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    Salesforce AgentForce as a traversable MCP knowledge graph. 40 nodes covering Einstein Trust Layer, Agent Topics, Actions, Data Cloud, and Resolution Criteria. 11x fewer tokens than RAG. Free: uvx ckg-agentforce
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    MIT
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    Enables interaction with Pega case operations through a metadata-driven tool layer, allowing users to fetch, update, and manage cases. It supports standard Pega actions such as retrieving case details, attaching documents, and submitting case actions via API.
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    Enables reading and writing Zendesk tickets, including searching, fetching comments, posting replies and internal notes, setting ticket status, assigning tickets, logging time, and formatting tickets as Markdown issue drafts for handoff to GitLab, GitHub, or Jira.
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    Apache 2.0
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    Connects AI assistants to Custify customer success data for querying account metrics, health scores, and usage trends. It also supports taking actions such as creating notes, managing tasks, and triggering playbooks.
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    MIT
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    Infobip MCP Servers let you build AI agents to interact with the Infobip platform through the Model Context Protocol (MCP). Connect to Infobip and enable your agents to perform actions, such as sending messages over channels like SMS, WhatsApp, or Viber, or managing customer data in a controlled, pr
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    MIT
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    Enables AI clients to seamlessly take HubSpot actions and interact with your HubSpot data through the Model Context Protocol.
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    MIT
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    Enables to use Front from any MCP-compatible client. Search conversations, manage contacts, send messages, tag, assign, and automate inbox workflows with 26 tools and 172 actions.
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    An MCP server that provides access to Vitally customer data, allowing users to manage accounts, users, tasks, and conversations through the Vitally API. It enables actions like querying account health scores and creating notes via natural language.
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    Integrates with ConnectWise Manage API to enable ticket, company, and contact management operations. Supports filtering, pagination, and CRUD actions.
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    MIT
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    Enables users to diagnose home appliance errors and receive safety actions by identifying products from photos or error codes, using official manufacturer data.
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    Enables read-only investigation of payment transactions by building normalized timelines, detecting anomalies like duplicate charges and stuck refunds, and creating diagnostic escalations for human review. Never executes or modifies payment actions.