hvilkenai-mcp

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

No annotations are provided, so the description carries the full burden. It discloses the update schedule (daily at 07:30 CET) and what data is shown (ranked models with scores), but it does not explicitly state that the tool is read-only, mention required permissions, or describe any side effects. The update timing is useful context, but more behavioral details would improve transparency.

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

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

The description consists of two sentences, front-loaded with purpose, and every sentence adds value. There is no wasted text.

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

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

No output schema is provided, and the description only briefly mentions 'ranked models with scores' without detailing the exact output structure, possible errors, or pagination. For a simple tool with two parameters, this is acceptable but not complete; more detail on the return format would improve completeness.

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

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

Schema coverage is 100% with enum descriptions for both parameters. The description adds extra context by specifying the benchmark includes scores for Norwegian, instruction following, and speed, which goes beyond what the schema provides. This adds meaning, so 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 retrieves daily AI benchmarks for Scandinavian languages, showing ranked models with scores for Norwegian, instruction following, and speed. It uses a specific verb ('henter') and resource ('dagens AI-benchmark'), and the purpose is distinct from sibling tools like get_model_history or get_weekly_summary.

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 daily benchmarks but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not or exclusions. The sibling tools have different focuses, so an agent can infer, but explicit guidance is lacking.

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 discloses data retrieval behavior (historical per-day scores) and access limitations (free/paid). However, it does not mention response format, pagination, or rate limits, which would improve transparency.

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

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

The description is three short sentences, all front-loaded with the core purpose. No unnecessary words or repetition. Highly concise and structured effectively.

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 explains what the tool does and the data it retrieves (scores per day), but lacks any specification of the output format or structure. Since there is no output schema, the description should at least hint at whether it returns an array, object, or specific fields. This gap reduces 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?

Schema description coverage is 100%, so the parameter descriptions in the schema already provide detailed semantics. The tool description adds the 'free vs paid' context for the days parameter, but this is also present in the schema's description. Thus, the description adds marginal value beyond the schema.

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

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

The description clearly states the verb ('Henter' = retrieves), resource (historical benchmark data for one AI model), and scope (over time, per day). It effectively distinguishes from sibling tools like get_daily_benchmark and get_weekly_summary which serve different temporal granularities.

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

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

The description mentions free vs paid limitations (3 vs 30 days) but does not explicitly guide when to use this tool over siblings like get_daily_benchmark or get_weekly_summary. Context is provided but not directive usage guidelines.

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 includes the score formula, which provides some transparency about the calculation. However, it does not disclose data source freshness, error handling, or whether the tool is read-only (though implied). Without annotations, it partially carries the burden.

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 sentences: first states purpose, second provides the formula. No redundant information.

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

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

For a simple tool with one optional enum parameter and no output schema, the description explains the purpose and scoring. It could clarify the score range or interpretation, but overall it's sufficiently 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% with one parameter (language) described via enum and description. The tool description adds the formula but does not explain how the parameter relates to 'norsk' in the formula. 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 states the tool provides a ranking of AI models suitable as orchestrators in multi-agent systems, with a specific formula for the score. It distinguishes from siblings by focusing on the orchestrator role.

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

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

No guidance on when to use this tool over siblings like get_recommendation or get_model_history. No prerequisites or when-not-to-use conditions 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 full burden of behavioral disclosure. It states the tool is based on a daily benchmark but does not disclose any additional traits such as caching, rate limits, data sources, or whether results are deterministic. The minimal description leaves significant gaps for an 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 two sentences, concise and front-loaded. The first sentence states the core function, and the second adds detail about free vs paid behavior. Every sentence adds value. Slightly higher score is warranted for efficiency.

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

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

With 3 optional parameters and no output schema, the description is too sparse. It does not explain the output format (e.g., what the top 3 list includes, justification style), nor does it cover data recency or prerequisites. For a recommendation tool, this missing context is significant.

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 clear descriptions for all three parameters. The description adds value by specifying that for 'free' budget the tool returns the best choice, while for 'paid' it returns top 3 with justification. This provides behavioral nuance beyond the enum values. However, the benefit is moderate.

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 recommends the best AI model for a specific use case and budget based on daily benchmark. The verb 'Anbefaler' and resource 'beste AI-modell' make the purpose explicit. It is distinct from sibling tools like get_daily_benchmark or get_model_history, which provide raw data rather than recommendations.

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

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

The description mentions different behavior for free vs paid budget options ('Gratis: beste valg. Betalt: topp 3 med begrunnelse.'), giving some context on when to use the tool. However, it does not explicitly state when to use this tool instead of siblings, nor does it provide exclusions or prerequisites.

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 adds behavioral context by stating the report is generated each Friday, implying a static, pre-generated nature. However, it does not disclose if the data is read-only, if there are any destructive effects, or latency considerations.

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

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

The description is extremely concise with two sentences. It front-loads the purpose and adds a critical behavioral note about generation frequency. No wasted 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?

Given the tool has only two optional parameters and no output schema, the description adequately covers what the tool returns (winner, trends, reliability analysis) and when it is available (each Friday). It could be more complete by noting that it is a read operation, but for a simple summary tool this is 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by providing default values for both parameters ('nåværende uke' for week and 'no' for language), which are not present in the JSON Schema. This helps the agent understand typical usage.

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

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

The description clearly states it provides a weekly summary of AI benchmarks including winner, trends, and reliability analysis. It distinguishes itself from sibling tools like get_daily_benchmark by specifying 'ukens' (weekly) and 'oppsummering' (summary).

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

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

The description does not provide explicit guidance on when to use this tool versus alternatives such as get_daily_benchmark or get_model_history. The word 'weekly' implies a temporal scope, but no when-not or alternative suggestions are given.

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