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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: the data is "dated" (a snapshot with a timestamp) and the grid carbon intensity / power price are "this hour" values, which tells the agent about freshness and temporal semantics.

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?

Well front-loaded: purpose first, usage examples second, accepted values last. The two long comma-separated value lists consume most of the length, but they carry real disambiguating information given the absence of schema enums, so the size is largely earned.

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?

With no output schema, the description carries the full burden of describing returns and does so thoroughly—it enumerates every category of output the agent can expect. Combined with annotations covering safety and a fully documented single parameter, nothing needed to invoke or interpret the call is missing.

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% and the single parameter is described with examples, so the baseline would be 3. The description goes beyond the schema by enumerating the full set of accepted metros (London, Frankfurt, Paris, Amsterdam, Dublin, Milan, Madrid) and 24 countries, effectively supplying the enum constraint the schema lacks.

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?

It names a specific resource (a European data-centre market snapshot) and enumerates the concrete facts returned: project counts ≥5 MW, permitted/building split, top developers, grid-connection posture, carbon intensity, power price, rule counts. That is far more than a restatement of the title. It stops short of 5 because it never contrasts itself against siblings like grid_carbon_and_price_now or data_centre_rules that cover overlapping slices.

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?

"Use for questions like 'data centres in Germany', 'Frankfurt data centre market'" gives the agent concrete trigger phrasings. However, there is no when-not guidance and no pointer to the alternative single-facet tools, so the agent must infer that this is the aggregate/market-wide choice rather than the per-topic ones.

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