Netzhandwerker EU Power Dispatch API

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

With no annotations, the description carries the full transparency burden. It discloses the 402 response status but omits the response body structure, whether this is an error case, and other behavioral details. The GET/POST ambiguity further undermines clarity.

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 a single, front-loaded sentence with no fluff. It is appropriately concise, though the brevity sacrifices clarity, which is reflected in other dimensions.

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 tool with no parameters, output schema, or annotations, the description must fully explain the fallback behavior and context. It fails to do so by mixing GET and POST, not specifying the response format, and not clarifying how it relates to sibling tools.

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 in its schema, so there is nothing for the description to clarify. Baseline score of 4 for zero-parameter tools 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 'GET-Fallback mit maschinenlesbarer 402-Antwort für POST /articles/{id}' indicates a fallback that returns a 402, but it confusingly mixes HTTP verbs (GET fallback for POST). It does not clearly define what the tool does or distinguish it from the sibling 'articles_id_post'.

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?

No guidance is given on when to use this fallback tool versus the normal articles endpoints. The term 'Fallback' implies a secondary role, but no conditions, triggers, or alternative tool references are provided.

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?

With no annotations to rely on, the description must convey behavioral traits such as side effects, authentication, or whether the operation is a read or write. The noun-phrase description does not disclose any of this, and the '_post' suffix introduces ambiguity about HTTP method side effects without explanation.

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 a single short sentence, front-loaded with the core content. It contains no filler or redundant information, earning high conciseness.

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?

The description is underspecified for a tool with no annotations, no output schema, and a sibling tool family. It does not explain how to invoke it, what the response format is, or what the '_post' suffix implies, leaving the agent unable to use the tool correctly based on the description alone.

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 input schema has zero properties, so the schema provides no parameter details. The 0-parameter baseline is 4, and the description does not add confusion about parameters. However, it also does not clarify how the article ID is identified, which is a notable semantic gap, but that is not a parameter semantics issue.

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 'Volltext eines Energie-Fachartikels' (full text of an energy article) indicates the returned resource type but lacks an explicit verb such as 'get', 'create', or 'update'. It fails to clarify the operation, especially given the '_post' suffix in the tool name, making it ambiguous whether this retrieves or submits full text.

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?

No guidance is provided on when to use this tool versus sibling tools like 'articles_id'. There is no mention of alternatives, prerequisites, or context where this tool should be preferred, leaving the agent without decision criteria.

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?

With no annotations, the description carries the full burden. It mentions 'eight-source' and 'raw evidence' as output components, but does not disclose side effects, cost implications of 'buy', authentication needs, or any limitations. The behavioral contract is vague.

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 a single sentence with zero filler, front-loaded with the main purpose. It is extremely concise and well-structured, though this brevity comes at the cost of omitted details.

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 lack of an output schema and minimal parameter documentation, the description is incomplete. It does not explain return format, parameter usage, or how this tool fits among sibling market tools, leaving significant contextual gaps.

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 has 0% description coverage, and the description does not explain the 'query' or 'sources' parameters at all. 'Eight-source' might hint at sources, but no explicit linkage is made, leaving agents without any semantic guidance for the required parameters.

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 clear action ('receive an eight-source market bundle') and identifies the target users (energy-trading agents). However, it does not distinguish the tool from similar siblings like buy_market_brief or summary_today, and the verb 'buy' is unorthodox for an MCP endpoint.

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?

No guidance is provided on when to use this tool versus alternatives. The mention of 'energy-trading agents' is a target audience hint, but there is no explicit context, prerequisites, or 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?

With no annotations, the description carries full burden but only states 'schedules' and lists return fields. It fails to disclose whether the tool is destructive, idempotent, or requires specific permissions. The behavioral impact of scheduling is not addressed beyond the immediate action.

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 two sentences long with no redundant words. It front-loads the core action and lists return values efficiently. However, it could be more structured by grouping elements or adding separation.

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 (6 parameters, nested constraints, no output schema), the description lacks crucial context on parameter relationships, preconditions, and validation rules. The return value list is helpful, but the overall completeness for agent decision-making is low.

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 0%, and the description does not explain any of the six parameters (opportunity_id, power_kw, energy_kwh, deadline, risk_profile, constraints). 'Deadline-bound' weakly hints at the deadline parameter, but no concrete semantics are provided.

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 uses a specific verb 'schedules' and identifies the resource as 'deadline-bound workload into an actionable power window'. It clearly distinguishes from siblings like 'buy_market_brief' and 'find_energy_opportunity' by focusing on scheduling execution rather than buying briefs or finding opportunities.

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 implies usage for scheduling workloads into power windows, but does not explicitly state when to use this tool versus alternatives. No guidance on prerequisites, exclusions, or post-scheduling steps is provided, making the context only inferred.

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?

No annotations are provided, so the description must fully convey behavioral traits. It mentions 'licensed' data and 'freshness and expiry', but does not disclose read-only nature, rate limits, authentication requirements, or what happens on data unavailability. This is insufficient for a tool with zero annotation coverage.

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 a single long sentence that packs many details, but lacks structure or prioritization. It is not front-loaded and would benefit from splitting into clear sentences or bullets. Still, it is not overly verbose.

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 absence of annotations and output schema, the description fails to fully inform an AI agent. It lists returned data fields but does not specify output format, pagination, or error states. The 3 required parameters are not adequately explained. Completeness is inadequate for the tool's 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 0%, so the description must compensate. It does not explain the meaning of 'region' or 'horizon_hours', nor does it detail how 'use_case' values relate to the returned data beyond listing agent types. The description adds minimal semantic value beyond the schema.

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 returns spot-market context with specific data points (prices, volatility, load, etc.) and lists target user types (compute, DePIN, battery, trading agents). However, it does not explicitly differentiate from sibling tools like buy_dispatch_plan or find_energy_opportunity.

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?

No explicit guidance on when to use or not use this tool versus alternatives. The description implies it is for market context retrieval, but fails to specify prerequisites, exclusion criteria, or comparison with siblings.

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?

With no annotations, the description must carry the behavioral burden. It clearly implies a read-only query (no parameters, 'current' value), but it does not disclose specifics such as the response format, units, data source, or update frequency. For a simple no-parameter tool, the description is adequate, but not exhaustive.

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 a single, concise sentence in German that directly states the tool's purpose. No unnecessary words, well-structured for quick scanning.

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 simplicity (no params, no annotations, no output schema), the description sufficiently identifies the key output (CO2 intensity) and context (German electricity mix). It omits potentially useful details like units (e.g., gCO2/kWh) and whether the value is real-time or averaged, but remains adequate for basic selection.

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 the description does not need to explain parameter meanings. The baseline for 0 params is 4, and the description adds no redundant parameter information.

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 provides the current CO2 intensity of the German electricity mix. It uses a specific noun phrase with a clear scope (German electricity mix) and distinguishes itself from sibling tools like renewable_share (renewable percentage) and subscribe_co2_low (low-CO2 alerts).

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 implies this tool is used when current CO2 intensity data is needed, but it does not explicitly state when to use it over alternatives or provide exclusions. No alternative tools are mentioned, so usage guidance is limited to what can be inferred from the description.

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?

With no annotations provided, the description carries the full burden of disclosing behavior. It clearly states that no payment results in a 402 challenge and that a valid USDC payment yields the full NegPrice decision, which are key behavioral traits. However, it does not elaborate on side effects, error conditions, or the exact format of the decision, preventing a perfect score.

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 two concise sentences, front-loaded with the tool's purpose and followed by specific behavioral details. Every word earns its place, with no redundancy or extraneous 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?

Given the lack of annotations and output schema, the description partially compensates by explaining the payment-based behavior. However, it does not describe the structure of the 'NegPrice decision' or provide guidance on how to handle the 402 challenge, leaving some gaps for an agent seeking full context.

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 input schema has zero parameters, so the baseline for parameter semantics is 4. The description correctly does not discuss parameters since there are none. This is appropriate for a parameterless 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?

The description clearly states the tool as a 'Cheapest end-to-end x402 purchase test' and specifies its exact behavior: always returning a 402 challenge without payment and a NegPrice decision after valid payment. This makes the tool's function unambiguous and distinct from siblings that handle actual purchases or data retrieval.

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 implies usage as a cheap test for the x402 purchase flow but provides no explicit guidance on when to use it versus alternatives or when not to use it. No exclusions or alternative tools are mentioned, leaving the agent to infer the intended use case.

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?

With no annotations, the description carries the full burden. It discloses the output components (timing, expected value, confidence, expiry) but omits critical behavioral traits: what 'buy' implies (cost? access?), whether it's a read-only decision, prerequisites, error handling, or limits. This is a significant gap for a decision endpoint.

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?

A single, efficient sentence with no redundant words. It front-loads the key value proposition. However, it is perhaps too terse for the tool's complexity, but it earns a high score for conciseness itself.

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?

The tool has 7 parameters, a nested constraints object, and no output schema. The description fails to provide adequate context: no usage scenarios, no parameter explanations, no behavioral details, and no return structure beyond a vague list. This is minimal for a decision-making 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 only 14% (only policy_profile is described). The description adds zero parameter semantics; it does not mention goal, deadline, constraints, or device types despite the schema being complex. It fails to compensate for the low schema coverage.

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 clearly states the tool outputs an executable action with timing, expected value, confidence, and expiry for EV, battery, and flexible-load agents. It distinguishes from siblings by highlighting the unique decision output, though it does not explicitly name 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?

No guidance on when to use this tool vs. alternatives like price_forecast or buy_dispatch_plan. It only implies the intended agent types (EV, battery, flexible-load) but does not specify scenarios or exclude other 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?

There are no annotations to provide safety or behavioral hints, and the description does not disclose any behavioral traits. It does not state whether the tool is read-only, what data it returns, whether it requires authentication, or any side effects. As a noun phrase, it gives no information about the tool's runtime behavior.

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 extremely concise, consisting of a single short German noun phrase. It contains no filler words, repetitions, or extraneous details. Given the zero-parameter schema, this brevity is appropriate and front-loaded with the core purpose.

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 and no annotations, the description carries the burden of explaining what the tool returns and how it behaves. It only lists event categories but does not specify the format, recency, or any other context about the feed. The description is too sparse to be considered complete for an agent to confidently select and invoke the 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?

The tool has zero parameters, and the schema is empty. According to the rubric, 0 params warrants a baseline score of 4. The description adds no parameter-specific information, but none is needed since there are no inputs to explain.

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 'Ereignisfeed für Dunkelflaute, Sturm, negative Preise und Rekorde' clearly identifies the tool as an event feed for specific event types (dark doldrums, storms, negative prices, records). However, it lacks an explicit action verb like 'shows' or 'lists', making it slightly less precise than a verb+resource formulation. The specific event types help distinguish it from sibling tools.

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 no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, use cases, or exclusions. With no mention of alternative tools or conditions, the agent is left to infer usage entirely from the name and brief description.

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?

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions 'buy this endpoint' which could imply a purchase or cost, but does not elaborate on costs, side effects, authentication, rate limits, or response format. This is a significant gap for a tool with no annotation support.

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 a single, concise sentence that fronts the key information (who uses it and what it does). It contains no fluff or redundant content, making it clear and efficient.

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 there is no output schema, the description should explain return values. It states the endpoint identifies 'cheapest upcoming operating hours,' giving a basic sense of the output, but lacks details on time horizon, granularity, or any constraints. For a simple parameterless tool, this is adequate but not fully complete.

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, and the schema is empty (coverage 100%). Per the rubric, the baseline for 0 parameters is 4. The description adds no parameter-specific information, but it is not needed since there are no inputs to clarify.

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: identify the cheapest upcoming operating hours, with specific target users (EV, heat-pump, and flexible-load agents). It uses a specific verb-resource combination, but does not explicitly distinguish from the sibling tool 'optimizer_cheapest_window', so it misses the top score.

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 implies usage for certain agent types and a specific need (finding cheap hours), but provides no explicit guidance on when to prefer this over alternatives, nor does it mention any exclusions. It gives context but no direct comparison with 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?

There are no annotations, so the description must disclose behavioral traits. It only says 'obtain,' implying a read operation, but does not clarify side effects, auth requirements, rate limits, or return format. The unusual verb 'buy' suggests a potential cost or subscription model but remains ambiguous.

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 a single sentence with no filler, though 'buy this endpoint' is slightly awkward phrasing. It is concise and front-loaded.

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?

The description conveys the key identity (German system load from SMARD) but omits output details such as units, time range, and whether a time series or single value is returned. Without an output schema, this gap leaves agents uncertain about the response structure.

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 takes zero parameters, and the input schema is empty, so there is nothing for the description to clarify. Baseline for zero parameters applies.

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 explicitly states the endpoint 'obtain[s] the latest German system load from SMARD', identifying both the action and the specific data resource. It also frames the audience ('grid-monitoring and dispatch agents'), distinguishing it from data tools like price_spot or renewable_share.

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 context for when to use the tool: if you are a grid-monitoring or dispatch agent needing German system load. However, it does not explicitly mention when not to use it or name alternatives, so it lacks exclusion criteria.

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?

With no annotations, the description must disclose behavioral traits. It only states 'hourly data for EPEX prices by period' but does not describe return format, expected side effects, authentication needs, rate limits, or any other operational behavior. It is minimal and lacks detail.

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 a single, concise sentence with no unnecessary words. It front-loads the core purpose, making it easy to parse. However, it could have included more useful information without sacrificing conciseness.

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 tool with one parameter, no output schema, and no annotations, the description is sparse. It does not explain what the returned data looks like, any limitations, or how the period parameter is formatted beyond the examples. Given its simplicity and the abundance of sibling tools, more context is needed for an agent to use it correctly.

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 description references the single parameter with 'nach Periode', indicating that the parameter specifies a time period. This adds some semantic beyond the raw schema examples (e.g., 'last-7d', '2025-Q4'), but it does not explain the value format or constraints in detail. Schema coverage is 0%, so partial compensation is made.

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 'Stundendaten für EPEX-Preise nach Periode' clearly identifies the tool as providing hourly EPEX price data filtered by period. It uses a specific resource (EPEX prices) and scope (hourly, by period), distinguishing it from price_forecast and price_spot, though it lacks an explicit verb like 'retrieve'.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or comparisons with sibling tools like history_prices_stats or price_forecast.

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?

No annotations are present, and the description does not disclose any behavioral traits such as return format, read-only nature, or data source. It simply states a noun phrase without actionable details.

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?

While the description is short, it errs on under-specification rather than conciseness. It provides minimal information, leaving the agent to guess the tool's functionality.

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 tool with no annotations, no output schema, and a vague description, the contextual information is severely lacking. The agent cannot determine expected output, period constraints, or relationship to sibling tools.

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 0%, and the description does not mention the 'period' parameter or its valid format. The only semantic clues come from the schema's examples, which the description fails to reinforce or expand upon.

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 'Statistik für eine historische Preisperiode' is essentially a direct translation of the tool name, providing no verb or concrete outcome. It fails to specify what statistics are computed or how it differs from the sibling tool 'history_prices'.

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?

No guidance is provided on when to use this tool over alternatives like history_prices or price_forecast. The single-sentence description offers no context for selection.

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?

With no annotations, the description must carry the full burden. It states the core function but does not disclose whether the tool executes a purchase, returns a schedule, or has side effects. The phrase 'buy this endpoint' is ambiguous and not elaborated. Score 2.

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?

A single sentence with no filler, front-loading the purpose. Score 5.

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 tool with no annotations, no output schema, and an ambiguous 'buy' action, the description leaves critical gaps about behavior and return values. The simplicity of the schema does not compensate for the missing context. Score 2.

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 has one parameter (duration_hours) with no description in the schema, and the description does not mention it. The parameter name and constraints are self-evident, but the description adds no value in explaining how it relates to the window. Score 2.

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 uses the verb 'schedule' and identifies the resource as 'a contiguous operating window at the lowest average electricity price,' which clearly conveys the tool's function. However, it does not explicitly distinguish it from similar sibling tools like flexibility_window, so a 4 is appropriate.

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 mentions the target user ('Flexible-load agents') and the context ('lowest average electricity price'), implying when to use it, but it does not provide explicit guidance on alternatives or exclusions. A score of 3 reflects that the usage is implied rather than explicit.

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?

There are no annotations, so the description carries the full burden. It mentions that agents 'buy' the endpoint, hinting at a commercial transaction, and that they receive outputs, but it does not disclose whether the operation is read-only, any side effects, rate limits, or auth requirements. The behavioral context is thin.

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 a single dense sentence that front-loads the audience and core outputs. It is concise and contains no filler, though the verb 'buy' could be clearer. It earns a 4 for efficient communication.

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 low complexity (1 optional parameter, no output schema, no annotations), the description covers the main outputs (probabilities, profiles, policy) but leaves details like how to interpret the executable response policy to the user. It is adequate but not fully complete for an agent invoking it blindly.

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% for the single parameter (policy_profile), so the schema already fully explains its semantics. The tool description adds no parameter details, but per the baseline for high schema coverage, a 3 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 clearly states the resource: calibrated 2h/4h/6h negative-price probabilities, along with threshold profiles and a response policy. The verb 'buy' is awkward but the intent is clear. It does not explicitly distinguish from sibling tools like price_negative_forecast, so it misses the top score.

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 identifies target users (Battery, EV, and trading agents) and implies they should use this for receiving negative-price probabilities, but it does not state when to use this tool versus alternatives or mention any exclusions. Guidance is implied rather than explicit.

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?

With no annotations, the description carries full responsibility for disclosing behavior. It mentions 'buy' (suggesting a cost) but does not explain side effects, authentication needs, rate limits, or what the response actually contains. The agent is left guessing about the operational details beyond the stated comparison purpose.

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 a single, compact sentence that front-loads the core purpose. It avoids unnecessary words, though the phrase 'buy this endpoint' could be simplified. Overall, it is concise and structured acceptably.

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 simple, no-parameter tool, the description gives the core purpose and target audience. However, with no output schema and no annotations, it should at least hint at the response format or any special conditions (e.g., subscription required). The description is minimal but not entirely inadequate, so a 3 is appropriate.

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 and the schema is an empty object (100% coverage). The description adds no parameter-specific information, but none is needed. Baseline for zero parameters is 4, and nothing in the description detracts from that.

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 function: comparing next 24 hours of EPEX prices and volatility. It names a specific resource (EPEX prices) and a distinct time window, which differentiates it from historical or spot-price siblings like history_prices and price_spot. The verb 'buy' is slightly odd but does not obscure the purpose.

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 implies usage for energy-trading and flexible-load agents, giving a clear target audience and context. However, it does not explicitly state when not to use this tool or mention any alternative tools, relying on the agent to infer from sibling names.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure, but it merely states on a high level what the tool returns and omits any information about read-only nature, data source, return format, or side effects. It does not even explicitly say 'returns' or 'forecasts', leaving the behavior largely implicit.

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 a single short phrase that communicates the essential content with no redundant words. It is extremely concise and front-loaded, with every word contributing to the meaning.

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 without an output schema, the description provides the key information (hours, negative electricity prices, 24-hour window) and is minimally viable. However, it lacks details about the output format and does not disambiguate from the similar 'predict_negative_price' tool, leaving meaningful gaps in what an agent needs to fully understand the 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?

The tool has zero parameters, so the baseline score of 4 applies. The description does not need to explain any parameter semantics, and no parameter-related gaps exist.

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 specifies the resource (hours with negative electricity prices) and the time scope (next 24 hours), making the tool's purpose fairly obvious even without an explicit verb. However, it does not differentiate from the sibling tool 'predict_negative_price', which likely serves a similar function.

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?

There is no guidance on when to use this tool versus alternatives such as 'price_forecast' or 'predict_negative_price', nor any exclusions, prerequisites, or context suggesting the intended use case.

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?

No annotations are provided, so the description must disclose behavioral traits. It only states the tool 'obtains' a price, which implies a read operation, but does not mention latency, caching, response format, units, or any other behavioral details. The phrase 'buy' could imply cost but is ambiguous. The description lacks sufficient behavioral transparency for a tool with no 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 a single, front-loaded sentence that clearly states the purpose and audience. Every word earns its place, with no redundant or vague phrasing.

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 has no parameters and no output schema, the description is mostly sufficient: it identifies the resource (current German EPEX spot price) and the intended use. However, it does not specify units (e.g., EUR/MWh), time granularity, or potential delays, which could be important for an agent making immediate decisions. The lack of annotations makes the description the sole source of information, and it leaves some gaps.

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 there is no parameter semantics burden. The schema description coverage is effectively 100% (empty object). The description correctly implies no parameters are needed, aligning with the schema.

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 obtains the current German EPEX spot price, with a specific verb ('obtain'), resource ('current German EPEX spot price'), and context ('for immediate decisions'). It distinguishes from siblings like price_forecast (forecast vs. current) and history_prices (historical vs. current).

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 clear context for when to use the tool (trading and dispatch agents, immediate decisions) implied by 'current' and 'immediate'. It does not explicitly mention alternatives or exclusions, but the context is strong enough for an agent to infer this is for live spot price retrieval rather than forecast or historical data.

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?

With no annotations, the description must disclose behavioral traits but only lists data categories. It does not state whether the operation is read-only, if data is current/forecast, or any limitations. This is insufficient for a tool without annotation support.

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 a compact phrase that lists the key data types without unnecessary words. It is front-loaded with 'Preis, CO2, Grünstrom und Wetter', making it easy to scan. However, it is more of a fragment than a complete sentence, which slightly reduces structure clarity.

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 simplicity (one parameter, no output schema), the description is minimal but not fully adequate. It does not specify the nature of the data (current vs. historical), any limitations, or what a typical response contains. This leaves important context missing for an agent needing to select the 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?

The schema has one parameter 'plz' with a pattern, but the description adds only the contextual mention of 'PLZ-Region'. It does not explain the meaning of the parameter beyond what the schema implies, nor does it clarify any expectations like required format or range. Description coverage is 0%, so this is a gap.

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 identifies the tool's purpose: retrieving price, CO2, green electricity, and weather data for a postal code region. However, it lacks an explicit verb (e.g., 'get' or 'retrieve'), which prevents a perfect score. It distinguishes itself from sibling tools by combining multiple data types.

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 no guidance on when to use this tool versus alternatives such as carbon_now or price_spot. It does not mention any exclusions or prerequisites. This leaves the agent to infer usage from the data combination.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds key context by stating the endpoint is purchased ('buy'), indicating a commercial/authorization requirement, and 'current' implies real-time data. The verb 'measure' implicitly communicates a read-only operation, which is important for AI agents.

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 a single sentence that immediately states the purpose and target user, with no filler or redundancy. Every word adds value, making it concise and well-structured.

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 has no parameters and no output schema, the description adequately communicates what is measured and for whom. It lacks explicit details about the response format (e.g., whether renewable share is a percentage or a breakdown), which would make it fully complete, but it is still sufficient for a simple data 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?

The input schema has zero parameters, and the description does not add parameter-level details because none are needed. Per the baseline for 0 parameters, a score of 4 is appropriate, though the description could have elaborated on the output format, which is not strictly a parameter-semantics concern.

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 measures the current renewable share and generation mix in Germany, with a specific verb ('measure') and resource. This distinguishes it from sibling tools, none of which mention renewable share or generation mix directly.

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 targets 'Carbon-aware and ESG agents' and explains they 'buy this endpoint' to obtain this measurement, providing clear context on who should use it. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a full 5.

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?

With no annotations, the description carries the transparency burden. It discloses the key behavioral trait of returning a machine-readable 402 response, which is useful for an agent to understand the error semantics. However, it does not explain the reason for the fallback or any other side effects, leaving significant behavioral context unexplained.

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 a single, compact sentence. It conveys the essential technical behavior (GET fallback, 402 response, POST /research) without any filler. Every word contributes, and it is appropriately sized for the simplicity of the tool.

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 lack of annotations, output schema, and the presence of many sibling tools, the description is too sparse. It does not explain what 'research' is, when to use the GET fallback versus research_post or research_quick, or what the 402 response means for the agent's workflow. The tool's purpose is not fully specified for effective tool selection and invocation.

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 no parameters and the schema is an empty object. With zero parameters and 100% schema coverage, the description need not add parameter-level detail. The baseline for no parameters is 4, and the description avoids irrelevant param statements.

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 that this is a GET fallback returning a machine-readable 402 response for the POST /research endpoint. It identifies the resource and behavior but not the actual business purpose of 'research'. It partially distinguishes from siblings by mentioning fallback, but the core function remains vague.

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 implies this is a fallback for when POST /research is not appropriate, but it does not provide explicit guidance on when to use this tool versus research_post or research_quick. There are no prerequisites, exclusions, or alternative usage conditions beyond the fallback label.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the research comes from live data sources, which hints at dynamic behavior but does not disclose side effects, permissions, rate limits, return format, or any other behavioral traits. This is insufficient for a tool with no annotation support.

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 a single, concise sentence with no redundant words. It is properly sized in terms of length, though it sacrifices necessary detail. It is not bloated, but it is under-specified, so it earns a 4 rather than a 5.

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 that the tool has 2 parameters, no output schema, and no annotations, the description is incomplete. It does not explain what the tool returns, how the parameters interact, or any relevant context for invocation. The only useful context is the geographic scope (DE/EU) and live data sources, but that is not enough for a reliable decision.

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 0%, and the description provides no explanation of the 'query' or 'sources' parameters. The description does not compensate for the lack of schema documentation, leaving the agent without any understanding of what values to provide or what these parameters control.

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 'Aggregierte Energie-Recherche für DE/EU aus Live-Datenquellen' indicates the tool provides aggregated energy research for Germany/EU from live data sources, but it lacks an explicit action verb (e.g., 'searches', 'retrieves'). It conveys the general topic and scope but does not specify what the tool actually does with the query or sources, making it somewhat vague and not clearly distinguished from sibling tools like 'research' or 'research_quick'.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any sibling tools, use cases, or conditions under which this tool is preferred. It leaves the agent without clear direction on when to invoke 'research_post' over other research-related 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?

With no annotations, the description must carry behavioral disclosure, but it only hints at the output being a 'live market snapshot' that is 'compact.' It does not mention whether the tool is read-only, requires authentication, has rate limits, or has side effects. This leaves significant behavioral ambiguity for a tool that performs a research action.

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 a single sentence and quite concise, stating the core use case without extraneous detail. However, the phrase 'buy this endpoint' is an awkward construction that could be clearer. It is still appropriately short and front-loaded.

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 has no output schema, no annotations, and two parameters, the description should provide more context about what the snapshot contains, how 'sources' is used, and what the response looks like. The single sentence is too thin for an agent to confidently invoke the tool and interpret results, especially with sibling tools like 'research' and 'research_post' offering potentially different levels of detail.

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 description provides no information about the parameters 'query' and 'sources,' and the schema has 0% description coverage. The parameter names are minimally self-explanatory, but 'sources' in particular lacks context about what sources are acceptable or how they influence the snapshot. The description adds no meaning beyond the raw schema.

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 identifies the tool's purpose as answering 'a focused question with a compact live market snapshot.' The verb 'answer' and the scope 'compact live market snapshot' are specific, and the 'quick' in the name distinguishes it from siblings like 'research' and 'research_post'. However, the phrasing 'buy this endpoint' is awkward and slightly obscures the intent.

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 implies usage for focused questions and market monitoring, saying it answers 'a focused question with a compact live market snapshot.' It provides context but does not explicitly mention when to use this tool versus alternatives like 'research' or 'research_post,' nor does it specify exclusion criteria.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the output components (RSI, Z-score, trend, confidence) but does not describe whether the operation is read-only, how the signal is computed, what data source it uses, or any limitations. This is insufficient for a tool with no annotation support.

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 a single sentence, which is concise and front-loaded with the key purpose. It could be slightly more polished ('buy' is a colloquial usage), but every word serves a purpose and it is not verbose.

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 there is no output schema, the description should clearly convey what the response looks like. It lists the signal components but does not specify their types, ranges, or example values. The tool is simple (0 params) but the output is multi-factor, so more detail would improve completeness.

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 input schema has zero parameters, so the baseline is 4. The description does not attempt to explain parameters because there are none. It does not mislead or create confusion about inputs.

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 that the tool provides a multi-factor buy/sell signal with RSI, Z-score, trend, and confidence. This clearly identifies the tool's purpose and output, though it does not explicitly differentiate from sibling tools. The phrasing 'Trading bots buy this endpoint' is a bit awkward but does not obscure the meaning.

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?

There is no guidance on when to use this tool versus alternatives. It implies the tool is for trading bots ('Trading bots buy this endpoint'), but does not mention any exclusions, prerequisites, or comparison to sibling tools. The intended usage context is vague.

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?

Without annotations, the description must carry the burden of behavioral disclosure, but it only says 'Webhook-Subscription für niedrige CO2-Intensität' and does not explain trigger events, threshold semantics, or what the webhook delivers. This is minimal behavioral information.

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 a single noun phrase, which is extremely short, but this is under-specification rather than effective conciseness. It is not a complete sentence and provides barely any useful 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 four parameters, no output schema, and no annotations, the description fails to explain essential aspects of the webhook subscription—how thresholds are used, what events trigger the webhook, or what the agent should expect. The tool is significantly under-described.

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 0%, and the description does not mention any of the four parameters (direction, webhook_url, threshold_g_kwh, threshold_ct_kwh). The description provides no semantic meaning to complement the schema.

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 identifies the resource ('low CO2 intensity') and labels it as a webhook subscription, which is clear enough to distinguish from sibling tools like subscribe_price_alert. However, it lacks an explicit action verb and does not state 'subscribes' or 'creates', so it falls short of a full purpose statement.

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?

There is no guidance on when to use this tool versus alternatives such as subscribe_price_alert. The description gives no context for when a low CO2 webhook subscription is appropriate.

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?

With no annotations, the description carries the full burden of behavioral disclosure. It only states what the tool is ('Webhook-Subscription') but leaves out critical behaviors: whether it creates a one-time or persistent subscription, authentication requirements, HTTP method, or what triggers the webhook. No annotations compensate for this gap.

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 exceptionally short, with no wasted words, which is good for conciseness. However, it is under-specified to the point of being more of a title than a functional description, so while it is not tautological, it earns only a middle score.

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?

The tool has 4 parameters, no schema descriptions, no output schema, and no annotations. The description provides only a general concept, omitting the required webhook_url parameter, the subscription flow, and any response behavior. This is incomplete for an agent to invoke the tool correctly.

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 0%, so the description must explain the parameters. It mentions 'Thresholds' generically but does not map to threshold_g_kwh, threshold_ct_kwh, direction, or webhook_url. The word 'Thresholds' is insufficient to understand the parameter roles.

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 indicates a webhook subscription for electricity price thresholds, using a specific verb-resource structure. It distinguishes from siblings like subscribe_co2_low by focusing on price rather than CO2, though it does not mention direction or exact trigger conditions.

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?

No guidance is provided about when to use this tool versus alternatives, such as price_forecast or subscribe_co2_low. There are no exclusions, prerequisites, or contextual hints beyond the single statement.

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?

No annotations are provided, and the description gives no information about data sources, freshness, limitations, or side effects. It only states the output's nature but does not disclose any behavioral traits such as whether it reads current data or performs any analysis.

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 a single, concise phrase that is front-loaded with the core purpose and includes a valuable differentiator ('token-effizient' and 'konkreter Handlungsempfehlung'). Every word contributes, with no redundancy or wasted space.

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 no parameters or output schema, the description does not specify what domain or topic the summary covers (e.g., energy, prices, trading). Given sibling tools like 'price_spot' and 'renewable_share', the context is not explicit, leaving the summary tool's intended use ambiguous.

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 the baseline is 4. The description adds meaning by explaining the output's content (summary with recommendation), which is sufficient given the absence of parameters.

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 it provides a 'Tageszusammenfassung' (daily summary) with a 'konkreten Handlungsempfehlung' (concrete action recommendation), which specifies the tool's output and scope. It is distinct from siblings like 'price_forecast' or 'research', though the verb is implied rather than explicit.

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?

Usage is implied: the tool is for getting a token-efficient daily summary with a recommendation, likely when a concise overview is needed. However, there is no explicit guidance on when to prefer this over alternatives or any exclusion criteria, so the context is only partially clear.

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?

The description discloses the primary behavior (returning a 402 response) and that it is machine-readable, which is useful. However, with no annotations, it carries the full burden and lacks details such as response structure or that it is essentially an error stub.

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?

A single, front-loaded sentence conveys the essential information without redundancy. It is concise, though extremely minimal, which is appropriate for such a simple fallback tool.

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 simplicity (no params, no output schema, trivial behavior), the description is reasonably complete: it states the tool returns a 402 error. It could describe the response body, but this is not strictly necessary for invoking the 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?

There are zero parameters and the schema is an empty object with 100% coverage by definition. The description adds no parameter-level detail, but none is needed; baseline 4 applies.

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 identifies this as a GET fallback returning a machine-readable 402 for POST /tasks, distinguishing it from the sibling tasks_post. It names the resource and the specific fallback behavior, though it is terse.

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?

It implies a fallback role for GET requests but does not explicitly state when to call this tool versus tasks_post or other siblings. No clear context or alternative guidance is provided.

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?

The description discloses only that the task is asynchronous, which is a minimal behavioral trait. With no annotations available, the description does not explain side effects, authorization needs, or what happens after posting. The 'minimum bounty' detail merely repeats the schema information.

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 a single noun phrase, which is terse but not effectively concise. It under-specifies the tool's purpose and lacks sentence structure, making it more of a label than a helpful description.

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 POST tool with no annotations and no output schema, this description is severely incomplete. It does not define what a 'deep-dive task' entails, how the bounty is used, what the expected response is, or any constraints beyond the schema's bounty limits.

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 covers only the bounty parameter; task_description has no description. The tool description does not explain task_description at all and only echoes the bounty minimum already present in the schema. No additional meaning is added beyond the structured field names.

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 'Asynchroner Deep-Dive Task mit Mindest-Bounty' is a noun phrase without an explicit verb, making it unclear that the tool creates or posts a task. It adds some context (asynchronous, minimum bounty) but does not clearly state the action or differentiate from sibling post tools like research_post.

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 no guidance on when to use this tool versus alternatives. It lacks any mention of appropriate use cases, exclusions, prerequisites, or relationships to sibling tools.

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