jaeger_span_statistics

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read. The description adds rich behavioral context: it aggregates spans by operation name, reports p50/p95/p99 in microseconds, error rate formula, and the return structure. No contradictions with annotations.

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 well-organized: a one-sentence summary, then calculation details, units, examples, exclusions, and return value. Every section earns its place—no wasted words, and the 'Returns' block clarifies expected output even though an output schema exists.

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 (aggregation, multiple metrics, parameter combinations) and the presence of an output schema and annotations, the description is thorough. It covers use cases, parameter effects, output format, and explicitly lists sibling tools for alternatives, leaving little ambiguity for the agent.

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?

Schema coverage is 100% with each parameter described, so the baseline is 3. The description adds value by explaining how 'limit' affects sample size/accuracy and that 'operation' filters traces before aggregation. It also gives parameter-specific examples (e.g., service='order-service'), but it doesn't fully replace the schema's role.

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 opens with a specific verb+resource+scope: 'Compute per-operation latency percentiles and error rates across recent traces.' It clearly distinguishes from siblings by stating 'Don't use when: You want to compare two specific traces (use jaeger_compare_traces instead)' and similarly for jaeger_get_trace.

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?

Explicit 'Use when' and 'Don't use when' sections provide concrete example queries with parameter settings and alternative tool names. This gives the agent clear decision rules for when to select this tool versus siblings like jaeger_compare_traces and jaeger_get_trace.

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