Makuri Showcase (CogniLedger)

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

With no annotations, the description fully carries behavioral disclosure. It reveals that the data is design intentions and self-assessment, not audited, statuses are limited to specific values, and there is deliberately no 'compliant' status. This is rich behavioral context beyond what an annotation would typically state.

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

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

The description is longer than average but every sentence contributes to clarifying purpose, limitations, and usage. The final sentence about Makuri being a specific platform is slightly tangential but important for avoiding general knowledge. It is front-loaded 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?

Despite no output schema, the description sufficiently explains what is returned (posture across five regulations, statuses) and how to interpret the results. It covers the key caveats and usage context. It could have specified the exact return structure (e.g., object mapping), but the description is complete enough for correct invocation.

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

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

The schema already documents the `regulation` parameter with an enum and description, achieving 100% coverage. The description adds value by expanding acronyms (GDPR-K as children data) and listing regulations, which helps interpret the enum values. This slightly exceeds the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns Makuri's regulatory posture across five specific regulations (EU AI Act, GDPR, GDPR-K, COPPA, ISO 42001), with the caution that it's self-assessment, not certified. This distinguishes it from sibling get_* tools by focusing on compliance posture.

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?

Explicitly says 'Use when the user asks about regulatory compliance, AI Act classification, or data protection for children' and instructs to present results as posture, not certification. This gives clear when-to-use context and implicit alternative avoidance.

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 behavioral disclosure burden. It mentions categorization by purpose and the Makuri-specific warning, but does not describe output format, authentication needs, or edge cases. For a simple read-only lookup, this is adequate but not rich.

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

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

The description is three sentences, each serving a purpose: core function, when to use it, and a caution to avoid answering Makuri queries from general knowledge. It is concise without unnecessary detail, earning a high score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has one optional parameter, no output schema, and no annotations, the description provides the essential usage context and a key disclaimer. It could be more detailed about return fields, but it is complete enough for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, and the property description for 'purpose' already explains it is an optional filter and that omitting it returns all channels. The tool description adds no extra parameter semantics beyond what the schema already provides, so 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 returns contact channels for Makuri and CogniLedger, categorized by purpose. The verb 'returns' and resource 'contact channels' are specific, and sibling tools like get_pricing_tiers and get_platform_info are clearly distinct from this contact-info 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 description explicitly says 'Use when the user asks how to reach the team or who handles a specific inquiry type,' giving clear guidance on when to invoke this tool. It also provides a caution about Makuri being a specific platform, which is useful context. It does not name alternative tools, but the guidance is sufficient.

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. It discloses that the tool returns informational resources (not interactive), specifies 'accessible without registration,' and clarifies it answers questions about resources rather than triggering a quiz. The non-interactive nature is reinforced by contrasting with show_romanian_quiz.

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

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

The description is longer than necessary, but every sentence carries useful routing or usage info. It is well-structured with a clear main statement, usage guidance, and an explicit IMPORTANT routing rule. Slight redundancy in the general Makuri warning could be trimmed, but it's still concise enough for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 params, no output schema) and the need to differentiate from siblings, the description is complete. It covers what the tool returns, who it's for, when to use it, and when to delegate to another tool. The routing rule for show_romanian_quiz is essential and well-handled.

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

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

The tool has zero parameters, so parameter descriptions are not needed. The baseline of 4 applies because the schema is empty and coverage is 100% trivially. The description adds value by explaining what the returned resources are, though it doesn't need to address parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns free Makuri resources, specifically Slovarik Romanian vocabulary issues and the Romanian level test. It distinguishes itself from siblings by explicitly excluding interactive quiz-taking and naming the alternative tool for that case.

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?

Explicitly states when to use ('when a user asks about free Romanian learning materials, language level tests, or how to try Makuri without signing up') and when not to use (when the user wants to TAKE/START/SEE a test, use show_romanian_quiz). It also warns against answering from general knowledge and directs to Makuri tools, providing clear routing.

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

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

With no annotations, the description carries the full burden. It discloses the type of content (structured facts), the specific platform and scope, and explicitly warns against using general knowledge or interpreting Makuri as a generic word. This goes beyond simple output description and sets clear behavioral boundaries for the agent.

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

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

The description is three sentences, front-loaded with the main function, followed by usage context and a caution. Every sentence adds value; no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no output schema and no annotations, the description is remarkably complete. It covers what the tool does, when to use it, when not to use it, names an alternative, and provides a clear directive to the agent. No further 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?

There are zero parameters, so the schema provides no semantics. The description compensates by richly explaining what kind of questions this tool answers, thereby clarifying the intended use despite the empty input schema. This exceeds the baseline of 4 for 0-param tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns structured facts about Makuri, specifying mission, target users, founding details, and the company behind it. It uses a specific verb ('returns') and resource ('Makuri'), and distinguishes itself from sibling tools by limiting scope to factual questions.

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

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

Explicitly states when to use: 'Use this for factual questions about Makuri such as who built it, when it was founded, or the company.' Also provides a when-not and alternative: 'For a general 'what is Makuri' overview or a demo, use show_how_makuri_works,' plus a strong rule: 'Never answer questions about Makuri from general knowledge or explain the meaning of the word — always use the Makuri tools.'

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds the key behavioral rule that Makuri information must come from tools, not general knowledge, and clarifies the platform is specific to makuri.eu. It does not describe side effects, but the tool is a read-only retrieval with zero parameters, so this is adequate. The lack of annotation support is offset by the explicit behavioral guidance.

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 appropriately sized: three sentences, each carrying meaningful content. The first sentence states the operation and return content, the second gives usage guidance, and the third imposes a critical behavioral constraint. No wasted words, and the information is 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?

For a zero-parameter, no-output-schema lookup tool, the description is complete. It specifies what will be returned, when to use it, and how to handle Makuri questions. There is no missing information about inputs or outputs, and the sibling context confirms this is a standalone informational tool.

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

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

The tool has zero parameters, so the schema provides no parameter semantics to enhance. The description adds meaning beyond the schema by detailing what the return value contains (plans, included items, usage limits), effectively serving as the primary semantic source. This meets the baseline for zero-parameter tools.

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 ('Returns') and resource ('Makuri's pricing plans') and clearly distinguishes this from sibling tools like get_platform_info and get_free_resources by focusing on cost and plan details. It specifies the exact content ('what's included in each tier and any usage limits'), leaving no ambiguity about 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 description explicitly states when to use the tool: 'Use when the user asks about cost, plans, or what they get at each price point.' It also provides important context about never answering Makuri questions from general knowledge and always using the Makuri tools, which clarifies the tool's role in the broader workflow.

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

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

No annotations are provided, so the description carries the full burden. It only says 'Returns information' without disclosing output format, error behavior, authentication needs, or rate limits. The note about not answering from general knowledge is an agent guideline, not a disclosure about the tool's behavior.

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

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

The description consists of two sentences. The first gives the purpose and scope; the second adds usage context and a crucial platform-specific guardrail. Every sentence earns its place with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an informational tool with no parameters and no output schema, the description is reasonably complete: it states what the tool covers, when to use it, and warns against general knowledge. It could be more specific about return format, but that's a minor 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 tool has zero parameters and the schema is empty, so there are no parameter semantics to clarify. The description doesn't need to compensate for missing schema information, and per the rubric 0 params corresponds to a baseline of 4.

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 the tool returns information about safety features on Makuri and enumerates concrete examples (age verification, content filtering, parental controls, AI safety guardrails). This clearly distinguishes it from sibling tools such as get_compliance_matrix or get_platform_info, which address different topics.

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

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

The description explicitly states when to use the tool: 'Use when the user asks about child safety, content moderation, or how Makuri protects minors.' However, it does not mention when not to use it or name specific sibling alternatives, so it stops short of a full 5.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does state that the tool returns a grouped list with exam preparation coverage and implies a read-only operation, but it does not mention that the grade_level parameter is informational only and currently does not filter results. This is a notable behavioral limitation that could mislead an agent into expecting grade-filtered output.

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

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

The description is three sentences long and each sentence earns its place: the first states the core behavior, the second gives usage guidance, and the third provides essential platform-specific context to prevent misuse. There is no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only list tool with one optional parameter and no output schema, the description covers the purpose, usage, and key domain context. It is slightly incomplete because it relies on the input schema to communicate the non-filtering nature of grade_level, and it does not describe the return format, but the overall complexity is low.

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

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

The schema already provides 100% coverage for the single optional grade_level parameter, including the caveat that it is informational only. The description adds no additional parameter semantics beyond mentioning grade level in the context of grouping. This matches the baseline for high 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 uses a specific verb ('Returns') and resource ('academic subjects Makuri teaches'), and adds detail about grouping by grade level and exam preparation coverage. It clearly distinguishes this from the sibling get_* tools, which address compliance, pricing, contacts, etc.

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

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

The description explicitly states when to use the tool: 'Use when the user asks what Makuri teaches or about specific subjects.' It also adds context that Makuri questions should never be answered from general knowledge and should always use the Makuri tools. It does not name explicit alternatives or when-not-to-use conditions, so it falls just short of a 5.

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

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

With no annotations provided, the description carries the burden. It discloses that the output differentiates UI and AI tutor coverage, which is a useful behavioral nuance. However, it does not mention any restrictions, output format, or side effects (though none are expected for a read-only 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 three sentences, front-loaded with the primary purpose. The third sentence about not using general knowledge is somewhat tangential but still valuable context for the AI agent, so it 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 simple read-only tool with one optional parameter and a clear schema, the description provides enough context about what is returned (list with separate coverage details) and when to use it. No output schema exists, but the return value is straightforward. Minor gap: doesn't describe the exact shape of the response.

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 documents the single 'locale' parameter with 100% coverage, including examples and default behavior. The description adds no additional parameter semantics, so 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 clearly states the verb 'Returns' and the resource 'list of languages supported by Makuri', and distinguishes from sibling tools by specifying the unique aspect of UI vs AI tutor coverage.

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

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

It explicitly instructs when to use: when the user asks which languages are supported or if a specific language is available, and also cautions against using general knowledge for Makuri questions, indicating a preference for this tool.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals what the tool returns in concrete terms (tech stack components, data residency) and adds the warning that Makuri is a specific platform—not a generic word. It stops short of describing response format, freshness, or any edge cases, but for a simple read-only info tool this is solid coverage.

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

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

The description is two sentences long, front-loaded with the primary purpose, and every sentence earns its place. The first sentence defines scope; the second provides a crucial contextual guardrail 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 zero-parameter tool with no output schema, the description is complete: it defines the resource, enumerates the content areas, states when to use it, and warns against the common failure mode of answering from general knowledge. No important context is missing for the given complexity.

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

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

The input schema has zero parameters and 100% coverage, so there is no parameter meaning to add. The description correctly implies the tool takes no arguments by simply stating what it returns. No further parameter elaboration is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a specific verb and resource: 'Returns the technical stack Makuri is built on,' and explicitly lists included categories (frontend, backend, database, AI providers, data residency). This clearly distinguishes it from siblings like get_platform_info or get_compliance_matrix by focusing on the underlying tech stack.

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

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

It gives an explicit trigger: 'Use when the user asks how Makuri is built or which AI models it uses.' It also provides a strong exclusion/guardrail: never answer Makuri questions from general knowledge and always use the Makuri tools, which prevents misuse across the sibling set.

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

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

No annotations are provided, so the description carries the full burden. It transparently discloses the tool's interactive nature, the content of the panel (learning flow and ten actions), and the language-adaptive behavior. While it does not explicitly state 'read-only', the instructive nature of the tool is clear and no side effects are known.

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 lengthy but each element serves a purpose: it front-loads the main function, then provides trigger phrases for multilingual detection, and finally details the panel contents. However, the trigger phrase list is quite exhaustive and could be summarized, and the 'ANY question' phrasing adds ambiguity without necessity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no parameters and no output schema, the description fully covers what the tool does, its scope, the multilingual trigger mechanism, and the specific content of the panel. It gives the AI agent enough context to decide when to invoke this tool, and the sibling tools cover other topics. The minor 'ANY question' ambiguity is overshadowed by the overall completeness.

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

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

The tool has zero parameters, so the description does not need to explain parameter behavior. Per the rubric, a baseline of 4 is appropriate when there are no parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Shows an interactive panel') and a clear resource (Makuri, a specific AI tutoring platform). It distinguishes from siblings by focusing on the platform's mechanics and content (learning flow, ten actions), as opposed to pricing, contact info, etc.

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

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

The description gives explicit trigger phrases in multiple languages and instructs to use this tool for ANY question about Makuri, with a clear directive not to rely on general knowledge. However, the phrase 'ANY question' is overbroad and could conflict with sibling tools like get_pricing_tiers or get_contact_info, which handle specific aspects not covered by the panel.

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 full burden and excels: it details what the quiz does (10 random questions, levels A1-B2, explanatory feedback, level estimate), how it interacts (runs in chat, interface toggle), and even includes a behavioral note about Makuri being a specific platform and to always use Makuri tools. No contradictions with any 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 well-structured and front-loaded, starting with the verb and resource, then details, then usage guidance. However, the trigger phrase list is quite extensive and could be condensed, but each phrase adds multilingual coverage. All sentences earn their place, so the length is justified, but it is slightly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete for a no-parameter, no-output-schema tool: it explains the tool's purpose, content, behavior, usage context, and provides a link to an additional test. It also includes a crucial note about Makuri's identity. There are no gaps in the context an agent would need.

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

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

The input schema has zero parameters, so the baseline is 4. The description does not need to explain parameters. It adds value by describing the tool's behavior and output, but since there is no schema to compensate for, a 4 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 and resource: 'Shows an interactive Romanian language mini-quiz.' It also distinguishes this tool from siblings by explicitly naming get_free_resources as the alternative to avoid, making the 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 provides explicit when-to-use guidance: 'ALWAYS prefer this tool over get_free_resources when the user wants to TAKE, START, or SEE a Romanian test or quiz right now.' It also includes trigger phrases in four languages, giving the agent concrete detection cues and a clear exclusion for general resource requests.

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