Systemores AI Agent & MCP Security Auditor

Analyze OpenTelemetry Agent Trace Spans

systemores_analyze_trace_telemetry

Read-onlyIdempotent

Ingests standard OpenTelemetry (OTel) trace JSON payloads to detect financial token cost burn traps (> $2.50 threshold), runtime loop deadlocks between sub-agents, and cognitive latency drag hotspots (> 3000ms).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "otelTraceJson": "<otelTraceJson>"
    },
    "name": "systemores_analyze_trace_telemetry"
  }
}

Input Schema

Name	Required	Description
`otelTraceJson`	Yes	JSON string containing OpenTelemetry trace spans or MCP telemetry log data.
`latencyThresholdMs`	No	Latency hotspot threshold in milliseconds.
`maxCostThresholdUsd`	No	Maximum allowable cost in USD per session before triggering burn trap alert.

Output Schema

Name	Required	Description	Default
`rating`	Yes
`traceId`	Yes
`recommendations`	Yes
`estimatedCostUsd`	Yes
`latencyHotspotsMs`	Yes
`totalSpansEvaluated`	Yes
`costBurnTrapDetected`	Yes
`loopDeadlocksDetected`	Yes
`traceContextPropagated`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 13, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "costBurnTrapDetected": {
+      "type": "boolean"
+    },
+    "estimatedCostUsd": {
+      "type": "number"
+    },
+    "latencyHotspotsMs": {
+      "type": "array"
+    },
+    "loopDeadlocksDetected": {
+      "type": "array"
+    },
+    "rating": {
+      "enum": [
+        "OPTIMIZED",
+        "WARNING",
+        "CRITICAL"
+      ],
+      "type": "string"
+    },
+    "recommendations": {
+      "type": "array"
+    },
+    "totalSpansEvaluated": {
+      "type": "integer"
+    },
+    "traceContextPropagated": {
+      "type": "boolean"
+    },
+    "traceId": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "traceId",
+    "traceContextPropagated",
+    "totalSpansEvaluated",
+    "estimatedCostUsd",
+    "costBurnTrapDetected",
+    "loopDeadlocksDetected",
+    "latencyHotspotsMs",
+    "rating",
+    "recommendations"
+  ],
+  "type": "object"
+}

First observedNov 12, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered and the description does not contradict it. The description adds the three detection categories and their default thresholds, which is genuine behavioral context. It says nothing about failure behavior on malformed payloads, payload size limits, or whether findings are summarized versus returned span-by-span.

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

Conciseness4/5

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

A single front-loaded sentence that names the input format first and the three detection outputs second, with no filler clauses. It is dense with domain jargon but every element earns its place. Slight readability cost from cramming three quantitative thresholds into one sentence.

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

Completeness4/5

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

With three parameters fully described, an existing output schema (so return values need not be explained), and rich annotations, the description covers the essentials of what the tool accepts and what it looks for. The remaining gap is operational: no note on expected payload shape constraints or how detection results relate to the sibling audit tools.

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

Parameters3/5

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

Schema description coverage is 100%, so all three parameters are already documented with defaults. The description restates the default values ($2.50 and 3000ms) in prose, which reinforces but does not extend the schema. No additional semantics such as units edge cases, accepted timestamp formats, or what the latency threshold measures end-to-end are added.

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

Purpose4/5

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

The description gives a specific verb and resource chain: it ingests OpenTelemetry trace JSON and detects three named failure classes (cost burn, loop deadlocks, latency hotspots). It is far more specific than the title alone. It stops short of distinguishing itself from siblings like systemores_measure_drag, which plausibly overlaps on the latency-drag analysis.

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

Usage Guidelines3/5

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

Usage is implied by the input domain — you would call this when you hold OTel trace spans or MCP telemetry logs and want anomaly detection. There is no explicit when-to-use statement, no prerequisites (e.g. that the payload must be well-formed JSON), and no routing guidance toward or away from the audit/measure siblings.

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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