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.Documentation Index
Fetch the complete documentation index at: https://developers.klarity.ai/llms.txt
Use this file to discover all available pages before exploring further.
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, workspace lookup | search results feel sparse, or you need richer metadata than fetch returns |
| Process observations | Recent changes, observations, activity timelines | ”What changed?”, “what happened?”, “why does this run this way?” |
| Visualization | Diagram generation | The user wants to see the flow |
| Artifacts | Document search, content retrieval, video frames | The user names a document or recording |
| Context Graph | Entities, communities, relationships, lineage | The question is relational |
| Objectives | Advisor objective findings, actions, agent state | The user is continuing in-flight transformation work |
| Workspace | List workspaces, switch active workspace, attribute configs | Orienting before drilling in, or switching context |
| Schema & web | Database schema, raw SQL, web research | Last-resort fallback for analytics or external context |
Before you call
Read Operating principles — the conventions for staying grounded, iterating on search, separating observed from inferred, and avoiding common mis-uses (treatingsearch_* snippets as final answers, exposing internal IDs in user-facing prose, calling switch_mcp_workspace without permission).