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Tail a log file

tail_log

Retrieve the last N lines of a log file. Optionally filter by case-insensitive text to isolate relevant entries.

Instructions

Read the last N lines of a log file under the configured log root, optionally filtered to lines containing a substring (case-insensitive).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesPath relative to the configured log root, for example nginx/error.log
linesNoHow many lines to return (default 50)
containsNoOnly return lines containing this text
Behavior3/5

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

With no annotations, the description carries the burden of disclosing behavioral traits. It explicitly says 'Read' (implying a safe, non-mutating operation) and mentions the 'configured log root' and case-insensitive filtering. However, it does not describe error behavior, return format, or what happens if the path is invalid, which are important for a tool with no structured metadata.

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, concise sentence that front-loads the primary behavior and includes optional parameters. Every word adds meaning, with no fluff or repeated information from the schema.

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?

Given the tool's low complexity, the rich schema descriptions, and the absence of an output schema, the description provides sufficient context for core usage. It does not specify the return format (e.g., array of strings vs. single string), but this is a minor gap for a straightforward log tail operation.

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

Parameters4/5

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

Schema coverage is 100% with clear descriptions for all three parameters. The tool description adds extra value by noting that the 'contains' filter is case-insensitive and that the path is relative to the configured log root, which is not evident from the schema alone. This goes beyond the baseline for well-documented schema.

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 clearly states the tool reads the last N lines of a log file under a configured root, with optional case-insensitive substring filtering. This is a specific verb ('Read') and resource ('log file'), and it is distinct from sibling tools like check_endpoint or runbook_search.

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 implies when to use the tool—whenever recent log output is needed. It clearly defines the scope (log root, last N lines) but does not explicitly discuss when not to use it or mention alternatives, though siblings are unrelated and no exclusion is necessary.

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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