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kubeopsai

k8s-telemetry-mcp

by kubeopsai

search_traces

Search distributed traces by service, operation, tags, duration, or time window to pinpoint failures and analyze request paths in your Kubernetes observability stack.

Instructions

Search for distributed traces in Tempo by service, operation, or tags.

Args: service_name: Filter by service name operation: Filter by operation name tags: Filter by span tags (key-value pairs) min_duration: Minimum trace duration (e.g. '100ms', '1s') max_duration: Maximum trace duration timeframe_minutes: How far back to search (1-1440) limit: Maximum traces to return (1-100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
limitNo
operationNo
max_durationNo
min_durationNo
service_nameNo
timeframe_minutesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv1.2.6

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral context. It makes the read-only search intent clear and exposes duration/timeframe/limit filters, but it doesn't mention result ordering, pagination, combination semantics of filters, or any operational caveats. This is adequate but not thorough.

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?

Purpose is stated in one sentence, then the parameter semantics are listed in a scannable Args block. Each line adds distinct information and the duration example '100ms', '1s' is efficient and useful.

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?

All seven parameters are explained and the output schema exists, so return shape is covered elsewhere. The main gap is the lack of when-to-use guidance versus sibling tools, but for invoking search_traces correctly the semantics are adequately specified.

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 carry parameter meaning. It provides a concise explanation for every parameter, including examples for min_duration and ranges for timeframe/limit, going well beyond the schema's bare titles and defaults.

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?

Description opens with a specific verb and resource: 'Search for distributed traces in Tempo by service, operation, or tags.' This clearly identifies the operation and its filters, though it doesn't explicitly contrast with the sibling get_trace, so it falls short of full sibling differentiation.

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

Usage Guidelines2/5

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

No guidance on when to choose this tool over alternatives such as get_trace or query_logs_custom. The Args block describes how to filter but not the selection context, exclusions, or prerequisites.

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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