ClaudeHopper – Construction‑Focused Model Context Protocol Server

ClaudeHopper is a purpose‑built MCP server that bridges large language models with the complex world of construction documentation. It turns a pile of CAD drawings, PDFs, and specification sheets into an intelligent knowledge base that can be queried by Claude or any LLM capable of speaking MCP. By embedding both text and images locally, it offers a privacy‑preserving, low‑latency search experience that is especially valuable in regulated or sensitive construction environments where external cloud services are not an option.

What Problem Does It Solve?

Construction projects generate thousands of pages of drawings, sections, and written specifications in a variety of formats (PDFs, DWG exports, etc.). Traditional document management systems treat these as opaque files; an LLM can only guess at content without a structured index. ClaudeHopper creates semantic embeddings for every paragraph and drawing image, stores them in a local vector database (LanceDB), and exposes MCP tools that let an assistant retrieve the exact drawing or specification snippet you need. This eliminates the need for manual browsing, reduces the risk of overlooking critical details, and speeds up design reviews or compliance checks.

Core Functionality & Value

• Vector‑based Retrieval – Textual queries are matched against high‑dimensional embeddings, delivering the most relevant drawings or specification sections in milliseconds.
• Visual Search – By embedding images with CLIP, users can ask for drawings that “look like a concrete foundation” or “show a lift station layout,” and the assistant returns the closest visual matches.
• Metadata Extraction – Specialized NLP pipelines pull out project names, discipline tags (Structural, Civil, Architectural), and drawing types, enabling fine‑grained filtering.
• Intelligent Chunking – Documents are split into contextually coherent segments, preserving meaning while keeping token usage low for the LLM.
• Local Processing & Security – All data stays on‑premises; no external API calls mean compliance with strict confidentiality requirements.

Use Cases & Real‑World Scenarios

• Design Review – Quickly locate all sections that discuss fire protection or interior finishes, and have Claude summarize them for a stakeholder meeting.
• Change Management – When a new material is introduced, search all drawings and specs for references to that material to assess impact.
• Construction Coordination – Query the assistant for “all drawings showing the foundation system” and receive a list of PDFs with embedded thumbnails, ready to share with subcontractors.
• Regulatory Compliance – Verify that every required section of a project’s specifications is present and up to date by searching for key phrases or terms.

Integration into AI Workflows

Developers embed ClaudeHopper as a local MCP server in their existing Claude Desktop or other LLM workflows. The assistant can invoke the following tools:

• – filter by project, discipline, or drawing type.
• – locate specific textual content within documents.
• – find drawings that match a visual description or similarity query.

These tools are called through standard MCP messages, so no custom code is needed beyond the server itself. The assistant can seamlessly combine RAG outputs with its own reasoning, producing precise, context‑rich responses that reference actual drawings or specification clauses.

Distinct Advantages

ClaudeHopper is the first MCP server engineered specifically for construction documentation. Its hybrid text‑image search, industry‑specific metadata extraction, and fully local deployment give it a competitive edge over generic RAG solutions. By keeping all processing on the client side, it protects intellectual property and ensures that sensitive design data never leaves the organization. For developers building AI‑powered construction assistants, ClaudeHopper offers a turnkey, privacy‑first platform that turns static PDFs into dynamic, queryable knowledge.