influencers for fake followers using engagement velocity patterns and RSS anomalies, returning specific outputs. It distinguishes itself from sibling tools like 'social_engagement_velocity_tracker' by focusing on fake follower detection.

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?

The description mentions it's optimized for CMOs evaluating influencer partnerships, implying a use case, but does not explicitly state when to use it versus alternatives or when not to use it.

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.

Resources

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