"x402 Bazaar MCP (Model Context Protocol) Server" matching MCP servers:

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Summarizes Slack activity and GitLab merge requests with OpenAI to help generate daily standups, weekly reports, or quarterly check-ins.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.

Enables AI agents to discover, query, and pay for HTTP endpoints and MCP tools using Stellar USDC/SEP-41 stablecoins through a federated P2P bazaar catalog and x402 payment facilitator.

A server that allows users to query and analyze cloud cost data from Cloudzero using natural language through Large Language Models.

Enables AI agents to generate videos by paying per call with USDC on Base via the x402 protocol, with no accounts or API keys required.

Discovers local model runtimes such as Ollama, LM Studio, and LocalAI, and exposes their endpoint metadata to MCP clients like Copilot.

Enables agents to purchase VPN access by locally signing and broadcasting x402 USDC payments on Solana or EVM, then fulfilling payments to obtain and save credentials.

Enables AI coding agents to offload large file reads and boilerplate code generation to cheaper or local worker models through MCP, reducing token consumption and keeping context clean.

Enables AI assistants to directly manage Anki flashcards, decks, notetypes, tags, and media, with support for batch operations, searches, and deck exports via file-based input/output.

A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.

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.

Routes subagent tasks across Claude and OpenAI lanes by task kind and quota state, deciding whether to spawn a single model, run a duel of both vendors, or run both and ship the merge. It records duels with attested proof-of-run session ids, grades sides blind with one judge per vendor, and tracks per-kind standings and lane pacing.

Enables AI agents to check their wallet address and USDC balance, preview the cost of an x402 call, and pay for API requests in USDC on Base without an account or API key. Spend caps, allow-lists and blocked contracts are enforced in code before anything is signed, so a prompt injection cannot bypass them.

Enables multi-agent coordination with file locking, shared registry, async messaging, and architectural decision logging via MCP tools.

This MCP server provides a local-first control plane for coding agents, enabling task routing, contract management, and shared learning across multiple AI vendors via markdown files.

MCP server for discovering and cataloging generative AI models, enabling agents to search by modality, view pricing/terms, and manage contracts with providers via Beckn-aligned APIs.

A paid MCP server that lets AI agents send SMS messages to US phone numbers via x402 micropayments, with automatic compliance and opt-out handling.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.