"How to create draft emails using Gmail's support agent class" matching MCP servers:

Exposes order status lookup and knowledge base search tools from the Support Agent AI over MCP, enabling MCP clients to handle customer support queries with grounded, citation-backed answers.

Enables AI assistants to handle customer support by looking up order status, semantically searching knowledge bases, and creating support tickets through MCP tools, with grounded answers and automated evaluations.

Enables support engineers to analyze tickets against a help center with cited retrieval, role-gated and human-confirmed actions, and safety-gated evaluation.

Enables AI agents to check order statuses, retrieve FAQ policy answers via RAG, and create/manage support tickets, all based on the e-commerce support workflow in the repository.

Enables an ElevenLabs support agent to call support tools over MCP, including Discord ticket flows, post-call webhook handling, and knowledge-base syncing.

Enables job-application lookup and domain support through MCP, exposing tools such as job lookup by ID over a local HTTP endpoint.

Provides e-commerce customer support tools for order status, delivery, account, and policy questions, with RAG-grounded retrieval and strict authorization checks over mock data.

Enables natural language querying and semantic search over a bilingual customer support ticket dataset using SQL and embeddings.

Exposes a support-ticket inbox as tools an AI client can use to list, inspect, triage, and update tickets, including LLM-based category, priority, and sentiment classification with a drafted first response informed by similar past tickets. It works with a mocked dataset styled after a home-loan partner-support inbox, with the tool interface designed so a real ticket source could be swapped in later.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

Enables ticket management through a REST API and support document search via a local vector database, powered by Ollama and LanceDB.

Enables a support bot to answer FAQ questions from a local vector store, with admin tools to add, list, and delete knowledge entries while enforcing read-only versus admin bearer-token permissions.

Enables enterprise SaaS support teams to get retrieval-augmented answers from indexed support documentation and to run natural-language questions as SQL queries against a support PostgreSQL database. It returns answers with source references and exposes the generated SQL alongside its results, with optional tracing and evaluation.

Enables travel-related queries and planning including weather, train and flight information, itinerary generation, and travel knowledge retrieval via RAG, using an AI agent with MCP protocol.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

A Model Context Protocol (MCP) server that provides AI-powered customer support using Cursor AI and Glama.ai integration.

Exposes read-only knowledge base articles on returns, warranty and shipping policies alongside tools that search, retrieve and create real support tickets. This lets an LLM agent ground its answers in store policy without inventing information, and act on actual ticket data rather than simulated records.