hvilkenai-mcp

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

With no annotations, description discloses data freshness (delay vs real-time), model count limits, and update frequency. Could mention that it's read-only and no side effects, but current info is sufficient for typical use.

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?

Three efficient sentences in Norwegian, no wasted words, front-loaded with key action. Perfectly 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?

Given no output schema, description explains what data is returned (benchmarks) and its freshness. Lacks detail on return format, but for a simple tool this is adequate. Good job for no annotations.

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 already covers both parameters with enums and defaults. Description adds no additional meaning beyond what schema provides. Baseline 3 for 100% coverage.

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

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

Description clearly states it returns benchmark results for AI models in Scandinavian languages, with specifics on free vs API access and update schedule. Distinct from siblings (daily benchmarks vs history, ranking, recommendation, 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?

Description provides context on when to use free vs API version (24h delay vs real-time) and update time. However, no explicit guidance on when to choose this over sibling tools, though the name and purpose imply it's for daily benchmarks.

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?

With no annotations, the description bears full burden. It discloses behavioral traits: the score formula, data freshness (yesterday's vs real-time), and access limitations (top 3 vs all models). This goes beyond basic purpose but could specify output format or pagination.

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

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

The description is concise, providing purpose, formula, and tier details in a single paragraph. It is front-loaded but could benefit from clearer separation of the tier information.

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

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

Given the single parameter and no output schema, the description covers the tool's core behavior and access model. However, it lacks guidance on output format and how this tool relates to siblings like get_recommendation.

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 one parameter 'language' with a description, and the schema coverage is 100%, but the description itself does not mention the parameter at all. It adds no meaning beyond the schema, which only says 'Språk. Standard: no' without explaining how language affects the ranking.

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: ranking AI models suitable as orchestrators in multi-agent systems. The verb 'ranking' and resource 'AI models' are specific, and the description distinguishes from siblings by its unique focus on orchestrator suitability.

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 (for orchestrator ranking) and differentiates between free and API-key tiers, but it does not explicitly state when not to use or provide alternatives among 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?

Since no annotations are provided, the description carries full burden. It discloses key behavioral traits: free mode has a 24-hour delay, API key mode provides real-time data with top 3 recommendations and reasoning. There is no mention of side effects or destructive actions, which is appropriate 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?

Two sentences with no fluff, front-loaded with main purpose. Efficient and clear.

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 3 optional enum parameters and no output schema, the description covers the key behavioral differences and output expectations. It lacks details on error handling but is sufficient for a simple recommendation tool.

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

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

Schema coverage is 100% with descriptions for all 3 parameters. The description adds no additional meaning beyond the schema, so 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 recommends the best AI model for a use case and budget, with specific verb (recommends) and resource (AI model). It distinguishes itself from sibling tools like get_daily_benchmark and get_model_history by focusing on recommendations rather than benchmarks or history.

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 guidance on when to use: for obtaining a recommendation based on use case and budget. It differentiates between free usage (with 24h delay) and API-key usage (real-time, top 3 with reasoning). However, it does not explicitly state when not to use or contrast with siblings.

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

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

With no annotations 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.