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The Klarity MCP exposes a read-only set of tools that an AI assistant can chain to answer real questions grounded in a customer’s workspace. The point of this reference is not to enumerate APIs — it’s to help you pick the right tool for the goal at hand.

How the toolset is organized

Every workflow reduces to: find the right process(es) → fetch detail → gather evidence → traverse relationships → synthesize.
GroupWhat it coversWhen to start here
Standard entry pointssearch, fetchThe default starting path for any process question
Process IndexHierarchy navigation, process details, ranked searchsearch results feel sparse, or you need richer metadata than fetch returns
Process observationsRecent changes, observations, activity timelines, sessions”What changed?”, “what happened?”, “why does this run this way?”
ArtifactsDocument search, content retrieval, in-document text searchThe user names a document or recording
WorkspaceList accessible workspacesOrienting before drilling in
Agent BuilderAgent builder instructions and build resourcesBuilding a skill, agent, or automation from your processes

Before you call

Read Operating principles — the conventions for staying grounded, iterating on search, separating observed from inferred, and avoiding common mis-uses (treating search_* snippets as final answers, exposing internal IDs in user-facing prose).

What the assistant should do with results

A human using the Advisor might use two or three tools per question. An assistant in an agentic loop can chain ten or more — searching, drilling in, cross-referencing, synthesizing. The whole point of the MCP is that composition is the value. See How-to guides for end-to-end recipes.