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originchain-ai

@originchain/telemetry-mcp

Official

incident_analysis

Analyze compound incidents by fusing semantic log clusters, latest occurrences, causal traces, service graphs, and rate anomalies to answer complex questions with cited evidence.

Instructions

FLAGSHIP: one call fuses semantic cluster + latest occurrence (with the real log line) + trace causal chain + service graph + rate anomaly, cited. Use for compound incident questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
query_textYes
window_minNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that results are cited and include real log lines, which is useful output behavior. However, it does not state whether the operation is read-only, how expensive it might be, or any side effects, leaving an agent to infer these from the word 'analysis.'

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 a single dense sentence with every clause adding a distinct element: semantic cluster, occurrence, trace, graph, anomaly, citations. It is front-loaded with 'FLAGSHIP' and contains no filler.

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

Completeness2/5

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

For a compound tool with no output schema, no annotations, and 3 parameters, the description leaves too much unspecified: return structure is only hinted at via 'cited,' parameter semantics are absent, and there are no operational caveats or guidance on result interpretation. It is not complete enough for an agent to use it reliably without additional context.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain query_text, k, or window_min. It is implied that query_text is the compound incident question, but k and window_min are entirely undocumented, so an agent cannot infer how to set them correctly.

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 names a specific capability: one call fuses semantic clusters, latest occurrence with the real log line, trace causal chains, service graph, and rate anomalies. This distinguishes it from the more focused sibling tools (log_volume, top_patterns, etc.) by framing it as the compound analysis tool.

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?

The description explicitly says 'Use for compound incident questions,' giving a clear when-to-use signal. It does not explicitly name alternatives or state when-not-to-use, but the contrast with single-aspect sibling tools is clear enough to guide an agent.

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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