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305,087 tools. Last updated 2026-07-22 19:24

"A server for finding architectural design resources" matching MCP tools:

  • Submit architectural questions to receive expert guidance on system design, scalability, and technical decision-making. Provide optional context for tailored recommendations.
    MIT
  • Consult Ollama AI models for architectural decisions, code reviews, and design discussions. Supports sequential chaining for complex multi-step reasoning.
    MIT
  • Analyzes a repository to generate a Knowledge Extraction Report with architectural insights, design decisions, data flow, strengths, risks, and learning paths.
    MIT
  • Analyze design documents, UI/UX mockups, or architectural diagrams to identify usability issues, accessibility concerns, aesthetic inconsistencies, and potential design flaws.
    Apache 2.0

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  • Generate architectural design feedback using natural language input and maintain context with optional conversation ID through POST requests to the LLM Architect tool on the MCP Server Template platform.
    ISC
  • Record, search, and update project design decisions with rationale, outcomes, and dependencies. Get reminders for overdue check-ins.
    MIT
  • Breaks down complex tasks into interactive, sequential plans with revision and branching for project planning, system design, and architectural decisions.
  • Stop the transport session for a specific device, freeing its resources, without affecting other running sessions.
    Apache 2.0
  • Compare a design against a rendered implementation and get structured discrepancies. Provide a Figma URL, design image, or component description to produce a report of visual differences.
    MIT
  • Search audit findings by title, severity, or status to check for duplicates before creating a new finding or to locate a specific finding for update or evidence attachment.
    MIT
  • Retrieve curated architectural rules for a specific task type. Returns only the 2–3 relevant rule files for tasks like adding tools, services, or debugging pipelines.
    MIT
  • Compare component output against a reference image to verify design accuracy. Returns pixel diff percentage and pass/fail status for design QA.
    MIT
  • Retrieve relevant learnings for the current task context. Returns scored warnings and architectural patterns based on step number, components, and domain.
    AGPL 3.0