/exploring-mcp-tool-quality

name: exploring-mcp-tool-quality
description: >
  Investigate the quality of PostHog MCP tool calls — error rates, latency,
  reach, and which tools are failing or slow. Use when the user asks "which
  MCP tool has the highest error rate?", "what's the slowest tool?", "which
  tools fail most often?", "how reliable is tool X?", wants a tool-quality
  matrix, or pastes an MCP analytics tool-quality / dashboard URL and asks
  what it shows.

Exploring MCP tool quality

Any MCP server instrumented with PostHog's MCP analytics SDK emits a `$mcp_tool_call` event on the shared `events` table every time an agent invokes a tool. There is **no dedicated ClickHouse table** — every field lives as a `$mcp_*` property on `events`, and every tool-quality metric (error rate, latency percentiles, reach) is an aggregation over this one event. This is the data behind the MCP analytics dashboard and tool-quality screens.

Governed metric first

For any MCP failure-rate headline, call `posthog:metric-list` before a typed tool or SQL recipe and look for `mcp_tool_call_fail_pct`. If it is approved and not drifted, run it with `posthog:data-catalog-metric-run` and use that result as the canonical headline. When the user also asks which tools drive failures, run the headline first, then use the workflows below for the breakdown and label that breakdown noncanonical. If no governed metric matches, state that the catalog has no match and label the derived rate noncanonical.

**For a single tool, prefer the typed tools** — `posthog:query-mcp-tool-stats` (calls, errors, p50/p95, users, sessions, intents), `posthog:query-mcp-tool-failures` (top error messages by harness), and `posthog:query-mcp-tool-daily-stats` (day-by-day trend). Each takes a `toolName` + `dateRange`, runs the same query runner as the tool-detail UI, and is gated behind the `mcp-analytics` flag — no hand-written SQL needed.

**HogQL via `posthog:execute-sql` is the path for cross-tool questions** — the "which tool errors most" ranking below has no typed tool, so rank with SQL, then drill into the worst tool with `posthog:query-mcp-tool-stats` and `posthog:query-mcp-tool-failures`. The full property schema and the established query recipes live in the shared MCP data reference: [`products/posthog_ai/skills/querying-posthog-data/references/models-mcp.md`](../../../posthog_ai/skills/querying-posthog-data/references/models-mcp.md). That reference is the single source of truth for the `$mcp_*` schema and the effective-tool-name idiom used below — this skill inlines only the noncanonical "which tool errors most" breakdown for convenience; pull the matrix, latency, and harness recipes from the reference rather than re-deriving them. Read it before writing queries.

The two rules that matter most

• **Always use the effective tool name.** New-SDK events wrap the real tool in

a single-exec call, so grouping on raw `$mcp_tool_name` collapses everything under the wrapper. Use:

  coalesce(nullIf(toString(properties.$mcp_exec_tool_call_name), ''), toString(properties.$mcp_tool_name))

• **Always read `$mcp_is_error` via `toBool(...)`** and cast

`$mcp_duration_ms` via `toFloat(...)`. The properties are strings.

Always set a time range — these queries scan `events` otherwise.

Workflow: which tool has the highest error rate (noncanonical breakdown)

For the "which tool errors most" breakdown, rank tools by error rate, but guard against small-sample noise with a `HAVING` floor on call volume:

posthog:execute-sql
SELECT
    coalesce(nullIf(toString(properties.$mcp_exec_tool_call_name), ''), toString(properties.$mcp_tool_name)) AS tool,
    count() AS total_calls,
    countIf(toBool(properties.$mcp_is_error)) AS errors,
    round(countIf(toBool(properties.$mcp_is_error)) * 100.0 / count(), 1) AS error_rate_pct
FROM events
WHERE event = '$mcp_tool_call'
    AND coalesce(nullIf(toString(properties.$mcp_exec_tool_call_name), ''), toString(properties.$mcp_tool_name)) != ''
    AND timestamp >= now() - INTERVAL 30 DAY
GROUP BY tool
HAVING total_calls >= 20
ORDER BY error_rate_pct DESC, total_calls DESC
LIMIT 20

Report both **rate and volume** — a 100% error rate over 3 calls is rarely the real story; a 12% rate over 50,000 calls is. Offer to pull the top `$mcp_error_message` values for the worst tool (see below).

Workflow: tool-quality matrix

One row per tool with error rate, latency percentiles, and reach — mirrors the tool-quality screen. The ready-to-run query is in [models-mcp.md](../../../posthog_ai/skills/querying-posthog-data/references/models-mcp.md) under "Tool-quality matrix".

Workflow: why is a tool failing

For one tool's top failure buckets (grouped by harness), call `posthog:query-mcp-tool-failures` with the `toolName` — it's the typed equivalent of the query below. Failures come from the **same source as the error rate**: errored `$mcp_tool_call` events (`$mcp_is_error`), scoped by the effective tool name. Failures are grouped by `$mcp_error_type` (a semantic bucket: `internal`, `validation`, `api_4xx`, `api_5xx`, `permission`, `timeout`, `rate_limited`, `missing_context`) and the HTTP `$mcp_error_status` when present. To see individual errored calls inside a bucket — with the captured `$mcp_error_message`, session id, harness, and intent — pass the bucket's raw `error_type`/`error_status` to `posthog:query-mcp-tool-failure-occurrences` (`$mcp_error_message` is empty on events captured before message capture shipped):

posthog:execute-sql
SELECT
    concat(
        coalesce(nullIf(toString(properties.$mcp_error_type), ''), 'unknown'),
        if(empty(coalesce(toString(properties.$mcp_error_status), '')), '',
           concat(' (HTTP ', coalesce(toString(properties.$mcp_error_status), ''), ')'))
    ) AS failure,
    count() AS n
FROM events
WHERE event = '$mcp_tool_call'
    AND toBool(properties.$mcp_is_error)
    AND coalesce(nullIf(toString(properties.$mcp_exec_tool_call_name), ''