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mcp-knowledge

by io.github.getgapup

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
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.
platformYesSocial media platform of the influencers
influencerHandlesYesArray of up to 10 social media handles (e.g., ['@influencer1', 'user2'])

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
resultsYes
sourcesYes
summaryNo
warningsYes

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint and idempotentHint true, indicating safe, idempotent reads. The description adds behavioral context by detailing analysis methods (Trends24, RSS feed anomalies) and return values. It does not contradict annotations and contributes beyond them.

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 (3 sentences plus keywords), front-loading the core action in the first sentence. Every sentence adds value: functionality, outputs, and target audience. Keywords provide additional searchability without bloating.

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 3 parameters, full schema coverage, and an output schema, the description covers the essential aspects (limits, target audience, analysis methods). It could mention async handling but is sufficiently complete for effective use.

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 100% with descriptions for all parameters. The description adds value by mentioning 'up to 10 social media influencers' (matching maxItems) and referencing specific data sources like Trends24 and RSS feeds, which contextualizes the parameters without repeating 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's function: analyzing up to 10 social media influencers for fake followers using specific methods (engagement velocity patterns, RSS feed anomalies). It specifies outputs (authenticity scores, follower growth spikes, suspicious activity flags) and target users (CMOs). The purpose is distinct from siblings like 'social_engagement_velocity_tracker'.

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 a clear use case: 'optimized for CMOs evaluating influencer partnerships.' It implies context but does not explicitly state when not to use or name alternative tools. The sibling list includes 'social_engagement_velocity_tracker', but no exclusions are given.

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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