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mcp-knowledge

by io.github.getgapup

paid_ads_optimizer

Read-only

Optimiseur de publicités payantes — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Spendesk (Google + LinkedIn · €45k/mo) — €9k/mo gaspillés identifiés · ROAS LinkedIn ×2.4. 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.
companyYes
campaignsYes
targetMetricYes
audienceDescriptionYes
totalMonthlyBudgetEurYes

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already set readOnlyHint=true, indicating no modification. The description adds that inputs are validated server-side and returns a deliverable, but doesn't disclose other behavioral traits like rate limits or specific output format. Adequate given annotations.

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 relatively concise, with a brief introductory line and a reference case. It avoids unnecessary detail, though the reference case could be considered slightly tangential. No structural issues.

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

Completeness2/5

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

Given the complexity (nested objects, 6 parameters, no output schema), the description is incomplete. It doesn't explain what the deliverable contains, how to interpret results, or how to handle the async parameter beyond its own description. The tool's behavior and output are underspecified.

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

Parameters2/5

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

Schema description coverage is low (17%), only the 'async' parameter has a description. The tool description does not elaborate on key parameters like company, campaigns, or targetMetric, leaving the agent to rely solely on the schema structure. Insufficient compensation for low coverage.

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 clearly states the tool optimizes paid ads and returns a structured deliverable. The reference case provides context, but it doesn't explicitly differentiate from sibling tools like programmatic_attribution_calibrator or seo_keyword_research. The purpose is clear but not fully distinguished.

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 implies use for paid ads optimization, targeting C-suite (CMO), but lacks explicit guidance on when to use vs. alternatives. No exclusion criteria or alternative tool mentions, leaving the agent to infer usage from context.

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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