"Coze" matching MCP servers:

Enables AI agents to get read-only trust verdicts on knowledge records by evaluating evidence maturity, provenance, intended use, risk level, and scope before deciding whether knowledge may be used.

Universal MCP server for executing TypeScript and Python code with progressive disclosure, reducing token usage by 98% by enabling on-demand access to all other MCP tools through code execution rather than loading tool definitions directly.

2-5x longer Claude Code sessions before compaction. Saves 30-40% on input token costs. Remembers your rules and corrections so Claude stops repeating mistakes after compaction. Auto-runs in the background, just install once and forget about it.

Enables Claude Code to act as an autonomous Telegram agent with tools for replying, reacting, editing messages, searching history, and handling attachments. Includes a TUI watchdog for unattended operation.

Enables authenticated MCP tools for planning commercial routes with fail-closed economic boundaries, plus marketplace discovery, matching, and agent interaction capabilities.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

Enables AI agents to hand high-level objectives to a decision-making core that autonomously reasons, plans, enforces deterministic policy, executes capabilities, evaluates outcomes, and persists semantic memory over stdio or Streamable HTTP.

Enables multi-agent council workflows for coding assistants, providing parallel independent reviews, plans, brainstorms, validation, and research with strict read-only boundaries.

Enables Codex to delegate long-running work to Claude Code via blocking MCP calls, with tools for model discovery, run, and resume without polling.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

This MCP server is designed for planning with Claude Code, Cline, or Cursor and making changes with Cerebras to maximize speed and intelligence while avoiding API limits. It uses the Qwen 3 Coder model for high-quality code generation and can be embedded in IDEs.

Provides persistent, client-side-encrypted memory for AI coding assistants via SAIHM, enabling tools to remember, recall, and forget with provable erasure.

Provides a secure interface to run Anthropic's Claude Code CLI as an MCP server, enabling task execution, persistent memory, and integration with MCP-enabled IDEs.

Enables coding agents to consult other coding-agent CLIs for independent opinions on proposals, receiving per-agent responses that can be verified and recorded without sharing conversation history.

AI consensus market oracle for crypto traders and autonomous agents. BUY/SELL/HOLD signals with 11-signal consensus (RSI, MACD, funding rate, Fear & Greed, congressional trading, Polymarket edges). Ed25519-signed. x402 micropayments on Base.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

Enables MCP clients to create, read, message, wait for, interrupt, and settle persistent peer threads through an existing T3 Code server, keeping them visible across its clients. It supports project-scoped coordination with source attribution and retry-safe commands.

Enables Claude Code to spawn child sessions for parallel, asynchronous task execution, allowing users to continue working while tasks run in the background.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.