gapup-mcp

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

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.
`focus`	No	Optional context (geography, language target, comparator window, etc.)
`sector`	Yes	Sector to monitor (e.g. 'B2B SaaS productivity', 'EU fintech', 'climate-tech hardware')
`keywords`	Yes	3-8 keywords describing the watch perimeter

Output Schema

TableJSON Schema

Name	Required	Description
`kpis`	No	3-5 headline KPI bubbles
`trends`	Yes	5-12 trend cards for the sector
`recommendations`	No	Prioritised strategic recommendations
`executiveSummary`	Yes	Board-ready sector overview prose

TDQS

A4.3/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds return structure (5-12 cards, specific fields) and typical use cases. No contradictions. It does not cover rate limits or latency, but the async parameter partially addresses that.

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 concise and front-loaded with the primary purpose. However, the final line about 'Delivered by Manue, the AI CMO' is extraneous and slightly reduces conciseness. Still efficiently communicates key info.

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's moderate complexity (4 parameters, 2 required, output schema exists), the description adequately covers return format, use cases, and parameters. It is complete enough for an agent to select and invoke correctly.

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%, and the description adds meaning by explaining sector as 'Sector to monitor' and keywords as '3-8 keywords defining the watch perimeter'. It also mentions inputs in prose, reinforcing schema info. The async and focus parameters are well-described in 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 verb ('monitor') and resource ('emerging trends, regulatory shifts and adoption signals for a given market sector'). It details output (5-12 trend cards with momentum score, outlook, opportunity windows, actions), which distinguishes it from sibling tools like 'competitive_deep_dive' or 'market_entry_strategist'.

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 explicitly specifies when to use this tool: when the user asks what's heating up, wants to time a roadmap/content calendar, or needs an early read on a sector. This provides clear context, though it does not list when not to use or provide explicit alternatives.

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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