"An explanation or resources about vectors (mathematics, physics, programming, or graphics)" matching MCP servers:

Enables Claude to act as a physics research assistant: explains concepts with dimension-checked equations, runs live simulations, ingests arXiv papers with verbatim citation anchoring, and audits bibliographies for fabricated references.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

A Model Context Protocol server that enables searching YouTube videos, retrieving and storing transcripts, and performing semantic search over video content without using the official YouTube API.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Enables adding a cite-or-refuse answer box to your docs from your agent.

PhysBound is a specialized "Physics Linter" for AI that deterministically validates RF and thermodynamic claims against hard physical limits, preventing hallucinations in engineering workflows.

An MCP server for source-grounded, page-cited evidence retrieval tailored for embedded and firmware development, returning verbatim snippets with exact citations or 'no match' to prevent fabrications.

Enables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.

A local self-hosted MCP server for searching and verifying Gurmukhi quotes from Sri Guru Granth Sahib, offering keyword search, authenticity verification, and text guarding via an MCP or HTTP API.

stdio server to read markdown with images or index markdown headings from a file, folder or URL

Enables natural-language semantic search over your own local files through an MCP server, fully offline without API keys or a server daemon, with optional LLM-grounded answers and exact file:line sources.

Record development decisions as structured JSON, embed them as vectors via Gemini, and search semantically over MCP. Works with Claude Code, Cursor, Windsurf, and any MCP client.

Semantic search over Nordic economic data — market announcements, quarterly reports (162 companies), macro data (NO/SE/DK/FI), commodity prices, and press releases. 180,000+ vectors.

Enables AI assistants to search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Lets AI agents search and read a folder of Markdown documentation at query time, exposing heading-level search, whole-page or section retrieval, and page listing as tools and resources over stdio or Streamable HTTP. Can run as a CLI, a hosted endpoint, or a library inside an existing Node or Next.js site.

A local-first codebase intelligence tool that enables AI assistants to research codebases using semantic search, multi-hop relationship discovery, and structural parsing. It allows users to extract architectural patterns and institutional knowledge across 30+ programming languages through an MCP-compatible interface.

An MCP server providing secure calculation, knowledge base search, and text statistics tools, along with resources and prompts for enterprise agent integration.

An MCP server that provides a search_docs tool with hybrid retrieval (BM25 + dense vectors) and cross-encoder reranking, backed by evaluation, prompt-injection guardrails, and OpenTelemetry tracing.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.