analyze-mcp-server

name

analyze-mcp-server

description

Analyze MCP server usage instrumented with Amplitude's MCP Analytics SDK: break usage and errors down by tool, read the rationales within each tool to see what callers are trying to do, and produce a prioritized write-up of actionable fixes. Use this skill whenever the user asks to understand how their MCP server is being used, what agents/users are trying to do with it, why tool calls are failing, what to fix or improve in their MCP server, or asks for an "MCP usage report", "tool error analysis", "intent analysis", "rationale clustering", or "MCP insights". Also trigger when the user mentions [MCP]-prefixed events, tool rationale, tool call errors, or just finished instrumenting their MCP server and wants to see what the data says. Requires the Amplitude MCP connector.

Analyze MCP Server — Intent & Error Analysis

Produce a report that answers three questions about an MCP server instrumented with Amplitude's MCP Analytics SDK:

1. Which tools are used, and which ones fail? (per-tool volume + error rate)
2. What are callers trying to do with each tool, and where does each intent break? (rationales within a tool × error message)
3. What should the team fix first? (fixes grounded in error evidence, not vibes)

The core move that makes this analysis valuable: tool locates the fix; rationale explains the intent behind it. An overall error rate says "4.5% of calls fail." Broken down by tool it says "query_chart fails 22% of the time" — now you know what to open. Adding the rationales within that tool says "the failures are dashboard-refresh agents hitting the batch-size limit" — now you know why, and the fix writes itself. Rationale is largely tool-scoped (each tool has its own reason-for-calling), so tool is the primary axis and rationale is the second cut inside it — clustering rationales globally just re-derives the tool boundaries while mixing incomparable intents.

Prerequisites

• Amplitude MCP connector active. If it isn't, stop and ask the user to add the Amplitude connector in their tools/connector settings — there is no fallback data source.

• A project containing the SDK's events. The SDK emits SDK-owned default events, all prefixed [MCP], and — critically — every default property they carry is also [MCP]-prefixed, they might have custom properties.

  Events:

  • [MCP] Tool Call Response — the workhorse: one event per tool invocation that reached a tool callback (success or error).
  • [MCP] Tool Call Rejected — a tools/call that failed before any tool ran (unknown/ disabled tool, input-schema validation). This is where hallucinated/typo'd tool names land, carried on [MCP] Attempted Tool Name (kept off the reserved [MCP] Tool Name).
  • [MCP] Session Initialized, [MCP] Session Ended, [MCP] Tools Listed.

  Key properties on [MCP] Tool Call Response: [MCP] Tool Name, [MCP] Client Name, [MCP] Client Version, [MCP] Session ID, [MCP] Auth Type, [MCP] Rationale, [MCP] Is Error, [MCP] Error Message, [MCP] Error Type (returned_error vs thrown_exception — separates a tool that reported failure from one that crashed), [MCP] Response Duration, [MCP] Response Size, [MCP] Request Size.

• Event/property names can still drift between SDK versions. Never assume — verify with the taxonomy tools in Phase 0. The canonical names above come from the SDK's constants.ts (EVENT_PROPERTY_KEYS); if the project's data disagrees, trust the project and adapt.

Phase 0 — Locate and validate the data

1. Call the Amplitude context tool (no projectId) to list projects. If more than one plausible project exists and the user hasn't specified, ask.
2. Confirm the [MCP] events exist in the chosen project (search for events or list events; get_events filters by exact name only, so use search for the [MCP] prefix discovery).
3. Pull event properties for [MCP] Tool Call Response (and [MCP] Tool Call Rejected) to confirm the exact property names — remember they are [MCP]-prefixed and Title Cased (e.g. [MCP] Rationale, not tool rationale). Bind whatever you find to the names used in the queries below.
4. Run one volume/recency query: totals of [MCP] Tool Call Response, [MCP] Session Initialized over the last 60 days, weekly interval. This tells you the usable date window and whether there's enough data. Under ~5k tool calls total, warn the user that per-tool breakdowns will be thin and percentages unstable — still proceed, but say so in the report.

Record: project ID, date window, total call volume. Every later query uses the same range so numbers stay comparable.

Phase 1 — Break down by tool (the spine)

This is the primary map; everything else drills into it.

• Volume by tool: totals of [MCP] Tool Call Response, grouped by [MCP] Tool Name, groupByLimit ~100.
• Errors by tool: same, plus filter [MCP] Is Error is "true".
• Rejected calls by attempted name: totals of [MCP] Tool Call Rejected grouped by [MCP] Attempted Tool Name. These never reach a callback, so they are invisible to the two queries above — but a high-volume attempted name that isn't a real tool is one of the most actionable findings there is (a rename or hallucination agents keep guessing).

Compute per-tool call volume, error count, and error rate, and rank tools two ways: by call volume (what's load-bearing) and by error volume (what's costing the most failures). Pick the top 3–5 tools for deep-dive — bias toward high error volume, not just high traffic, and honor the user's choice if they named specific tools. A low-traffic tool with a 90% error rate can outrank a busy healthy one.

Phase 2 — Rationales within each top tool (the "why")

For each top tool, sample its rationales — totals of [MCP] Tool Call Response grouped by [MCP] Rationale, filtered to that one tool ([MCP] Tool Name is "<tool>"), groupByLimit ~150. Then read them and group by what the caller is trying to accomplish with this tool, not by wording. Guidance:

• Within a single tool you'll usually find 2–5 intent groups, not nine — the tool already narrows the purpose. Name them by intent (e.g. for a query_chart tool: "live dashboard refresh", "ad-hoc exploration", "scheduled report generation").
• Weight by call volume (each rationale value carries its total), not by distinct strings.
• Watch for a junk group: "placeholder", "ignore", "noop", "test", "dummy", single letters. Its size is a rationale-hygiene finding, and junk must not contaminate real intents.
• Rationales like "Retry after rate limit" / "Retrying X" signal retry loops — note them.
• Non-English rationales are normal; group them by meaning.
• Compute rationale coverage per tool (share of the tool's calls with a non-empty rationale). If it's low (<50%), the intents describe only a slice — say so, and "improve rationale coverage at instrumentation time" becomes a recommendation itself.
• Compute top-intent share per tool (the largest intent group as a % of the tool's rationale-covered calls). This is the concentration signal: ~90% means the tool serves one job and you can optimize hard for it (tighten the schema/description around that path); a flat spread (top intent ~30%) means the tool is overloaded and may deserve splitting, or its errors will be spread across unrelated intents. Report the number and, when the top intent is dominant, name it next to the tool.

Cross-tool workflows (secondary): some rationales describe a multi-tool goal ("resolve chart id before refreshing the dashboard"). Where a single intent clearly spans tools (e.g. search_events → create_chart → create_dashboard), call it out as a workflow in the cross-cutting section — it's the one thing a pure tool-by-tool view misses.

Phase 3 — Error drill-down per tool

For each top tool, run (filtered to that tool + [MCP] Is Error is "true"):

• grouped by [MCP] Error Message → why it fails
• optionally split by [MCP] Error Type (returned_error vs thrown_exception) when a message is ambiguous — a crash and a well-formed refusal both raise [MCP] Is Error, but only one is a bug in the tool.

Where a tool has a dominant intent group from Phase 2, qualify the errors by rationale (add the rationale-contains filter) to get the "this intent breaks this way" sentence.

Classify each significant error message into an archetype — the archetype determines the fix:

Archetype	Signature	Fix pattern
Stale/hallucinated tool name	[MCP] Tool Call Rejected with [MCP] Attempted Tool Name = a renamed or plausible-sounding tool	Server-side aliases for renamed tools; rejection responses that name the closest real tool so agents self-correct in one shot
Missing required param	validation error, "Required", path names a param	Loosen the schema (accept single value or array, add defaults), or make the tool description state requirements so agents get it right pre-flight
Schema shape mismatch	"Expected object, received string" etc.	Add coercion server-side, or an inline example in the tool description
Hard limit collision	"Maximum of N …", "too_big"	Raise the limit, auto-chunk server-side, or state the limit in the parameter description — agents currently only learn limits by failing
Range/retention violation	"time range too large", "does not retain data before …"	Auto-coerce (e.g. downgrade granularity) instead of erroring; advertise retention/range limits in the context payload
Chicken-and-egg param	a context/discovery tool requires the very ID it exists to provide	Make discovery tools work with zero required args, defaulting from the auth token
Permission denied	role/permission language	Distinct error class so "not allowed" is separable from "broken"; often also a docs/onboarding gap
Recurring scheduled error	same message repeating daily on the same tool	Fix the job's caller if internal; for the product, flag as silently-degrading automation — nobody is watching a chat window
Transient/upstream	5xx, timeouts, rate limits	Retry-after hints in the error body; check whether retries actually happen (see loop funnel)

The best error messages state the fix ("re-run with start >= …"). Hold every high-volume error message to that standard — "make this error as actionable as your best one" is often a legitimate recommendation.

Phase 4 — Optional context metrics

Add if time permits; they sharpen the story:

• Repeat-error loop: 3-step funnel, each step [MCP] Tool Call Response with [MCP] Is Error is "true", holding [MCP] Tool Name constant, ~1h conversion window. Shows what share of erroring users spiral into loops on the same tool. (Steps need not be consecutive events — say "3+ errors on the same tool within an hour", not "3 consecutive".)
• Error → recovery: 2-step funnel, error then success, holding tool name constant. High recovery + high loop rate together mean the cost is wasted tokens/latency, not abandonment — a materially different conclusion.
• Client mix: uniques grouped by [MCP] Client Name. Large "unknown" share or near-duplicate client names (casing, plugin-prefixed variants) are instrumentation findings worth reporting.

Phase 5 — Verify, then write

Before writing, re-verify at least one headline number a second way (e.g. overall error rate via a separate two-event totals query). Recompute every percentage you quote from raw totals. If a number can't be verified, cut it or label it an estimate.

Report structure

Use this template — organized tool-first:

# MCP Server Analysis — <server/project name>

Data: <project>, <date window>, <N> tool calls, overall error rate <X>%.
Method note: tool breakdowns are exact query results; within-tool intents are model-derived
from rationales (directional). Rejected-call names come from [MCP] Tool Call Rejected.

## Tools at a glance
<table: tool | calls | errors | error rate | top intent (share %) | rationale coverage>
<one line on rejected/attempted tool names if material; call out junk rationales if material>

## Deep dive: top 3–5 tools (ranked by error volume)
For each tool:
**Tool: <name> — <calls>, <error rate> — what it's for: <one-sentence>**
- Intents (rationales within this tool): <group : share>, …
- 1–4 numbered recommendations. Each states the evidence (error message + count),
  the archetype, and the concrete fix. Lead with the fix that removes the most error volume.

## Cross-cutting findings
<cross-tool workflows, loop/recovery rates, client-name hygiene, rationale hygiene, internal test traffic, …>

## The one-sentence takeaway
<the single change that removes the most failure volume, with its number>

Style: numbers next to every claim; name the error messages verbatim (truncated) so engineers can grep for them; recommendations phrased as actions ("Ship alias for query_chart → query_charts"), not observations ("tool naming could be improved").

Charts in the output

Render supporting Amplitude charts where the client supports it, using the ad-hoc chart query + render tools (build an eventsSegmentation/funnels definition, then render it). Be deliberate about what can and cannot be a chart:

• Chartable — anything query-backed. These are real Amplitude segmentation/funnel results, so render them: calls and errors by tool (grouped by [MCP] Tool Name), error rate over time, rejected calls by [MCP] Attempted Tool Name, the per-tool repeat-error loop and recovery funnels, and client mix by [MCP] Client Name. The tool breakdown is the natural hero chart.
• Not chartable — the within-tool intent groups. Grouping rationales into intents happens in this model; those groupings don't exist as an Amplitude property, so there's no saved chart for "calls by intent." Present intents as a table built from the rationale totals, and say plainly that intent figures are model-derived (directional) while the charts you render are exact query results. Don't imply a rendered chart backs the intent grouping.

Query Cookbook

All queries are eventsSegmentation or funnels definitions against the project ID from Phase 0. Adjust event/property names to whatever Phase 0 verified. Shapes that are known to work:

Tools breakdown (Phase 1):

{"type":"eventsSegmentation","app":"<PROJECT_ID>","name":"[MCP] Calls by Tool",
 "params":{"range":"Last 30 Days",
  "events":[{"event_type":"[MCP] Tool Call Response",
    "group_by":[{"type":"event","value":"[MCP] Tool Name"}]}],
  "metric":"totals","countGroup":"User","groupBy":[],"interval":30

Errors-by-tool: add {"subprop_type":"event","subprop_key":"[MCP] Is Error","subprop_op":"is","subprop_value":["true"]} to filters. groupByLimit ~100.

Rejected calls (Phase 1): totals of [MCP] Tool Call Rejected grouped by [MCP] Attempted Tool Name — catches hallucinated/renamed tool names that never fire an error on [MCP] Tool Call Response.

Rationales within a tool (Phase 2):

{"type":"eventsSegmentation","app":"<PROJECT_ID>","name":"[MCP] Rationales for <tool>",
 "params":{"range":"Last 30 Days",
  "events":[{"event_type":"[MCP] Tool Call Response",
    "filters":[
      {"subprop_type":"event","subprop_key":"[MCP] Tool Name","subprop_op":"is","subprop_value":["<tool>"]},
      {"subprop_type":"event","subprop_key":"[MCP] Rationale"
    
  

Use groupByLimit ~150. Set the time-series limit to 0 or low — only totals matter. Drop the is not (none) filter once to measure that tool's rationale coverage.

Per-tool errors (Phase 3): same shape, ONE event, [MCP] Tool Name is "<tool>" + {"subprop_key":"[MCP] Is Error","subprop_op":"is","subprop_value":["true"]}, group_by [MCP] Error Message (or [MCP] Error Type). Add a [MCP] Rationale contains [...] filter to qualify errors by intent.

Loop funnel (Phase 4):

{"type":"funnels","app":"<PROJECT_ID>","name":"[MCP] Repeated Error Loop",
 "params":{"range":"Last 30 Days",
  "events":[<error-filtered Tool Call Response> x3],
  "cs":3600,
  "holdingConstant":[{"type":"event","value":"[MCP] Tool Name"}],
  "countGroup":"User","segments":[{"conditions":[]}]}}

Recovery funnel: 2 steps, second step filter [MCP] Is Error is not true. Read the "Total" row of funnel output; daily rows are often zero-filled and misleading.

Known pitfalls:

• Every SDK property is [MCP]-prefixed and Title Cased — [MCP] Tool Name, [MCP] Rationale, [MCP] Is Error, [MCP] Error Message, [MCP] Response Duration, [MCP] Response Size. Bare names won't match.
• Group-by values only render when a single event in the definition carries the group_by. Put only ONE grouped event per query; volume/error-count totals (no group-by) can share one.
• propavg is not a valid metric; property aggregations use sums/value_avg with the property in the event group_by. Skip property aggregations if they fight back — they are not core to this analysis.
• High-cardinality group-bys ([MCP] Session ID) will not work; don't try.
• Free-text properties ([MCP] Rationale, [MCP] Error Message) can be long — keep groupByLimits modest and let volume ranking do the work.