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Python
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login_storm_analysis

Detect login storms by grouping concurrent logins into storm episodes, then rank slow login and boot contributors to pinpoint endpoints and users causing delays.

Instructions

[READ] Detect login storms + rank the slowest login/boot contributors.

Answers "is this a morning login storm, and which endpoints/users are dragging login and boot times?" Groups logins into storm episodes (>= min_concurrent within a window_s sliding window) and flags sessions slower than the thresholds — every flag is reported with its number, not a verdict. Pass 'sessions' for pure analysis, or a target to pull live via session_list.

Args: since_hours: Live look-back window when sessions is omitted (default 24). window_s: Sliding window that defines "concurrent" logins (default 300). min_concurrent: Logins within a window that constitute a storm (default 10). slow_login_ms: Login duration (ms) flagged as slow (default 30000). slow_boot_ms: Boot duration (ms) flagged as slow (default 90000). sessions: Injected session records — {endpoint, user, login_ms, boot_ms, timestamp (ISO-8601), result}; skips live collection. target: Endpoint-management target name from config; omit for the default. limit: Max rows in each returned list (default 25).

Returns dict: {totalSessions, sessionsReceived, inputTruncated, stormCount, storms:{items:[{start, end, count, peakConcurrent, spanS, distinctUsers, distinctEndpoints, avgLoginMs}], returned, limit, truncated}, slowLoginCount, slowestByLogin:{items[], ...}, slowestByBoot:{items[], ...}, failedLogins, thresholds}. Each list is a truncation envelope — when its 'truncated' is true, re-run with a higher limit; stormCount is the full episode count.

Example: login_storm_analysis(sessions=[{"endpoint":"tc01","user":"a", "login_ms":42000,"timestamp":"2026-07-12T08:00:00Z"}, ...]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
targetNo
sessionsNo
window_sNo
since_hoursNo
slow_boot_msNo
slow_login_msNo
min_concurrentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.3
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 25,
+  "title": "Limit",
+  "type": "integer"
+}
  2. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It declares '[READ]', explains that every flag is reported as a number rather than a verdict, describes storm grouping semantics, threshold behavior, truncation envelopes, and when live collection is skipped. This gives an agent a reliable model of what the tool does and does not do.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a summary, then organized into Args, Returns, and Example sections. Every sentence provides necessary information about behavior, parameters, or output structure, with no filler or repetition of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description correctly explains the full return dict, list truncation envelopes, and how to interpret stormCount. A concrete example is included. The only minor ambiguity is precedence when both sessions and target are provided, but the 'or' phrasing sufficiently signals that they are alternative modes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does. All 8 parameters are documented with defaults, meaning, and interactions: since_hours applies when sessions is omitted, target triggers live collection, and sessions must contain specific fields. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it detects login storms and ranks the slowest login/boot contributors. It answers a concrete question and is clearly distinct from sibling tools like endpoint_list or endpoint_reboot because it is a pure aggregation and analysis operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit guidance on input modes: pass 'sessions' for pure analysis, or use a target to pull live data via session_list. It also frames the tool around the question 'is this a morning login storm...'. It does not explicitly list when not to use this tool relative to siblings, but the decision context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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