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

With no annotations, the description carries the full burden. It communicates a read-only lookup behavior and the language scope, but does not disclose output format, error handling for unsupported languages, or any limitations. This is acceptable but adds limited behavioral context beyond the basic operation.

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 wastes no words. It names the action, resource, and scope all at once, making it highly 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 the tool's simplicity (one required parameter, no output schema, no annotations), the description is mostly adequate. It clearly conveys the tool's purpose and scope. However, because no output schema exists, a brief mention of what the lookup returns (e.g., translations, definitions) would make it 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?

Schema coverage is 100%, and the schema already describes the 'language' parameter with examples. The description reinforces these examples through 'Klingon, Vulcan, Ferengi, etc.' but adds no new semantic detail about value formatting, case sensitivity, or accepted language 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 uses a specific verb ('Look up') and clearly identifies the resource ('phrases and vocabulary from Star Trek alien languages'), with named examples. This clearly distinguishes it from sibling tools like rules_of_acquisition, which has a narrower Ferengi-specific 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 intended usage is implied by the description: use when needing a phrase or vocabulary lookup in an alien language. However, there is no explicit guidance about when not to use it or which sibling tool might be a better alternative for related but distinct needs (e.g., Ferengi rules).

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 behavior on its own. It reveals the output includes effects and resolutions and sets tone with 'classic TNG vibes.' However, it does not mention whether the output is random, how the severity parameter affects results, or the format of the response. This is adequate for a creative generator but leaves room for more 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?

A single, front-loaded sentence that delivers the core purpose, output components, and stylistic flavor. No filler or redundant phrasing. Ideal conciseness for a simple 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?

For a simple one-parameter tool with no output schema, the description is nearly complete: it states what is generated and what the output includes (effects and resolutions). It does not mention the severity parameter or return format, but these are largely evident from the schema and the nature of generation. It is sufficient for an agent to invoke 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 coverage for the single optional 'severity' parameter is 100%, and the schema already includes an enum and description. The tool description does not add any additional meaning or usage guidance for this parameter. Baseline 3 is appropriate since the schema fully documents the 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 a specific verb ('Generate') with a clear resource ('a Star Trek space anomaly') and specifies what it produces ('effects on the ship and possible resolutions'). This clearly distinguishes it from sibling tools focused on other topics like battles or diplomatic scenarios.

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 clearly implies its use case: when you need to generate a Star Trek anomaly with ship effects and resolutions. It provides clear context but does not explicitly name alternatives or exclusions. Since the purpose is unmistakable and the context is unambiguous, it meets the bar for 4.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It discloses that the recommendation is based on crew specialties, but it does not mention output format, failure modes, or how 'optimal' is determined.

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 is front-loaded with the action and context, containing 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?

The tool has no output schema and no annotations, so the description should clarify the return value. It only explains the recommendation basis, leaving the output format and any prerequisites unspecified.

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 fully describes both parameters (100% coverage), with clear descriptions for mission and team_size. The description adds no additional parameter semantics beyond what the schema already provides.

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 ('optimal away team composition'), and adds a criterion ('based on crew specialties'), clearly distinguishing it from siblings like crew_manifest. It unambiguously states the tool's 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 phrase 'for a mission' provides clear context for when to use this tool. It does not explicitly mention alternatives or when not to use it, but the context is sufficiently 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description states the action (pitting ships) and output type (tactical analysis), but gives no details on what the analysis includes, whether it determines a winner, what format the response takes, 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 single, focused sentence that immediately communicates the tool's action and purpose. Every word contributes value, and no unnecessary details or fluff are included.

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 relatively simple, but with no output schema and no annotations, the description should explain what the tactical analysis comprises and what the user can expect in the response. It does not, and it also fails to clearly differentiate itself from sibling 'compare' beyond the verb choice. This leaves significant gaps for an agent deciding how to use the tool and interpret results.

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 already covers both parameters with descriptions and examples (100% coverage), so baseline is 3. The description adds meaningful context by stating the analysis is 'based on their specs,' which clarifies that the parameters are ship identifiers and that the simulation uses canonical specifications. This surpasses the schema's basic 'description' fields.

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 with a specific verb ('Pit') and resource ('two Star Trek ships'), and defines the outcome as 'tactical analysis based on their specs.' This distinguishes it from sibling tools like 'compare' which likely focuses on direct stat comparison rather than battle simulation.

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 when a user wants to simulate a battle between two ships, but it does not provide explicit when-to-use vs. when-not-to-use guidance or mention alternatives. For example, it does not clarify that 'compare' might be more suitable for simple spec side-by-side analysis instead of a tactical battle simulation.

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 a read action ('Browse') and gives examples, but fails to mention what exactly is returned (e.g., article titles, metadata), whether items are paginated or sorted, or how errors like invalid categories are handled. This is minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with illustrative examples, front-loaded with the key action and resource. No filler or redundant 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?

This is a simple two-parameter tool, but without an output schema the description should ideally indicate what the response looks like. It omits any mention of return format, though the name and verb imply a list of articles. The description is adequate for basic usage 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 input schema has 100% coverage with descriptions and examples for both parameters. The description's category examples are redundant with the schema's own examples and add no new semantic meaning. Base score of 3 is appropriate because schema handles parameter explanation.

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 ('Browse') with a clear resource ('Memory Alpha articles') and a scope ('by category'), providing examples for context. This clearly distinguishes it from sibling tools like search_memory_alpha or get_article, which have different functions.

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 usage is implied from the tool's name and verb – it's for browsing by category – but there is no explicit guidance on when to use it vs. alternatives like search_memory_alpha. No exclusions or contrasting scenarios are mentioned.

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 burden of behavioral disclosure. It states what data is returned (rank, species, affiliation, biography) which is helpful, but it does not mention behavior for missing characters, edge cases, or any limitations. It is a read-oriented lookup, but that is not explicitly stated.

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 front-loads the primary action ('Get details about a Star Trek character') and efficiently lists the key return fields. No unnecessary words.

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 lookup tool with no output schema, the description adequately communicates what the tool returns. It explains the scope (character) and the expected output fields. It could be improved by noting behavior for invalid inputs, but overall it is complete for the tool's simplicity.

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 description covers 100% of the parameter 'name', providing the meaning and examples. The tool description adds no additional semantic information about the parameter. Since schema coverage is high, the baseline of 3 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 states the tool's function: 'Get details about a Star Trek character'. It specifies the resource (character) and lists concrete return fields (rank, species, affiliation, biography). This distinguishes it from sibling tools like species_info or get_starship.

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 by the purpose: use when you need character details. However, the description does not explicitly mention alternatives or when not to use this tool (e.g., for species-specific queries, use species_info). No exclusions or context is provided beyond the basic 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 full burden of behavioral disclosure. It only reveals that the tool is stateless and compares two strings, but it does not describe the return value, matching semantics (e.g., case sensitivity, letter vs. full-text handling), or any side effects. This is a significant gap for an agent deciding how to interpret the result.

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?

Two concise, front-loaded sentences deliver the core purpose and a key behavioral trait without wasting words. Every sentence contributes meaningful information, making it easy to scan and process.

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 schema fully documents the parameters, so the invocation context is clear. However, because there is no output schema, the description should explain what the tool returns (e.g., a boolean, a verdict string, or a personalized message). Its absence leaves a notable gap in understanding the complete behavior.

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 already provides 100% description coverage for all parameters, so the baseline is 3. The description adds only minor context by calling correct_answer a 'token' and referring to player_answer as 'the player's response,' but it does not add substantive meaning 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's function with a specific verb and resource: 'Check a player's trivia answer against the correct answer.' It also distinguishes itself from siblings like start_trivia_game and trivia_quiz by explicitly noting it is 'fully stateless,' indicating this is a pure answer-validation tool rather than a game-flow tool.

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 phrase 'Clara passes the correct answer token and the player's response' implies the core usage pattern: provide both the correct answer and the player's answer. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites beyond the parameters.

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 basic purpose and gives no detail about output format, whether it is a read-only operation, or how it obtains data. This leaves the agent without essential information about side effects and results.

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 front-loads the verb and resource. It contains no filler and every word contributes to understanding the tool's function.

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 only two string parameters and no output schema, the description fails to explain what the comparison returns or how it is presented. Without annotations or an output schema, the description should cover this, but it does not, leaving the agent with incomplete guidance.

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 covers 100% of the parameters, each with descriptions and example values. The tool description does not add any extra meaning beyond the schema, so the baseline of 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 uses the specific verb 'Compare' and identifies the resource as 'two Star Trek subjects side-by-side,' with examples (characters, ships, species). This clearly distinguishes it from sibling tools that fetch single subjects (get_article, get_starship) or perform other operations.

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 when to use the tool—when a side-by-side comparison of two subjects is needed—and this context is clear. However, it does not explicitly state when not to use it or mention alternatives, so it does not fully meet the highest bar.

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 burden. It clearly states the operation is a read ('Get') but adds no context about response structure, edge cases, or data scope beyond the roster itself.

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?

One sentence with no redundant words; front-loads the verb and resource, extremely concise.

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 getter, the description provides essential purpose and input, but without an output schema, the vague 'crew roster' leaves some ambiguity about the return format; otherwise 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 schema already fully describes the ship parameter with examples, so the description adds no additional parameter semantics; baseline 3 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 uses a specific verb 'Get' with a clear resource 'crew roster' scoped to a starship or station, distinguishing it from sibling tools like get_starship or character_lookup.

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 clearly implies use when needing crew roster for a specific ship or station, but does not explicitly name alternatives or exclusions, so it lacks 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?

With no annotations, the description carries the burden of behavioral disclosure. It states that the tool generates fictional content and lists the components, but does not explicitly mention that it is non-destructive or that it does not access external data. It conveys the main behavior without delving into edge cases or side effects.

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, well-structured sentence that immediately states the action and output components. It contains no filler 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?

While there is no output schema, the description sufficiently explains the generated content's key elements. However, it could be slightly more explicit about the structure of the scenario and the optional 'dispute' parameter, though the schema covers that. Overall, adequate for a simple generator.

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 already provides 100% parameter descriptions, so the baseline is 3. The description adds no extra parameter-level detail beyond what the schema provides, such as syntax or formatting.

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 'Generate' and clearly identifies the resource as a 'Star Trek diplomatic negotiation scenario' with three concrete output components (positions, demands, leverage points). This distinguishes it from sibling tools like battle_simulator or away_team_builder.

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 use when a diplomatic negotiation scenario is needed, but provides no explicit guidance on when to choose this over related tools such as battle_simulator or holodeck_program. There are no stated exclusions or alternatives.

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 full responsibility. The verb 'Get' implies a safe, read-only operation, and the description adds the behavioral context of being based on user preference. However, it does not mention any side effects, rate limits, or behavior on unknown episodes, which would enhance transparency. No contradictions with annotations since none exist.

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, well-structured sentence that is front-loaded with the primary action ('Get episode recommendations') and contains zero redundancy. It efficiently conveys the essential information without wasting words.

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 only two parameters and no output schema, the description is largely sufficient. It explains the core function and based on what input. However, it does not specify the return format (e.g., a list of episode titles) or any edge-case behavior, which would be useful given there is no output schema. Still, the tool's simplicity means this is not a critical gap.

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 already documents both parameters with descriptions (100% coverage): 'episode' is 'Episode title you liked' and 'count' is 'Number of recommendations (default: 5)'. The tool description adds minimal extra semantics beyond the schema, only reinforcing the 'enjoyed' aspect. Thus, a baseline score of 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 uses a specific verb ('Get') and resource ('episode recommendations') with a clear basis: 'based on a Star Trek episode you enjoyed'. It clearly distinguishes from sibling tools like 'get_episode' (which fetches a specific episode's details) and 'list_episodes' (which lists all episodes), 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 the primary use case: when you have a Star Trek episode you enjoyed and want recommendations. However, it provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any exclusions (e.g., 'for direct episode info, use get_episode'). The context is clear but not explicit about decision boundaries.

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 disclosing behavioral traits. It states that the tool performs a lookup and assessment, but does not disclose whether it is read-only, how it handles unknown species, whether it requires any authentication, or what the response format/error behavior is. This lack of transparency is a significant 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 a single, well-structured sentence that front-loads the trigger ('Given a species name') and then lists the key assessment dimensions. There is no wasted wording or redundant information; every part contributes to understanding the tool's function.

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 (one input, no output schema), and the description does cover the main output dimensions (warp capability, government type, recommended first contact approach). However, it lacks guidance on usage context, edge cases, and behavioral details, making it only minimally complete for an agent to invoke without further clarification.

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 already thoroughly documents the single 'species' parameter with description and examples, achieving 100% schema description coverage. The description adds no additional parameter semantics beyond restating 'species name', so the baseline score of 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 uses a specific verb ('look up' and 'assess'), names the resource (species on Memory Alpha), and clearly states the output dimensions (warp capability, government type, first contact approach). This distinguishes it from sibling tools like search_memory_alpha or get_article, which are general lookups, and from prime_directive_check or diplomatic_scenario, which focus on different 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 opening 'Given a species name' implies when to use the tool, but there is no explicit guidance on when not to use it or which sibling tools might be alternatives. The description does not mention exclusions or comparison to related tools like species_info or prime_directive_check.

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. It doesn't disclose what happens if the title isn't found, whether partial article retrieval is via section/summary_only, or return format. The 'full or partial' phrase hints at partial retrieval but doesn't explain parameters' behavioral impact.

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?

Two sentences, no redundant information. The first sentence states purpose, the second provides a usage tip. Very 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?

The tool has no output schema and the description doesn't describe return values or error cases. For a simple get tool, it's still missing critical context about response format and behavior when the article doesn't exist.

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 three parameters have descriptions in the schema (100% coverage), so baseline is 3. The description adds only the 'exact title' guidance, which supplements title param but doesn't add meaning to section or summary_only beyond 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 uses the verb 'Get' with the resource 'article from Memory Alpha' and mentions full/partial scope. It is clear but doesn't explicitly distinguish from sibling tools like search_memory_alpha or get_random_article, though the 'exact title' hint provides some differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use exact article titles for best results' implies this tool is for known titles, but it doesn't state when to use search_memory_alpha or other alternatives. No explicit exclusions or when-not conditions.

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. 'Get' clearly implies a read-only operation with no side effects, which is helpful. However, it does not address behavior around ambiguous input combinations (e.g., what if both title and series are supplied), error handling, or data source freshness, leaving some gaps.

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 immediately states the action and resource, then lists return fields. Every word adds value, with no filler 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?

Despite the schema covering parameter definitions, the description does not explain how to uniquely identify an episode when all parameters are optional. It also lacks any return-structure details, and with no output schema the agent is left unsure whether to provide a title alone or series+season+episode. This ambiguity is a critical gap for a tool whose entire purpose is retrieving a specific episode.

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 fully describes all four parameters with example values and allowed series abbreviations, achieving 100% coverage. The description adds no parameter-level meaning beyond the schema, so the baseline score of 3 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 uses the specific verb 'Get' with the resource 'Star Trek episode' and enumerates the exact content fields (synopsis, writer, director, stardate, guest cast). This clearly distinguishes it from sibling tools like list_episodes or get_starship, which target different resources or scopes.

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 when details of a specific episode are needed, but it offers no explicit guidance on when to choose this over alternatives like list_episodes or search_memory_alpha. There are no stated exclusions or complementary tool references.

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 carry the burden. It states the action is 'get', implying a read-only operation, and mentions the source (Memory Alpha). However, it does not describe the return format, whether duplicates can occur, or any other behavioral details. The description adds minimal value beyond the name, but the operation is inherently non-destructive and straightforward.

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 core purpose and a use case. It is concise, front-loaded, and contains no filler or redundancy. Every word contributes to understanding.

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 optional parameter and no output schema, the description covers the essential purpose and a typical use case. It could be improved by mentioning what is returned (e.g., article titles, URLs, or full content), but this is a minor gap given the tool's simplicity and the clear context provided by the 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 schema description covers 100% of parameters (count has a full description: 'Number of random articles (1-5)'). The tool description does not mention the count parameter at all, so it adds no semantic value beyond the schema. The baseline of 3 applies because the schema already provides complete parameter documentation.

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: 'Get random Star Trek articles from Memory Alpha'. It uses a specific verb ('get'), a clear resource ('random Star Trek articles'), and a source ('Memory Alpha'). This distinguishes it from sibling tools like get_article or search_memory_alpha, which target specific articles or searches.

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 phrase 'great for discovery and trivia' provides a clear use context. It implies this tool is for exploratory or casual use rather than targeted lookup, though it does not explicitly state when not to use it or draw contrasts with siblings. This is sufficient for a simple tool but lacks explicit 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 the full burden. It implicitly indicates a read operation ('Get details') but does not disclose edge-case behavior such as exact-name matching, case sensitivity, or behavior when the ship is not found. This is acceptable for a simple lookup but lacks explicit behavioral 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, front-loaded sentence with no filler or repetition. Every word contributes meaning, listing both the action and the key output fields.

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 no output schema, the description provides adequate context by enumerating the returned attributes. It could be more complete by noting potential errors or format, but given the low complexity and high schema coverage, it is largely sufficient.

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 already provides 100% coverage for the single 'name' parameter, including an example. The description adds value by listing what the returned details will include, but it does not need to further explain the parameter syntax, so baseline 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 uses the specific verb 'Get' with a clear resource ('a Star Trek starship') and enumerates the attributes returned (class, registry, armaments, crew complement). This makes the tool's purpose immediately obvious and distinguishes it from sibling tools like get_episode or character_lookup.

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 clearly implies when to use this tool: when you need details about a starship. It doesn't explicitly name alternatives or exclusions, but the resource-specific phrasing provides clear context among the many 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Get events' without describing output format, ordering, completeness, or error handling. It doesn't state whether it returns a list, summary, or detailed entries, nor how it handles invalid years.

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 concise sentence that front-loads the action and resource. No filler words; every word earns its place.

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 low-complexity tool with one well-documented parameter, the description is minimally adequate. However, with no output schema, it fails to describe the return value (e.g., array of events, format), and it does not mention when to prefer this over the similar on_this_day tool. More context would improve usability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a clear description of the 'year' parameter, and the description's examples echo the schema's examples (2364, 22nd century). The description adds no additional semantic meaning beyond what the schema already provides, so baseline 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?

Clear verb 'Get' plus specific resource 'Star Trek events for a specific in-universe year'. Distinguishes from siblings like get_episode (focused on a single episode) by scoping to a year/era. The purpose is immediately understandable.

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 clearly specifies the use case: fetching events for a specific in-universe year. It doesn't explicitly call out alternatives or exclusions, but the 'in-universe year' qualifier provides clear context that differentiates from date-based tools like on_this_day.

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 does state that the tool generates three artifacts (designation, safety assessment, malfunction probability), which is useful. However, it omits details such as whether output is text vs. structured data, how the safety_protocols parameter influences behavior, and any edge cases or limitations.

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 conveys the core function and outputs without any filler. Every word contributes meaning, and it is properly sized for the tool's simplicity.

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 generation tool with rich schema coverage and no output schema, the description lists the output components, covering return-value expectations. It does not specify formatting or response structure, but that is not critical for a creative generation tool and the context is otherwise 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?

Schema description coverage is 100%, and both parameters are clearly described in the schema (scenario with an example, safety_protocols with default). The description itself adds no parameter-specific meaning beyond confirming the generation scope, so it meets the baseline but does not exceed it.

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 specifies a clear verb ('Generate'), a distinct resource ('Star Trek holodeck program'), and three concrete outputs (designation, safety assessment, malfunction probability). This distinguishes it from sibling tools like episode_recommender or diplomatic_scenario, which serve different creative or reference functions.

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 the tool is used when a user wants a holodeck program, but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites. The context is clear but not framed as 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?

No annotations are provided, so the description carries full responsibility for disclosing behavior. It only states 'List all episodes' without mentioning the response format (e.g., titles only or full details), ordering, pagination, error conditions, or any side effects. This is a minimal statement that leaves the agent uncertain about what to expect.

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 unnecessary words. It efficiently communicates the core purpose without padding.

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 list tool with well-documented parameters, the description is minimally adequate. However, without an output schema or annotations, it would benefit from specifying what the list contains (e.g., episode titles, air dates) to fully prepare the agent. The description is not misleading but lacks these details.

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%: both 'series' and 'season' have descriptive text. The tool description adds no new information about the parameters beyond what the schema already offers, so the baseline score of 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 uses the specific verb 'List' with a clear resource ('all episodes for a Star Trek series season'). It directly distinguishes itself from sibling tool 'get_episode' (which presumably retrieves a single episode) by specifying 'all episodes' and the scope of a season.

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 when to use the tool (when you need a complete list of episodes for a given season), but it does not explicitly state alternatives or exclusions. For example, it does not mention that 'get_episode' should be used for a single episode or that 'search_memory_alpha' may be better for textual queries.

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 burden of behavioral disclosure. It adds useful context by noting that the tool 'Checks Memory Alpha for a dedicated mirror article,' implying a read operation with potential for no dedicated article. However, it does not specify what happens when no mirror article exists or the exact nature of the returned content, leaving some ambiguity.

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, front-loaded with the primary purpose. The second sentence adds valuable behavioral context without fluff. Every word earns its place, making it highly 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?

For a simple one-parameter lookup tool with no output schema, the description is complete. It explains what the tool does, what kind of subjects it handles, and its data source. There is no nested complexity or additional behavior that would require more explanation.

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 already provides a comprehensive description for the 'subject' parameter, covering what it accepts and examples. The tool description echoes the same concepts (character, ship, or event) without adding new details. With schema coverage at 100%, the baseline of 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 tool's function: to describe the mirror universe version of a Star Trek character, ship, or event. This distinguishes it from sibling tools like character_lookup or get_starship by specifying the mirror universe scope and the source (Memory Alpha).

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: when you need the mirror universe counterpart of a Star Trek subject. It does not explicitly name alternatives or exclusion conditions, but the specific scope is evident enough that the usage context is 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?

With no annotations, the description carries the full burden of behavioral disclosure, but it only states the type of data returned. It does not mention return structure, ordering, pagination, error conditions, or side effects. The gap between 'for a specific date' and the schema's default-to-today behavior also leaves room for misinterpretation.

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 states the action and the subject without filler. Every word earns its place, making it highly 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?

The tool is simple (2 optional params, no output schema), and the description covers the main purpose. However, it lacks usage guidance and behavioral transparency, which would help an agent decide when to invoke it and what to expect. The schema fills in parameter details, so the overall picture is minimally complete but leaves 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 input schema already fully documents both parameters (date and timezone) with format and defaults, so the description adds little beyond what the schema provides. The phrase 'for a specific date' aligns with the date parameter but doesn't enrich it. Baseline 3 is appropriate given 100% 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 the tool's verb ('Get'), resource ('Star Trek events, air dates, and birthdays'), and scope ('for a specific date'). It is specific enough to convey the core function, but it does not explicitly differentiate from adjacent sibling tools like get_timeline or list_episodes, which might also involve date-based 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 implies usage: call this when you want events for a given date. However, it provides no explicit guidance on when to prefer this over alternatives, no exclusions, and no mention of whether it works without a date (though the schema says it defaults to today).

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 the assessment is humorous and Starfleet-themed, which sets expectations for tone. However, without annotations, it does not clarify side effects (e.g., read-only) or what form the assessment takes (e.g., a verdict string, a 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 a single, front-loaded sentence that conveys the core purpose and tone without unnecessary words.

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, but the description omits the return format or any behavioral details. It does not specify what the 'assessment' looks like, and without an output schema the agent must infer the result structure from the tool name 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 schema already provides a full description for the single 'action' parameter, so the description adds no additional parameter semantics. The tool's description of 'evaluate' does not go beyond the schema's 'The action to evaluate'.

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 'evaluate' with a clear object ('whether an action violates the Prime Directive'), and adds a unique tone ('humorous Starfleet assessment'). It clearly distinguishes from generic tools and indicates the tool's 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 the tool is for checking Prime Directive compliance, but it does not specify when to prefer it over similar tools like first_contact_assessment or diplomatic_scenario, nor does it mention any exclusions or alternatives.

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 only says 'assess survival odds' but does not explain how the assessment works, what factors influence it, whether it returns a probability, a verdict, or a humorous response, or any limitations. This is a significant transparency 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 a single, memorable sentence that front-loads the action and maintains a strong thematic voice. It is concise, with no fluff, and perfectly sized for a playful tool like this.

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 output schema, the description should clarify what the agent should expect in return. It does not mention return types, example outcomes, or any caveats about the analysis. While the tool is simple, the description leaves too much to inference and is incomplete for fully informed 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?

Schema coverage is 100% for the single parameter, so the baseline is 3. The description adds no parameter-specific information, but the schema already provides a clear description with an example, so the agent can understand the required input without additional explanation.

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 a specific action—'assess survival odds'—and ties it to a memorable Trek theme. It distinguishes itself from sibling tools like battle_simulator by focusing on the 'redshirt' survival concept. However, it lacks explicit scope about what exactly is analyzed or what kind of output is produced, so it doesn't reach a 5.

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, no scenario description, and no mention of any exclusions or prerequisites. For an agent, the description gives no hint about whether this is a standalone fun tool or a serious analysis, leaving it ambiguous.

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 for behavioral disclosure. It says only 'Look up', which implies a read-only operation, but it does not state what is returned, how conflicting parameters (e.g., both random and rule_number) are handled, or what happens with no parameters. The description adds minimal behavioral context beyond the tool's name and schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence that clearly enumerates the tool's functionality without any fluff. Every word contributes meaning, and it is shorter and more scannable than a longer multi-sentence explanation would be.

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 three optional, fully documented parameters, the description covers the principal usage modes. However, there is no output schema and the description does not specify the return format or behavior for empty/conflicting inputs, which would make it fully complete. It is adequate for most agent invocations but leaves a few edge cases undocumented.

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 fully documents all three parameters (random, search, rule_number) with descriptions, so the baseline is 3. The description restates these modes in prose but does not add any new detail about parameter formats, validation, or precedence. It provides marginal semantic value on top of 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 states a clear verb ('Look up') and a specific resource ('Ferengi Rules of Acquisition') while listing three distinct modes: by number, randomly, or by keyword. This is specific and immediately distinguishes the tool from the sibling Star Trek lookup 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 implies when to use the tool (whenever a user needs a Rule of Acquisition) and describes the three invocation modes, but it does not explicitly contrast it with alternatives like search_memory_alpha or get_article. There are no exclusions or conditional guidance, so it offers only implied usage 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 must disclose behavioral traits. It only states what is searched, not how results are returned, whether it is read-only, pagination, or result ordering. The description adds minimal behavioral context beyond the basic search scope.

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, clear, and front-loaded sentence with no wasted words. It effectively communicates the tool's purpose and scope in a concise manner.

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 no annotations, no output schema, and the description is minimal. It does not explain what the search results look like, how to interpret them, or when to use the tool. For an agent to invoke it correctly, more context about return values and usage context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both 'query' and 'limit', including examples and constraints. The description does not add any parameter-specific meaning, so the baseline of 3 for high schema coverage 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 tool's function with a specific verb ('Search'), resource ('Memory Alpha'), and scope ('Star Trek articles, characters, ships, episodes, and more'). This distinguishes it from sibling tools like get_episode or get_starship, which target specific content 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 implies general search usage but provides no explicit guidance on when to choose this tool over more specific siblings (e.g., get_article, character_lookup). No alternatives or exclusions are mentioned, so the context is only implicit.

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. It explicitly lists the types of information returned, providing insight into the output, and implies a read-only operation via 'Get.' It stops short of disclosing edge-case behavior or response format, but for a simple lookup this is moderate-to-good 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?

One concise sentence, front-loaded with the primary action, with no filler. Every word adds value.

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 single-parameter read tool with no output schema, the description adequately specifies what the tool returns (homeworld, physiology, culture, quadrant) and makes the use case clear. No further details are necessary.

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 already fully describes the only parameter 'species' with examples. The description adds no additional parameter-level meaning, so the baseline of 3 for high schema coverage 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 states the verb 'Get' and the resource 'info about a Star Trek species,' and enumerates specific facets (homeworld, physiology, culture, quadrant), distinguishing this from sibling tools like get_starship or character_lookup.

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 when to use this tool—when species information is needed—by listing exact data types returned. It does not explicitly name alternatives or exclusions, but the context is clear enough for correct 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?

No annotations are provided, so the description must carry the full burden. It states the conversion behavior ('convert', 'get current stardate'), which implies a read-only computation, but does not disclose details like output format, behavior when both date and stardate are supplied, or absence of side effects.

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?

Single sentence, front-loaded with the core action, and no redundant filler. Every word earns its place.

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 three optional parameters and no output schema. The description does not clarify that exactly one of date/stardate should be provided, nor what happens if both or neither are given. This is a notable ambiguity for an otherwise simple converter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptive parameter entries, so baseline is 3. The description adds no extra meaning beyond echoing 'real-world dates' and 'stardates' already present in 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's verb ('convert') and resource ('between stardates and real-world dates'), and adds the distinct capability to get the current stardate. This is specific and differentiates it from siblings like get_timeline or temporal_incursion.

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 date conversion queries but provides no explicit guidance on when to choose this tool over alternatives, nor any exclusions. The intended use is clear from the purpose, but no alternative tools are mentioned.

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 burden. It discloses key behavior: the tool returns a game_id and question set, and it explicitly instructs the agent to store game state in session memory and track scores, which is essential operational context not obvious from the schema. It does not cover potential side effects like rate limits, but for a generation tool this is adequate.

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?

Two sentences, front-loaded with the primary action, followed by return value and an important state-handling instruction. Every sentence earns its place; no filler 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?

The description is complete for the tool's complexity: it states what is returned (game_id and question set) and provides the necessary state management context. Without an output schema, it does not detail the question set structure, but that may be acceptable for starting a game. The instruction about session memory fills a critical gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the parameter descriptions already provide sufficient meaning. The tool description adds no new parameter detail but the multi-player context implies why difficulty and number of questions matter. This meets the baseline for full schema coverage without needing additional explanation.

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 ('Generate') and clearly identifies the resource ('a bundle of Star Trek trivia questions') and context ('for a multi-player game session'). It distinguishes the tool from siblings like check_trivia_answer and trivia_quiz by emphasizing multi-player session initiation.

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: this tool is for creating a multi-player game session, implying it should be used when starting a game. It does not explicitly name alternatives or exclusions, but the multi-player qualifier distinguishes it from single-quiz tools, making the usage context clear without explicit when-not 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?

With no annotations, the description is the sole source of behavioral transparency. It implies a read-only lookup ('look up', 'describe') and mentions a secondary capability ('fetch details from Memory Alpha'), but it does not disclose whether the operation is read-only, any required permissions, rate limits, or failure behavior. It adds some context but leaves significant gaps.

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 consists of two concise sentences that directly state the primary purpose and a secondary capability. There is no redundancy, and the information is front-loaded with the core 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?

For a tool with a single optional parameter and no output schema, the description covers the essential function but misses details about the response format, the random event behavior (only present in the schema), and how the Memory Alpha integration is triggered or used. It is adequate but not comprehensive.

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 already provides a complete description of the single parameter, including examples and the default random behavior. The tool description adds no additional parameter semantics, so with 100% schema coverage the baseline score of 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 specifies the tool looks up Star Trek time-travel events and describes the temporal paradox and resolution, which distinguishes it from generic episode or article lookups like get_episode or search_memory_alpha. However, it does not explicitly contrast with sibling tools, so it loses a point for not fully differentiating.

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 over alternatives such as get_episode or get_timeline. It does not mention any exclusions, prerequisites, or preferred contexts, leaving the agent without enough information to make a selection decision.

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 carries the full burden of behavioral disclosure. It only says the question comes from 'random Memory Alpha facts', but does not explain output format (e.g., multiple choice, with answer), potential non-determinism, or any prerequisites. This leaves the agent uncertain about what to expect from the 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 a single sentence of 10 words, with no filler. Every word contributes to the core purpose, making it highly 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 tool is simple with one optional parameter, but there is no output schema. The description says it generates a trivia question but doesn't specify whether the answer is included, whether choices are provided, or any other return details. This is a gap given the context, but the tool is simple enough that the description is minimally viable.

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% because the only parameter 'difficulty' has a description and enum values. The description text adds no additional meaning about how difficulty affects the generated question, so the baseline score of 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 uses the specific verb 'Generate' and identifies the resource as 'a Star Trek trivia question', which clearly states the tool's action. It also distinguishes from sibling tools like start_trivia_game and check_trivia_answer by focusing on a single question generated from Memory Alpha facts.

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 start_trivia_game or who_said_it. It doesn't mention when a standalone question is appropriate or when another tool would be better, leaving usage context entirely to the agent.

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, so the description carries full responsibility. It only states the challenge concept but does not disclose how the tool behaves: whether it presents a quote, expects an answer, evaluates the response, or returns a result. This lack of interaction details is a significant gap for an unannotated 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 a single sentence that effectively communicates the core purpose without any fluff or redundancy. It is front-loaded and every word contributes to understanding.

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 no output schema and no annotations, but the description does not explain what the challenge returns or how the agent should interact with it. The single parameter is well-documented in the schema, but the overall interaction flow and expected outputs are missing, leaving the description incomplete for agent use.

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 100% description coverage for the only parameter (difficulty), with an enum and default. The description adds no extra parameter information beyond the schema, so baseline 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 identifies the tool as a quote attribution challenge, specifying the action (guess which Star Trek character said it). This distinguishes it from search and reference tools among siblings. However, it could be more explicit about the game format, so it doesn't earn a 5.

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: when a quote attribution challenge is desired. There is no explicit guidance on when to use this instead of other game tools like trivia_quiz or away_team_builder, and no stated exclusions. This is borderline between implied and missing guidance.

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