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When you connect an AI to Clearskies over MCP, it gets a set of tools for reading your Context Graph and building in Clearskies. This page describes what those tools let a connected AI do, whether that’s Claude, ChatGPT, or a platform like n8n or Retool. For connection steps, see MCP Server. Each category below names a few representative tools rather than listing every one, the definitive list is always what your connected AI sees at runtime.

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CRM records

Read the accounts, contacts, and deals in your graph, and answer questions across them.
  • List and read records: accounts_list, contacts_list, and deals_list return your core CRM records, and crm_records_list covers any other object type in your graph
  • Drill into an account: account_get_contacts and account_get_deals pull one account’s contacts or deals directly
  • Aggregate: records_aggregate runs counts and other aggregations across records, pipeline by stage, deals by owner, without pulling every record into the conversation

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Schema discovery

Before querying, a connected AI can look up which object types and fields exist in your workspace, so it queries real fields instead of guessing.
  • Object types: object_definitions_list returns the object types in your graph
  • Fields: object_get_fields_schema returns the field schema for one object type
  • Search: schema_search finds relevant objects and fields without pulling the full schema

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Activity and communications

The calls, emails, and meetings connected to your accounts, not just that they happened, but what was said.
  • List and search: events_list and events_search find calls, emails, and meetings across the graph or for a specific account
  • Read contents: events_get_contents returns the substance of an event, a call transcript, an email body
  • Look ahead: calendar_get_upcoming shows upcoming meetings, useful for call prep

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Team and workspace

  • People: employees_list returns the people on your team, so an AI can attribute activity to the right owner
  • Identity: identity_get resolves who the connected user is
  • Slack: channels_list returns the Slack channels available in your workspace

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Support tickets

support_tickets_list returns support tickets connected to your graph, so account health questions can include what’s happening in support.

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Engineering activity

github_activities_list returns GitHub activity connected to your graph, putting engineering work alongside CRM and communication data.

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Deep research

For questions that span many records and sources, a connected AI can hand the work to Clearskies instead of assembling it call by call: deep_research starts a long-running research job across the graph, and deep_research_status polls it until the result is ready.

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Building workflows and agents

Beyond reading data, a connected AI can take Clearskies workflows and agents through their full lifecycle, create, validate, test, and publish, using the workflows_* and agents_* tools, with workflow_capabilities_get and agent_capabilities_get telling it what your workspace supports before it builds. See Manage workflows from Claude or ChatGPT and Manage agents from Claude or ChatGPT for how that works. To put these tools to work from an assistant, see Build in Claude or ChatGPT.