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timps_log_pattern_analyzer

Cluster production log errors to identify root cause and suggest fixes, handling stack traces, JSON logs, syslog, and journald output.

Instructions

Cluster production log errors, identify root cause, suggest a fix. Handles stack traces, JSON logs, syslog, and journald output.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestNoPlain-English task or context for the agent.
languageNoPrimary programming language (default: python).python
Behavior2/5

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

No annotations are provided retical. The description mentions 'suggest a fix' but does not clarify side effects (e.g., is it read-only? does it modify logs?), required access permissions, or output format. For a tool that analyses logs)Skip, the lack of explicit non-destructive disclosure is a notable gap.

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 extremely concise: two short sentences that clearly communicate capabilities. Front-loaded with the primary function, and the second sentence enumerates supported log formats. No wasted words.

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?

For a tool with no output schema and no annotations, the description leaves out expected return format and any side effects (e.g., whether it writes or only analyzes). It mentions input types (stack traces, JSON logs, syslog, journald) but not how the request parameter is interpreted. Given the simplicity of the tool (2 params) and no output schema, a moderately informative description is acceptable, but some behavioral details (e.g., whether it requires prior setup) are missing.

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 coverage is 100% (both 'request' and 'language' are described). The description adds minimal semantic detail beyond the schema—it implies the tool processes logs based on the request, but does not explain how the 'language' parameter influences analysis. Baseline 3 is appropriate.

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 clearly states the action (cluster log errors, identify root cause, suggest fix) and the input scope (production log errors). It doesn't explicitly distinguish from sibling tools like timps_log_interpreter or timps_log_detective, but the purpose is specific and actionable.

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 is provided on when to use this tool versus the many sibling log-related tools (e.g., timps_log_detective, timps_log_interpreter). The description does not mention prerequisites, typical scenarios, or trade-offs, leaving the agent to guess when this tool is preferred.

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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