Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

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
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.
team_sizeNo
expected_loadYes
workload_typeYes
business_contextNo
cloud_preferenceNo
region_preferenceYes
budget_monthly_eurNo
compliance_requiredNo

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the tool is safe and open-ended. The description adds that it returns a 'structured, audited deliverable' and mentions server-side validation, but provides little additional behavioral context beyond the annotations.

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 lengthy due to many example queries, which reduce conciseness. The first sentence effectively front-loads the purpose, but subsequent sentences could be trimmed to improve readability without losing essential information.

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 tool's complexity (9 parameters, design deliverable), the description lacks detail about the output format or structure beyond 'structured, audited deliverable'. No output schema exists, so the description should provide more context on what the deliverable contains.

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

Parameters3/5

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

Schema description coverage is low (11%). The description's example queries cover parameters like workload_type, expected_load, region_preference, budget, and compliance, adding meaning beyond the schema. However, parameters like async, team_size, and business_context are not explained, so compensation is partial.

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 designs a cloud infrastructure blueprint for various workload types, using verbs like 'Architecte' and 'Design'. It distinguishes itself from siblings by focusing on cloud architecture, which is unique among the listed tools.

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 example questions implying usage for cloud architecture design, but lacks explicit guidance on when to use this tool versus alternatives or when not to use it. No exclusions or alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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