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mcp-knowledge

by io.github.getgapup

brand_builder

Read-only

Architecte de marque — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Pennylane — brand identity SaaS fintech B2B FR/EU (2023). 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.
brandYes
targetYes
founderYes
existingAssetsNo

TDQS

C2.9/5.0
Behavior3/5

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

The description adds value beyond annotations by stating it returns a structured, audited deliverable and that inputs are validated server-side. Annotations already declare readOnlyHint=true and openWorldHint=true, so the behavioral profile is partially covered. However, it does not mention async behavior or response format.

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

Conciseness3/5

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

The description is brief (3 sentences) but lacks structure. The first sentence conveys the purpose, but the second sentence (case reference) is less actionable. Could be more front-loaded with core behavior.

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 (5 parameters with nested objects, no output schema), the description is insufficient. It does not explain the deliverable structure, processing time, or error handling. The async parameter is mentioned in schema but not in description, which is a gap.

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 20% (only async has a description). The description does not explain the purpose or constraints of the required objects (brand, founder, target). Users get no guidance on how to structure these inputs beyond what the schema provides.

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 it is a brand architect tool that returns a structured, audited deliverable. It references a case (Pennylane) to illustrate its purpose. While not extremely detailed, it distinguishes itself from siblings by focusing on brand architecture for startups.

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?

Provides some guidance: 'send the documented case fields' and mentions server-side validation. However, it does not explicitly state when to use this tool versus alternatives like positioning_strategist or content_audience_profile. No exclusion criteria.

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.

Resources