Tool reference - Klarity MCP

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.

Group	What it covers	When to start here
Standard entry points	`search`, `fetch`	The default starting path for any process question
Process Index	Hierarchy navigation, process details, ranked search	`search` results feel sparse, or you need richer metadata than `fetch` returns
Process observations	Recent changes, observations, activity timelines, sessions	”What changed?”, “what happened?”, “why does this run this way?”
Artifacts	Document search, content retrieval, in-document text search	The user names a document or recording
Workspace	List accessible workspaces	Orienting before drilling in
Agent Builder	Agent builder instructions and build resources	Building 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.