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Systemores Cloud MCP Server

Systemores Cloud MCP Server

by bobcar0
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

Analyze OpenTelemetry Agent Trace Spans

systemores_analyze_trace_telemetry
Read-onlyIdempotent

Analyze OpenTelemetry trace data to detect token cost burn, sub-agent loop deadlocks, and latency hotspots, helping teams diagnose performance and spending issues in MCP workflows.

Instructions

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

NameRequiredDescriptionDefault
otelTraceJsonYesJSON string containing OpenTelemetry trace spans or MCP telemetry log data.
latencyThresholdMsNoLatency hotspot threshold in milliseconds.
maxCostThresholdUsdNoMaximum allowable cost in USD per session before triggering burn trap alert.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.3

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds real behavioral content by naming the three detection classes, but the >$2.50 and >3000ms thresholds simply restate the schema defaults rather than adding new information. It says nothing about what the analysis returns, which matters because there is no output schema.

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 opens with the verb and resource before listing detection targets, with no filler or redundant restatement of the title. The triple-clause detection list is dense but readable, and nothing could be removed without losing a detection class.

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 only three parameters, 100% schema coverage, and annotations covering the safety profile, the remaining burden is describing the analysis itself, which the description does by naming the three finding types. The notable gap is that there is no output schema and the description never says what the results look like — findings, scores, or alerts — which an agent would want before invoking a diagnostic tool.

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 (otelTraceJson, latencyThresholdMs, maxCostThresholdUsd) are already documented with defaults. The thresholds mentioned in the description map directly onto those schema fields and add no syntax, format, or validation detail beyond them. Baseline 3 applies when the schema does the heavy lifting.

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?

Uses a specific verb ('Ingests') with a specific resource ('standard OpenTelemetry (OTel) trace JSON payloads') and enumerates the three findings it produces: cost burn traps, loop deadlocks, latency hotspots. This is far more concrete than a generic 'analyze' tool. It stops short of naming a sibling to contrast with, so an agent must infer differentiation from the sibling names alone.

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 only implied: the agent can infer it should call this when it holds OTel trace JSON or MCP telemetry logs. There is no explicit when-to-use statement, no exclusion of alternatives (e.g. systemores_measure_drag, which sounds adjacent), and no stated prerequisite about the input format beyond 'standard OTel'.

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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