Log Analyzer MCP

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

The description adds valuable behavioral context beyond annotations: it explains what gets detected (specific trace ID formats), how entries are grouped, and what the output contains (trace ID types, entry counts, time spans, error indicators). While annotations cover safety (readOnlyHint=true, destructiveHint=false, idempotentHint=true), the description provides operational details about the tool's analysis behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with a clear purpose statement, detailed parameter explanations in an Args section, and a Returns section. Every sentence adds value: the first explains what the tool does, the second adds detection specifics, and the parameter/return sections provide essential usage information without redundancy.

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

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

Given the tool's complexity (trace ID detection, grouping, flow analysis) and the presence of an output schema, the description is complete: it explains the analysis approach, documents all parameters with semantics, describes the return structure, and provides enough context for an agent to understand when and how to use this tool effectively.

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

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

With 0% schema description coverage, the description compensates well by explaining all 5 parameters in the Args section, including their purposes, constraints (ranges for max_traces and max_lines), defaults, and the response_format options. It adds meaningful context about what each parameter controls in the analysis process.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Extract and follow trace/correlation IDs across log entries' with specific details about detecting trace IDs (OpenTelemetry, X-Request-ID, AWS X-Ray, UUID) and grouping related entries to show request flows. It distinguishes from siblings by focusing specifically on trace/correlation ID analysis rather than general log analysis.

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

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

The description implies usage context through its focus on trace/correlation ID analysis, but doesn't explicitly state when to use this tool versus alternatives like log_analyzer_search or log_analyzer_correlate. There's no guidance about prerequisites or when-not-to-use scenarios, leaving the agent to infer appropriate usage from the purpose statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.