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

No annotations are provided, so the full burden falls on the description. It describes the output as 'Coverage report with matchup counts and notable weaknesses' but omits details on how coverage is calculated, edge cases like dual types, or limitations. This is a minimal disclosure of 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 short and structured with Args and Returns sections. The opening sentence captures the tool's purpose, and every line adds value without 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?

Given the tool has a single parameter, no annotations, and an output schema exists, the description provides a basic summary and return description. However, it lacks details on the analysis logic or what constitutes 'notable weaknesses,' which could be important for users deciding whether to use this tool. The output schema likely covers the return structure, so the description is adequate but not rich.

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 defines names_or_dexes as an array of strings with no description. The description adds essential meaning by specifying 'Pokemon names or dex numbers to analyze,' which clarifies the expected input beyond the schema's bare type. Though it could mention accepted formats, it effectively compensates for the 0% 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?

The description clearly states 'Summarize defensive type coverage for a roster,' which is a specific verb+resource pair. It distinguishes itself from sibling tools like get_pokemon or get_moves by focusing on team-level coverage analysis, making its purpose unambiguous.

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 does not explicitly state when to use this tool versus alternatives. It implies use when analyzing team type coverage, but offers no exclusions or comparisons to sibling tools such as suggest_moveset, which might overlap in context.

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 and does well: 'Retrieve' clearly indicates a read-only operation, and the 'Returns' section discloses the output type (ability names and effect text). However, it does not mention potential errors, edge cases, or behavior for invalid inputs, which would make it more transparent.

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 succinct and well-structured: an opening summary sentence, followed by labeled Args and Returns sections. Every sentence carries necessary information with no fluff or 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 simple tool with one parameter and an output schema, the description is nearly complete. It defines the input and the type of output. It does not elaborate on output structure, but that is covered by the output schema, and it does not mention limitations or related alternatives, which 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?

Although schema coverage is 0%, the description fully compensates for the single parameter by stating 'name_or_dex: Pokemon name or national dex number.' This adds meaning beyond the schema, which only indicates type string. It is clear and practical, though it does not specify formatting for dex numbers (e.g., leading zeros).

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 action with a specific verb and resource: 'Retrieve ability details for a Pokemon.' It distinguishes itself from sibling tools like get_pokemon and get_moves by focusing specifically on abilities.

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 by saying 'for a Pokemon,' but does not explicitly state when to use this tool over alternatives. It lacks exclusions or comparisons to sibling tools like get_pokemon or get_moves, leaving the agent to infer from the name and purpose.

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 burden of behavioral disclosure. It adds useful context by stating that results are 'grouped by version and method,' but it does not explicitly confirm that the operation is read-only, mention any side effects, or address error behavior for invalid inputs. The 'Find' and 'Returns' wording suggests a query, but this is not fully explicit.

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 concise, using a standard docstring format with Args and Returns sections. It contains no filler and every line adds information. The structure is consistent with typical tool descriptions and easy to parse.

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 lookup tool with one parameter and an output schema (though not shown in detail), the description provides sufficient context: what the tool finds, what input it takes, and how results are organized. It does not need to explain return values in depth because an output schema exists, and the grouping detail adds useful context beyond that.

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 only provides a type and title for 'name_or_dex' with no description. The tool description compensates fully by defining it as 'Pokemon name or national dex number,' which is essential for correct invocation. With 0% schema description coverage, this addition is highly valuable and complete.

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 with a specific verb and resource: 'Find wild encounter locations for a Pokemon.' This distinguishes it from sibling tools like get_pokemon or get_moves, which serve different purposes. The 'Returns' line further clarifies the output scope.

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—if you need encounter locations for a Pokemon, use this tool—but it does not explicitly state when to use it versus alternatives or provide exclusions. No sibling tools are named as alternatives, so an agent must infer the appropriate context from the purpose 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, so the description carries the full burden of behavioral disclosure. It explains the filtering behavior of the 'game' parameter and what data is returned, but it does not explicitly mention side effects, permissions, or the fact it is a read-only operation. Still, the behavior is straightforward and sufficiently transparent for a lookup tool.

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 compact and well-structured, with a clear one-line summary followed by Args and Returns sections. Every sentence adds value, and there is no redundant or filler content. It is appropriately sized for the tool's complexity.

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 presence of an output schema and the moderate complexity, the description adequately covers the tool's purpose, parameters, and return value. It might have mentioned edge cases or prerequisites, but the explanation is sufficient for an agent to select and invoke the tool correctly alongside 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?

With schema description coverage at 0%, the description fully compensates by explaining both parameters: name_or_dex is described as 'Pokemon name or national dex number,' and game is described as 'Optional game identifier for egg move filtering.' This adds crucial meaning beyond the bare schema definitions, which only provide titles.

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 starts with 'Summarize breeding details for a Pokemon,' which uses a specific verb (summarize) and resource (breeding details) that clearly distinguishes it from sibling tools like get_moves or get_pokemon. It also details the return content (egg groups, hatch steps, egg moves), making the tool's purpose unambiguous.

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 context: use when you need breeding-related information for a Pokemon. It does not explicitly compare with siblings or state exclusions, but the tool's purpose is narrow and self-evident among the sibling tools, providing clear context without needing further alternative mentions.

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. It discloses the return type ('Mapping of game version to flavor text') and parameter usage, but does not mention potential errors, rate limits, or other behavioral traits that could affect agent decisions.

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 concise and well-structured, with a single-sentence summary followed by clearly labeled Args and Returns sections. Every sentence earns its place, and there is no redundancy or filler.

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 read-only tool with two parameters and an output schema, the description covers inputs and return shape adequately. However, it lacks broader context such as when to use the tool or potential limitations, though this is not critical given its straightforward nature.

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%, but the description compensates by explaining both parameters: 'name_or_dex' as 'Pokemon name or national dex number' and 'language' as 'Language code for flavor text.' This adds meaningful context beyond the raw schema, though it could specify the expected language code format.

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 'Fetch localized Pokedex flavor text,' which uses a specific verb and resource, making the tool's purpose clear. It is clearly distinguished from sibling tools like get_moves and get_sprites, which handle different 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. There is no mention of scenarios, exclusions, or preferred contexts, leaving the agent to infer usage solely from the tool's name.

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 behavior disclosure burden. It states inputs and return value, but does not mention error behavior on invalid game/name, whether the learnset includes all move acquisition methods, or any potential rate limits or PokeAPI quirks. This is adequate but not rich.

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 and well-structured, with a short summary followed by Args and Returns sections. Every sentence serves a purpose and there is no redundant 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 tool's moderate complexity, the description is complete enough: it names the inputs, explains the output, and specifies the game context. The output schema exists, so the description need not enumerate return fields in detail. It could mention edge cases, but overall it sufficiently equips an agent to 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?

Schema coverage is 0%, but the description compensates by defining both parameters: name_or_dex as a Pokemon name or national dex number, and game as a PokeAPI game identifier with an example. This adds meaning beyond the bare schema and is sufficient for the two-parameter 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 opens with a specific verb-resource pair: "List the learnset for a Pokemon in a specific game." This clearly distinguishes the tool from siblings like get_pokemon or find_encounters by focusing on moves/learnset for a game-specific context.

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 makes the appropriate use case clear: to retrieve the learnset of a Pokemon in a given game. It does not explicitly list when not to use it or mention alternatives, but the context and siblings like suggest_moveset imply it is for raw learnset data rather than optimized movesets.

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 burden of disclosing behavior. It reveals the read-only nature via 'Fetch' and specifies return content (summary stats, typing, measurements), but does not mention error handling, invalid identifiers, or any rate limits, leaving partial transparency.

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 concise and well-structured with a one-sentence purpose followed by Args and Returns sections. Every line earns its place, with no redundant 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?

The tool is simple, has an output schema, and the description covers the parameter and return overview. However, it lacks usage guidance and error behavior, which slightly reduces completeness for a tool with many siblings.

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 no description for name_or_dex (0% coverage), but the description explicitly explains it as 'Pokemon name or national dex number,' fully compensating for the missing schema detail and adding clear meaning.

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 'Fetch a Pokemon summary' with a specific verb and resource, and the scope is defined as summary stats, typing, and measurements. This distinguishes it from siblings like get_moves and get_sprites, which fetch specific data.

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 like get_moves or get_sprites. It does not state exclusions or prerequisites beyond the identifier, leaving the agent to infer usage from the summary nature.

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 it returns a sprite URL (not the image itself), which is useful. However, it doesn't mention any potential side effects, error behavior, or network dependencies. For a simple resolver this is adequate but not rich.

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 concise and well-structured, with a one-sentence purpose followed by Args and Returns sections. Every line adds value, and the format is easy to parse quickly.

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 is simple, and the description covers both inputs and the output format. The existence of an output schema means return details are handled elsewhere. It could mention what happens for invalid inputs, but that's not essential for a sprite URL lookup, so this is complete enough.

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%, but the description fully compensates by explaining each parameter: 'name_or_dex' as Pokemon name or national dex number, 'side' with examples 'front'/'back', and 'variant' with examples 'default'/'shiny'. This adds significant meaning beyond the raw schema titles and defaults.

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 ('Resolve') and clear resource ('sprite URL for a Pokemon'), making its purpose immediately obvious. It also distinguishes itself from sibling tools like get_pokemon or get_moves by focusing on sprite 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 provides clear context that this tool is for getting sprite URLs, which implies when to use it. It doesn't explicitly mention alternatives or exclusions, but the Args section clarifies the required inputs and typical usage, so context is sufficient for an agent to decide when this tool 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 provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation via 'enumerate' and 'returns' and specifies the high-level output, but it does not disclose edge cases, error conditions, or any side effects. This is adequate for a simple query tool but not rich.

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 succinct and well-structured with a purpose line, Args section, and Returns section. Every element serves a purpose, with no unnecessary words or repetition.

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 one-parameter tool with an output schema, the description is complete enough. It provides a high-level return summary and leaves detailed field documentation to the output schema. Minor ambiguity about the exact interpretation of 'evolution paths' exists, but overall it is adequate.

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 provides only a field name and type (string) with 0% description coverage. The description compensates by explicitly defining the parameter as either a Pokemon name or national dex number, adding meaningful semantics beyond the schema. This is sufficient for a single parameter.

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 specific verb 'enumerate' and clearly identifies the resource as 'evolution paths' for a given Pokemon. This distinguishes it from sibling tools like get_moves or get_sprites, which focus on other aspects.

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 does not explicitly state when to use this tool vs alternatives, nor does it mention exclusions or sibling tools. Usage is implied from the name and description (i.e., when evolution paths are needed), but there is no explicit guidance.

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 that recommendations are based on 'simple heuristics' and that results are ranked, which adds useful context beyond the bare function. However, it does not detail what those heuristics are, whether external API calls are made, or how failures are handled. With no annotations, this falls short of full transparency.

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 concise and well-structured: a one-sentence purpose, then a clean Args/Returns section. Every line adds value, and the info is front-loaded with the main action.

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 covers the tool's purpose, all parameters, and the general return type ('Ranked move recommendations'). With an output schema present, detailed return formatting is not needed. It lacks only deeper context like failure modes or the nature of the heuristics, making it nearly 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?

All four parameters are explicitly described with their meaning: name_or_dex, game, limit, and include_tm. This fully compensates for the 0% schema description coverage, providing clear semantics for each argument beyond the schema's minimal titles.

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 ('Recommend') and resource ('moveset for a Pokemon'), clearly distinguishing this from sibling tools like get_moves or analyze_type_coverage. It states the exact action and target, leaving no ambiguity about what the tool does.

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 does not provide explicit guidance on when to use this tool versus alternatives. It implies use for moveset recommendations but lacks context such as 'use this when you need a ready-made set rather than raw move data' or exclusions for other tools. No prerequisites or edge cases are mentioned.

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