"Information about costs or pricing" matching MCP servers:

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Provides AI assistants with real-time access to 1000+ AI models including their latest pricing, context windows, capabilities, and specifications. Supports model search, comparison, recommendations, and live testing.

AI agent knowledge marketplace where agents share solutions and earn tokens. Search, publish, and unlock previously solved problems to reduce token usage and computational costs.

Give your AI assistant real-time LLM/VLM knowledge. Pricing, benchmarks, and recommendations — updated every hour, not every training cycle.

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

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

NocturnusAI sits between your agent and the LLM. It extracts facts, reasons about what's relevant, and returns only what changed optimizing your token usage, saving you costs.

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

Turn Any Website Into Clean Fit-Markdown for AI Extract clean, structured Markdown from any static or dynamic website at enterprise scale. Reduce token costs by 90% and feed pure signal into your RAG pipelines and AI agents.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Compute price oracle for AI agents. Compare inference pricing across OpenAI, Anthropic, and DePIN providers like Hyperbolic. Get routing recommendations that save up to 80% on compute costs.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

An AI-powered chatbot that researches and tracks LLM inference pricing across providers, stores structured pricing data locally, and answers pricing questions via natural language.

Enables web search functionality through Perplexity AI's API with time-based filtering options. Allows users to search the web for recent information within specified time periods (day, week, month, or year).

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Enables AI agents to query up-to-date, sourced information about workflow automation services, AI agents, integrations, pricing, case studies, FAQs, and contact details in Dutch and English through read-only tools.

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.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

Provides persistent, semantic memory storage for LLMs across sessions using vector embeddings and FAISS search. Features bio-inspired memory consolidation, intelligent forgetting, and semantic retrieval without API costs.

Provides infinite long-term memory for AI agents with persistent, searchable storage of project details, preferences, and snippets. Reduces token costs by retrieving only relevant memories while keeping all data stored locally.

Enables semantic search over Omarchy, Arch Linux, and Hyprland documentation, allowing users to query configuration and usage information via natural language in Cursor, OpenCode, or Claude Code.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

A local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.

Enables AI agents to answer questions about a personal site straight from its published JSON bundle — profile, posts, case studies, pages, projects, stats and small datasets — through seven read-only tools for listing, full-text search, fetching Markdown, and querying dataset rows, with no sign-in, database or write path.