"A server for finding SEO (Search Engine Optimization) resources and information" matching MCP servers:

A fully offline MCP server for token estimation, prompt compression, model routing, and semantic caching to optimize LLM usage costs and efficiency.

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.

Get access to real-time SEO data, including: keyword insights, backlink data, traffic estimates and more. Allow AI tools and Large Language Models (LLMs) to tap into the real-time SEO Review Tools API with natural language commands.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

The Search MCP Server enables seamless integration of network and local search capabilities in tools like Claude Desktop and Cursor, utilizing the Brave Search API for high-concurrency and asynchronous requests.

Graph RAG based context management engine for coding agents

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.

MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.

Multi-engine aggregated search MCP server that combines results from 7 search engines with deduplication, relevance ranking, and web page content extraction.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

Enables AI applications to submit claims, decisions, and evidence so it maintains a typed state model of beliefs, conflicts, changes, and next actions, preserving claim lineage and semantic evidence across versioned persistence.

An MCP server that enables natural language interaction with Google's Discovery Engine API, allowing users to search, recommend, and manage data through conversational interfaces.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

MCP server for querying a personal knowledge base using semantic RAG, powered by DeepSeek and local embeddings from Ollama.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.

Universal search engine for AI agents. Discover products, services, and businesses across every category. 10 MCP tools, zero LLM calls, millisecond responses.

Federated, local-first search for AI agents: one query fans across transcripts, files, a knowledge graph, a vector store, the live web, and YouTube, fused by trust-weighted RRF into one ranked answer. Five modes (grep/lexical/semantic/hybrid/rerank), runs entirely on your machine over MCP.

A persistent world your agents share across sessions and models. MCP memory server with semantic memory retrieval, cross-agent handoff, a hosted remote endpoint with OAuth, a free 24-hour room, and an open-source local stdio server.