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"Generating a PDF report with context and images" matching MCP servers:

  • A
    license
    A
    quality
    B
    maintenance
    A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.
    10
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    An MCP server for Chinese financial reports, providing 44 tools to resolve companies, list/retrieve filings, extract report sections, run AI-based indicator extraction, search via Elasticsearch, and browse a 21,698-rule industry rules dashboard.
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    This MCP server enables AI agents to view PDFs as accessible HTML with bounding-box citations, and provides tools for layout-aware parsing, schema extraction, cross-document Q&A, and PDF rendering.
    27
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server that provides powerful RAG (Retrieval-Augmented Generation) capabilities for PDF documents. This server uses ChromaDB for vector storage, sentence-transformers for embeddings, and semantic chunking for intelligent text segmentation.
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.
    10
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.
    11
    19,534
    Elastic 2.0
  • A
    license
    A
    quality
    C
    maintenance
    An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.
    8
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Give your AI agents access to 8,500+ community curated awesome lists with over 1 million curated resources.
    2
    65
    57
    MIT
  • A
    license
    A
    quality
    D
    maintenance
    Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.
    6
    3
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Context Forge is a vendor-neutral continuity layer for AI tools. It stores project context in Markdown and JSON, retrieves task-scoped context, and leaves structured handoffs so another model can continue without being taught the project again.
    7
    Apache 2.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    A unified context layer that connects your local data — repositories, documents, remote machines, and notes — to LLM interfaces through the Model Context Protocol (MCP).
    3
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.
    363
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    An MCP server that enables AI assistants to manage Context Repo prompts, documents, and collections with semantic search and progressive disclosure navigation.
    112
    1
    MIT