Query Time-Series Data

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable context beyond this: it explains that points are identified by UUIDs, mentions the max limit of 200 point IDs, provides common usage patterns with examples, and describes the return format. While it doesn't detail rate limits or auth needs, it enriches the behavioral understanding significantly.

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-structured and front-loaded with the core purpose. It efficiently uses bullet points for parameters and common patterns, avoiding redundancy. Every sentence adds value, such as clarifying sibling tool relationships and providing usage examples, with no wasted words.

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 (7 parameters, 2 required) and rich annotations, the description is highly complete. It covers purpose, usage guidelines, parameter details, behavioral context, and return values. Although there's no output schema, the description specifies the return format ('Array of series, each with timeseriesId, name, type, unit, equipment, and data records [{t, v}]'), filling that gap 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 100% schema description coverage, the baseline is 3. The description adds meaningful semantics: it clarifies that point_ids are 'comma-separated timeseriesId UUIDs (max 200)', provides default values for optional parameters (e.g., start: '-1h', end: 'now()', aggregate: 'mean'), and gives practical examples like 'Last hour raw: start="-1h" (default)'. This enhances understanding beyond the schema's basic descriptions.

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: 'Query historical or live sensor data for one or more points.' It specifies the resource (sensor data/points), the action (query), and distinguishes it from sibling tools by mentioning that point IDs come from 'tacit_graphql' and that for current/live values, 'tacit_graphql' should be used instead. This provides specific verb+resource differentiation.

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 provides explicit guidance on when to use this tool versus alternatives. It states: 'Use tacit_graphql first to find points and their timeseriesId values' and 'For current/live values, use tacit_graphql with the currentValue { value timestamp quality } field on Point instead of this tool.' This clearly defines prerequisites and exclusions, helping the agent choose correctly.

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