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J-X0

lockwood-group-observability-mcp

by J-X0

triage_logs

Cluster a window of log records by semantic similarity and rank clusters worst-first to prioritize incident triage.

Instructions

Cluster a window of log records by semantic similarity and rank the clusters for incident triage. Returns clusters worst-first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoPath to a JSON-lines file of log records (alternative to 'records').
recordsNoInline log records.
budget_msNoOverride the p99 latency budget for this call.

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations exist, so the description carries the behavioral burden. It usefully discloses that clustering is semantic and that clusters are ordered worst-first, but it does not mention latency budget behavior, error modes, or whether the operation is read-only.

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 followed by a short return-value note. Every phrase adds information, and there is no filler or redundant restating of the tool name.

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

Completeness3/5

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

This is a complex clustering tool with no annotations and no output schema, so the description must do more. It conveys the high-level purpose but leaves important context unspecified, such as how a 'window' is determined, whether path and records are alternatives, and what the returned cluster objects look like.

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 the schema already documents path, records, and budget_ms. The description adds no extra meaning about how these parameters relate to the 'window' of logs, so it stays at the baseline 3.

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 verb and resource: 'Cluster a window of log records by semantic similarity and rank the clusters for incident triage.' It also adds a concrete output behavior, 'Returns clusters worst-first,' which clearly distinguishes it from generic log-processing tools.

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 phrase 'for incident triage' provides clear intended context, and with no sibling tools, explicit alternative routing is not required. It could be more explicit about when not to use the tool, but the context is clear enough for an agent to select it.

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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