Exploring MCP intent clusters

Intent clustering takes the free-text $mcp_intent values agents attach to their tool calls, embeds them, and groups semantically similar goals into clusters. Each cluster carries its tool distribution, call counts, and error rates — answering "what are people _trying_ to do, and does it work?" rather than "which tool was called".

Unlike tool quality and sessions (which are plain HogQL over $mcp_tool_call), clustering needs embeddings and is not expressible in SQL. It is served by two typed tools backed by a stored snapshot.

Tools

Workflow: read the current clusters

posthog:mcp-analytics-intent-clusters-retrieve
{}

Returns a snapshot with status, last_computed_at, computed_with (the embedding model and clustering parameters), and a clusters array. Each cluster has a label, intent_count, call_count, error_count, error_rate_pct, routing_entropy, a tool_distribution (which tools that goal routes to, with per-tool error rates), and sample_intents.

Read clusters by call_count for "what are agents mostly doing", or by error_rate_pct for "which goals are failing" — a high error rate on a cluster points at a class of agent goals the tools serve badly.

routing_entropy is how spread-out a cluster's tool usage is: low entropy means one goal reliably maps to one tool; high entropy means agents are casting around for the right tool for that goal (often a missing-capability signal).

Workflow: handle an empty or stale snapshot

• Empty / idle with no clusters (status: idle, clusters: []): no run has

happened yet. Trigger one (below) and tell the user it computes in the background.

• Stale `last_computed_at`: offer to recompute.

Workflow: recompute

posthog:mcp-analytics-intent-clusters-recompute
{}

Returns immediately with status: computing (HTTP 202); the work runs in the background. Poll ...-retrieve until status returns to idle (done) or error. Don't block waiting — tell the user to re-ask in a minute.

Constructing UI links

• Intent clustering: https://app.posthog.com/project/<project_id>/mcp-analytics/intent-clustering

Tips

• Clusters are only as good as the $mcp_intent coverage — if few calls carry

an intent, clusters will be sparse; cross-check intent coverage with a quick countIf(toString(properties.$mcp_intent) != '') over $mcp_tool_call

• A cluster with high error_rate_pct plus high routing_entropy is the

strongest "the tools don't serve this goal well" signal — worth a closer look at its sample_intents and tool_distribution

• Recompute is throttled to one run at a time per project; a 202 while already

computing just re-confirms the in-flight run

Related skills

• exploring-mcp-tool-quality —

per-tool error rates and latency

• exploring-mcp-sessions — the individual

runs behind the intents