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mcp-knowledge

by io.github.getgapup

marketing_roi_dashboard

Read-only

Dashboard ROI marketing — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — H1 2026 · 5 canaux · ROI 3.2× · Attribution W-shaped · Budget €60k. Inputs are validated server-side — send the documented case fields.

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.
arpuEurYes
channelDataYes
companyNameYes
periodLabelYes
totalRevenueAttribEurYes
targetAttributionModelYes
currentAttributionModelYes
totalMarketingBudgetEurYes

TDQS

C2.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, indicating safe read operation. The description adds that inputs are validated server-side and that it returns an audited deliverable, but does not clarify timeliness (via async parameter) or any constraints beyond the schema. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short but inefficient, containing jargon ('Gapup agent-payable C-suite expertise') and a verbose reference case that could be condensed. The core purpose is not immediately front-loaded; the first sentence mixes the name with esoteric terms.

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

Completeness1/5

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

Given the tool has 9 parameters (8 required) and no output schema, the description is severely lacking. It does not explain the input fields, the structure of the deliverable, or how to use the async feature. The reference case provides an example but not sufficient guidance for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 11%, with only the 'async' parameter described. The description does not explain any of the 8 required fields (e.g., companyName, channelData structure). It vaguely says 'send the documented case fields' without specifying what they are. This forces an agent to rely entirely on the schema, which has minimal descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it returns a structured, audited deliverable related to marketing ROI dashboard. It gives a specific reference case, making the purpose clear. However, it does not explicitly differentiate from sibling marketing tools like 'brand_builder' or 'competitive_deep_dive', relying on the name for distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The phrase 'Gapup agent-payable C-suite expertise (CMO)' is confusing and does not help an agent decide when to invoke it. There is no mention of prerequisites or when not to use.

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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