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mcp-knowledge

by io.github.getgapup

trend_watcher

Read-onlyIdempotent

Monitor emerging trends, regulatory shifts and adoption signals for a given market sector. Returns 5-12 trend cards, each with a momentum score (rising/stable/declining), a 3-month and 12-month outlook, opportunity windows, and recommended actions. When to use this tool: the user asks what is heating up in a market, wants to time a product roadmap or content calendar, or needs an early read on a sector. Inputs: a sector to monitor and 3-8 keywords defining the watch perimeter. Delivered by Manue, the AI CMO of the Gapup portfolio.

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.
focusNoOptional context (geography, language target, comparator window, etc.)
sectorYesSector to monitor (e.g. 'B2B SaaS productivity', 'EU fintech', 'climate-tech hardware')
keywordsYes3-8 keywords describing the watch perimeter

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kpisNo3-5 headline KPI bubbles
trendsYes5-12 trend cards for the sector
recommendationsNoPrioritised strategic recommendations
executiveSummaryYesBoard-ready sector overview prose

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, and no destruction. The description adds behavioral details: returns a structured set of 5-12 trend cards with specific fields (momentum score, 3-month/12-month outlook, etc.). It does not contradict annotations and provides useful context about the output structure.

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 reasonably concise and front-loaded with the main purpose. However, the closing sentence 'Delivered by Manue...' is marketing fluff that does not add functional value. Removing it would improve conciseness.

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

Completeness5/5

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

Given the tool's moderate complexity, existing annotations (readOnly, idempotent, non-destructive), and the presence of an output schema, the description fully covers what an agent needs: purpose, when to use, parameter guidance, and output structure. No gaps identified.

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%, so baseline is 3. The description adds value by paraphrasing the two required params ('sector to monitor' and 'keywords defining the watch perimeter') and clarifying the optional 'focus' parameter as 'geography, language target, etc.' This aids understanding beyond 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 clearly states the tool's purpose: 'Monitor emerging trends, regulatory shifts and adoption signals for a given market sector.' It specifies the output format (5-12 trend cards with scores, outlooks, etc.) and explicitly differentiates from siblings by listing when to use ('what is heating up in a market, wants to time a product roadmap...'). This is specific and distinguishes it from other market analysis 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 includes an explicit 'When to use this tool' section with three concrete scenarios, helping the agent select it over siblings. However, it does not provide when-not-to-use guidance or mention alternative tools, which would strengthen it further.

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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