"A guide for reducing token count in AI requests" matching MCP servers:

Orchestrates AI agents through structured markdown documents, enabling multi-agent workflows with automatic context injection and workflow management.

Enables AI agents to drive real Google Chrome with stealth and captcha handling, using Patchright to avoid bot detection.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Discovers LLM models in real time from cloud providers and local Ollama instances, returning compatibility profiles and live pricing so AI agents can route tasks to the cheapest viable model without breaking tool calls or context clipping.

Enables AI coding agents to conserve tokens by enforcing rules like delegating heavy work to subagents, checkpointing before token limits, and grepping before reading files.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents to cut token usage by locally pruning, encoding, and summarizing MCP tool responses, with lossless recovery of original data when needed.

Enables MCP-compatible clients to manage AI agent teams, including agents, Kanban issues, projects, goals, approvals, budgets, and raw API access.

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

Enables adaptive token bucket rate limiting and backoff scheduling to prevent HTTP 429 errors for autonomous agents and API clients. Provides a deterministic zero-dependency MCP/CLI engine for acquiring token permits and returning structured telemetry.

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

Enables terminal AI coding assistants to interact with web AI chat services through browser automation, supporting multiple platforms like Doubao and ChatGPT.

Enables a host AI agent to trigger a multi-agent debate, running N-rounds where agents critique each other's answers, then synthesizes a consensus recommendation with ranked options.

A multi-user AI group chat server via MCP that lets multiple users' AI agents join a shared discussion room to debate topics and reach convergence.

A secure Model Context Protocol server that enables Claude Code to connect with OpenAI and Google Gemini models, allowing users to query multiple AI providers through a standardized interface.

Merx - TRON Resource Exchange Merx is the first TRON resource exchange that aggregates energy and bandwidth providers into a single MCP server. GitHub: https://github.com/Hovsteder/merx-mcp Hosted SSE: https://merx.exchange/mcp/sse npm: merx-mcp Website: https://merx.exchange Stats * 52 tools, 30 prompts, 21 resources * 7+ energy providers (CatFee, ITRX, PowerSun, Fe

Enables AI assistants to intelligently select and switch between different AI models (OpenAI, Anthropic, etc.) within the same conversation based on task requirements. Provides a unified interface for accessing multiple AI providers through a single MCP tool.