GridHub Electricity Market Data

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds substantial behavioral detail beyond them: the oldest-first ordering with limit truncating from the OLDEST end (a genuine gotcha), per-call window limits, metric-specific availability caveats (generation limited to GB and US-CAISO, carbon-intensity to GB, capacity to European zones), and free sample-mode truncation at 50 rows.

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 long but every sentence earns its place. The core purpose and the critical truncation gotcha are front-loaded, followed by defaults, limits, metric semantics, and auth — each piece is operational for making a correct call. There is no filler or repetition of the schema's plain parameter text.

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 explaining return behavior, and it covers ordering, truncation, units, per-zone availability, pagination, and authentication modes thoroughly. The only gap is that it never specifies the exact output row shape/field names (beyond mentioning the 'fuel' field), so an agent cannot fully predict the response structure before calling.

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%, which sets a baseline of 3, but the description adds meaning the schema lacks: the 'limit' parameter's non-obvious oldest-end truncation behavior, default window semantics per metric type, and per-zone metric availability that the enum alone does not convey. The metric field is enriched with units (local currency per MWh, MW, gCO2/kWh) and the 'fuel' sub-field behavior.

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 first sentence states a specific verb and resource: 'Time series for one metric in one zone over a start/end window (Unix seconds).' It goes well beyond the name/title, and explicitly differentiates itself from siblings by pointing to get_latest / get_zone_brief for current values, so an agent can distinguish it from the alternatives.

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 gives explicit when-to-use guidance: 'for the latest N points set start close to now, or use get_latest / get_zone_brief for current values.' It also documents pagination strategy ('paginate with start/end for more'), default and maximum windows per metric, and which metrics apply to which zones, leaving little to inference.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description goes beyond annotations by disclosing the day-ahead pricing quirk (timestamps up to ~36h in the future), authentication requirements and rate limits, and free sample-mode truncation behavior. The only minor gap is that there is no explicit statement about pagination or output shape, but that is adequately handled by the stated timestamp and unit disclosure and the absence of an output schema.

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 front-loaded with the core scope and use case, then adds a caveat, a sibling pointer, and auth/sample-mode notes in a logical order. It is longer than the minimum, but every sentence earns its place: the day-ahead caveat prevents a real misunderstanding, and the credential guidance affects whether the api_key parameter is required. A small bit of tightening could remove the repeated authentication detail, but it is not bloated.

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?

For a read-only lookup tool with complete schema documentation, the description covers the main agent needs: what data is returned, which sibling to choose instead, the odd timestamp behavior, and the auth/sample-mode fallback. It could additionally note that returned timestamps may vary by metric or explain the exact JSON shape, but those are not necessary for correct selection and invocation of a list-style current-value endpoint.

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%: the zone parameter has a full enum list and a call-to-list_zones note, and api_key has a thorough description of header-vs-argument usage. The description adds valuable context about what metrics are included per zone, but it doesn't materially extend the schema's already complete parameter documentation. Baseline 3 is appropriate when the schema does the heavy lifting.

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 names the exact resource ('most recent value of every metric one zone publishes'), enumerates the metric types per region, and includes a quoted use case that tells the agent when it is the right tool. It differentiates itself from get_zone_brief by noting the day-ahead price caveat, so an agent can distinguish between the two at a glance.

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 says this is 'the right tool for' real-time queries and explicitly tells the agent to use get_zone_brief when a strictly at-or-before-now value with historical context is needed. It also gives credential guidance and explains what happens without credentials, so the agent knows when an api_key parameter is needed versus optional.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior; the description goes well beyond this by disclosing refresh cadence ('Rebuilt every 5 minutes'), authentication options (Bearer key, x402 payment), and free sample mode behavior with truncation and marked results. This is rich behavioral context.

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 front-loaded with purpose and examples, then follows with refresh, auth, and fallback details. It is economical despite length; the only minor issue is some repetition of api_key guidance already present 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 one optional parameter, no output schema, and readOnly annotations, the description covers all essential aspects: purpose, usage, refresh behavior, authentication, and unauthenticated fallback. It doesn't describe return shape, but that's not critical for a straightforward snapshot tool.

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 already documents api_key thoroughly with header preference and sample mode. The description adds complementary practical meaning: rate limits, x402 alternative, and the no-credential fallback, making the parameter more actionable.

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?

Description states 'Current headline values for all 25 zones in one call', giving a specific resource and scope. The examples ('which European zone has the lowest price right now', 'rank US ISOs by demand') make its comparative purpose unmistakable and distinguish it from per-zone or history siblings.

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 gives clear usage context with concrete examples of when to use this tool ('cheapest way to compare zones'). It does not explicitly name alternatives or state when not to use it, so it stops short of full exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the safety profile is well covered. The description adds useful context beyond annotations by specifying per-source/per-zone granularity, the possibility of no error, and that the tool is free to use.

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 three short sentences with no wasted words. It front-loads the core purpose, then adds return-field detail and an actionable usage note, all in an efficient structure.

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?

For a zero-parameter tool with rich safety annotations and no output schema, the description is sufficiently complete: it explains what will be returned, the granularity, and the intended use case. Nothing needed for correct selection or invocation 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?

The tool has zero parameters, so parameter semantics are naturally simple; the baseline for this case is 4. The description correctly mentions the scope ('per source and zone') without inventing irrelevant parameter details.

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 states a specific resource ('ingestion health per source and zone') and details the exact data returned (last successful fetch timestamp and last error, if any). This clearly separates it from the listed siblings, which concern data history, latest values, and zone maps.

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 gives an explicit, concrete use case: 'Use it to explain stale or missing values before drawing conclusions from them.' It provides clear context for when the tool should be consulted, though it does not explicitly state when not to use it or name alternative sibling tools.

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

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

Annotations already mark the tool read-only, idempotent, non-destructive, and open-world. The description goes well beyond that by disclosing data recency ('strictly at-or-before now'), the 30-day ranking window, sample-mode truncation behavior, authentication methods, and the fact that sample results are clearly marked. This is rich behavioral context with no contradiction.

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 longer than average, but nearly every sentence earns its place: core output, use-case guidance, authentication options, and sample-mode caveats. It is front-loaded with the tool's purpose and distinctive value, though the authentication and sample-mode section could be trimmed slightly without losing critical 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?

With no output schema, the description fully compensates by enumerating the return components: current metrics, percentile/median/min/max ranking, sample count and data window, 24h trend, generation mix where published, and a plain-English summary. It also covers authentication, rate limits, sample-mode truncation, and marked sample results, making the tool safely invocable by an 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 description coverage is 100%, so the baseline is 3. The description adds meaningful value by explaining the auth semantics of api_key: prefer an Authorization header, use the parameter only when headers cannot be set, and behavior without a key (free sample mode with truncated output). It also clarifies that zone supports specific regions and that list_zones provides more metadata.

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-plus-resource construction: 'Composite, interpretation-ready snapshot of one zone,' listing exact metrics (price, demand, carbon intensity), ranking context, 24h trend, generation mix, and summary. It clearly differentiates itself from raw data tools by saying 'A raw price means little without this context' and positioning itself as the best tool for current-state questions.

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 explicitly states when to use the tool: 'Best tool for questions like "is electricity cheap/clean in X right now" or "is this a good time to run a flexible workload".' It provides clear context for selection, though it does not explicitly name sibling alternatives or state when not to use the tool, leaving some differentiation to inference.

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

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

Annotations already signal read-only, open-world, idempotent, and non-destructive behavior. The description adds context beyond annotations by stating 'Free, no credentials needed' and enumerating the data sources covered, which helps the agent understand availability and attribution requirements.

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?

Three short sentences, each earning its place: the first states purpose and output, the second covers cost and credentials, the third gives usage direction. Information is front-loaded and there is no redundancy.

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?

For a zero-parameter listing tool, this is complete: it names the output fields, scope, access requirements, and when to invoke it. There is no output schema, but the description provides sufficient expectations for the return value.

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?

The schema declares zero parameters, so no parameter documentation is needed. The description compensates by detailing the output fields (id, name, data source, timezone, currency, licence, attribution), which is the relevant semantic content for this tool.

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?

Description states a specific verb ('List') and resource ('25 electricity grid zones GridHub covers') and names the exact fields returned. It clearly distinguishes from sibling get_* tools by scope: this is the enumeration of all zones, while siblings fetch data for specific zones.

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 guidance is given: 'Call this first if you are unsure which zone id to use.' It implies use before zone-specific tools like get_zone_brief or get_latest, though it does not explicitly name alternatives or state when not to use it.

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