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

Annotations already declare readOnly, idempotent, non-destructive and openWorld=false, so safety and scope are covered. The description adds meaningful context beyond that: results are "dated" (point-in-time), the price and carbon figures are "this hour" (freshness), and it is an aggregate rather than a single-fact lookup. It does not say what happens for an unsupported market name, nor how stale the pipeline figures may be.

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?

Front-loaded: the content inventory comes first, then usage examples, then valid values. Every clause earns its place except the long country roster, which is verbose but justified given there is no enum in the schema.

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 burden of describing returns, and it does so by listing the seven categories of data the caller will receive. It also supplies the full valid-market vocabulary. Remaining gap is structural: no indication of the response shape or error behavior for an unknown market.

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% for the single `market` parameter, so the baseline would be 3. The description exceeds that by enumerating the complete accepted value set — 7 metros and 24 countries — which the schema only hints at with examples and which no enum enforces. This materially reduces the risk of an unsupported market string.

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?

Opens with a specific verb+resource+scope ("Dated overview of a European data-centre market") and then enumerates the concrete contents: project counts ≥500 kW, permitted/building split, top developers, grid connection regime, carbon intensity, power price, rule count. That is far more than a restatement of the name. It stops short of naming the sibling tools that cover single slices (grid_carbon_and_price_now, grid_connection_status, data_centre_rules), so differentiation must be inferred from the composite framing.

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?

Provides explicit trigger phrasing ("Use for questions like 'data centres in Germany', 'Frankfurt data centre market'") which maps naturally onto the single `market` parameter. It gives no when-not-to-use rule or pointer to the narrower sibling tools, so an agent cannot learn from this text that the individual grid/rule tools exist as alternatives.

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