Get Power Availability Timeline

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

While annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the description goes far beyond by detailing how the timeline is composed: 'new generation coming online from EIA-860M monthly, split by confidence class (under-construction vs planned vs testing — never blended); scheduled retirements as dated subtractions; LBNL interconnection-queue depth as congestion context (NO delivery dates — the feed has none and most queued MW never completes).' It also discloses the derived number's rule, 'counts ONLY under-construction+testing minus retirements,' and the honesty line about 'supply-side signals, not a load-interconnection promise.' No contradiction 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 dense but not bloated; it front-loads the core function and then provides examples, exclusions, and honesty caveats. Every sentence contributes value, but the length is substantial. It earns a 4, as it is appropriately sized for the tool's complexity but could be tightened slightly without losing essential information.

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?

Despite having an output schema, the description explains critical context for interpreting results: the derived number cumulative_firm_signal_mw, the constraint_coverage field declaring out-of-scope items, and the 'dated, sourced, per-lane-vintaged' nature of responses. It also clarifies the tool's scope limits (one state, supply-side only) and its internal data sources. This is complete for a tool of this complexity.

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 description coverage is 100%, so each parameter is already described in the input schema. The description adds extra meaning beyond the schema, especially for the 'mw' parameter: 'Optional target MW for CONTEXT ONLY — echoed back with an explicit note; never converted into an energize-by date, which this data cannot honestly state.' It also adds that state is 'the timeline grain; a state can span ISOs' and confirms the default for years. This exceeds the baseline but does not dramatically overhaul the semantics, so a 4 is appropriate.

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 begins with 'Power-availability TIMING for one US state — when power gets EASIER, year by year.' This clearly names the tool's verb (get), resource (power availability timeline), and scope (one US state). It also distinguishes from siblings by explicitly naming what it is NOT: 'Do NOT use for the raw project list (get_power_pipeline), live headroom today (get_grid_intelligence), queue survivors (get_refined_queue), or where-to-build ranking (rank_markets / ai_capacity_index) — this answers WHEN, for one state.' This is a model of purpose clarity.

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 when-to-use guidance via example ('Try: get_power_availability_timeline state=OH') and clear exclusions for five sibling tools, stating this tool 'answers WHEN, for one state.' It also explains what data it composes and the honesty limitations, giving the agent a thorough decision basis for choosing this tool over alternatives.

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