"Forwarding Work Requests and Comments to Other Models or Tools" matching MCP servers:

MCP server for file operations with non-UTF-8/Unicode encoding: Cyrillic, CP1251, CP1252, ISO-8859, KOI8 auto-detection

MCP server for encoding-aware file operations supporting 24 encodings, with secure directory-restricted tools and optional local execution for ChatGPT Web via OpenAI Secure MCP Tunnel.

Go Development MCP Server for Claude Desktop and other AI tools

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.

Perform DNS lookups, WHOIS queries, connectivity testing, TLS certificate analysis, HTTP endpoint monitoring, and hostname resolution, all from your trusty AI.

An MCP server that provides read-only access to the Paprika Recipe Manager, allowing users to list and retrieve recipes, grocery items, and meal plans. It enables seamless interaction with recipe details and category information through the Paprika API.

Enables natural-language interaction with self-hosted Jira and Bitbucket, providing tools for ticket management, pull requests, code review, and git context.

Self-hosted webhook relay and tunnel with Noise NK encryption. Inspect requests, replay webhooks, trace AI pipelines, connect/disconnect tunnels — 13 MCP tools. No tokens or passwords needed.

Agent-native discussion forum for the x402 / A2A ecosystem. A hosted MCP server exposes the whole forum as tools (threads, comments, votes, bounties, reviews, search, profile) with x402 paid threads and USDC bounties on Base. Endpoint: https://api.achivx.com/mcp/

Enables interaction with Bitbucket through the Model Context Protocol, allowing users to manage pull requests, add comments, review code, create tasks, and perform other repository operations using natural language.

Telegram MTProto-based MCP server with 19 tools for reading, searching, sending, forwarding, and summarizing messages

An MCP server that enforces authorization by evaluating Rego policies or querying an OpenID AuthZEN-compliant PDP, providing accurate access decisions for AI agents. It includes tools for policy evaluation, AuthZEN evaluation, batch evaluation, and PDP discovery.

Production-ready MCP server designed to empower AI agents and LLMs to interact seamlessly with ClickHouse. It exposes your ClickHouse database as a set of standardized tools and resources that adhere to the MCP protocol, making it easy for agents built on OpenAI, Claude, or other platforms to query, explore, and analyse your data.

Enables MCP-capable agents to join Landfall incident war rooms as live investigators, contributing findings and widgets to the incident timeline in realtime while pulling other investigators' discoveries, with single-use share links or authenticated sessions.

KSM MCP is a Model Context Protocol (MCP) server that acts as a secure intermediary between AI language models (like Claude) and Keeper Secrets Manager (KSM). It allows AI agents to manage your KSM secrets-such as listing, creating, retrieving, and deleting records and folders-while protecting your

A policy and audit layer for Model Context Protocol (MCP). MCP Firewall sits transparently between any MCP client (Claude Desktop, Cursor, VS Code, etc.) and an upstream MCP server, allowing you to inspect, allow, block, or audit requests before they reach the server.

LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.

Enables AI assistants and IDEs to work with the AGNTCY Agent Directory, providing tools for validating, publishing, searching agent records, and navigating OASF taxonomies.

self-hosted development harness (Go engine + CLI + desktop, Linux first) · brief → requirements → spec → plan → build → review → release · verdicts are exit codes, never model opinions · local models first (llama.cpp, vLLM) beside any OpenAI-compatible or Anthropic endpoint · operable by humans or by other agents over MCP with recorded, attributed decisions