gapup-mcp

infra_blueprint_designer

Read-only

Architecte infra cloud — Gapup agent-payable C-suite expertise (CTO). Returns a structured, audited deliverable. Answers: Design a cloud infrastructure blueprint for a app with expected traffic and requirements. · What is the recommended AWS vs GCP vs Azure architecture for a SaaS multi-tenant app with EU data residency and SOC2? · How should I architect my cloud infra to stay under €5k/month with GDPR compliance and a junior DevOps team? · What cloud services do I need for a with load — compute, DB, cache, CDN, observability? · Give me an end-to-end cloud architecture with scaling plan, security baseline, and IaC tool recommendation. Reference case: Spinora fintech B2B SaaS — saas-multi-tenant · medium load (1k-100k req/d) · eu-west · . Inputs are validated server-side — send the documented case fields.

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.
`team_size`	No
`expected_load`	Yes
`workload_type`	Yes
`business_context`	No
`cloud_preference`	No
`region_preference`	Yes
`budget_monthly_eur`	No
`compliance_required`	No

TDQS

B3.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable context: it is an 'Architecte infra cloud' that provides CTO-level expertise, mentions the ability to use async mode ('returns a job_id immediately'), and notes server-side validation. No contradictions with annotations.

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 excessively verbose, containing a long block of example questions and mixed French/English. It lacks structure and conciseness; the key information could be conveyed in fewer sentences.

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?

Despite the tool's complexity (9 parameters, no output schema), the description fails to explain the return format, limitations, or expected behavior beyond stating it returns a 'structured, audited deliverable'. More completeness is needed for an agent to use it reliably.

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 only 11%, meaning most parameters lack explanations. The description does not systematically describe each parameter beyond the example queries. For a tool with 9 parameters and low schema coverage, more explicit parameter semantics are needed.

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: 'Architecte infra cloud — ... Returns a structured, audited deliverable.' It includes specific example queries that demonstrate its function (e.g., 'Design a cloud infrastructure blueprint for a <workload_type> app'). The title and examples distinguish it from sibling tools, none of which focus on cloud architecture design.

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 provides implicit usage guidance through example questions, indicating when to use the tool (e.g., for cloud architecture design with specific constraints). However, it does not explicitly state when not to use the tool or mention alternative tools, leaving the agent to infer 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

C2.5/5.0

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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