AgentKernel AI model data

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

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description genuinely adds that price is displayed but excluded from ranking, which is non-obvious behavior, but says nothing about response shape, ordering details, or how ties are handled.

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?

Front-loaded with the core ranking behavior in the first clause, then a single dense sentence enumerating covered task domains. Every part earns its place, though the long taxonomy list reads as a run-on rather than an optimized structure.

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 no output schema, the description carries the burden of describing returns, and 'price shown' is the only hint about response contents; ordering, count, and result fields are unaddressed. The limit and license filters go entirely unmentioned, so a three-parameter tool is only partially covered.

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 67%, with the license parameter already documented in the schema and task's enum giving the allowed slugs. The prose enumerates task families (text, coding, agents, image/video, audio, embeddings) but omits 'overall' from the enum and never mentions the limit or license parameters, so it only partially compensates.

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 specific verb and resource ('Ranks AI models for one task') with a clear scope modifier ('by performance only'). The parenthetical carve-out that price is shown but never used for ranking effectively separates it from cheaper_alternative, though no sibling is named explicitly.

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 'by performance only (price shown, never used for the rank)' implies that this is the performance-first tool as opposed to price-oriented siblings, but the agent has to infer that routing. There is no explicit 'use X when Y' statement or exclusion of 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?

Annotations already declare readOnly, idempotent, and closed-world behavior, so safety is covered. The description adds genuine behavioral value by disclosing the selection rule (≤5 performance-index points lower for the same usage), which tells the agent how candidates are chosen rather than just that a lookup occurs.

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 with no filler. The constraint that defines the tool is stated immediately and nothing is repeated from the title or schema.

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 core selection semantics are explained, but for a 3-parameter tool with no output schema the description leaves the undocumented parameters and the shape of the result (e.g., a single model vs. a ranked list, and whether cost savings are returned) unstated.

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

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

Schema description coverage is 0%, so the description must compensate for all three parameters, and it largely does not. "For the same usage" hints that token usage matters but never explains that model is the input model or that input_tokens/output_tokens default to 1,000,000 and 250,000.

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?

States a specific verb (finds) and resource (a cheaper model) with a precise selection constraint (at most five performance-index points lower). This is clearly distinct in intent from siblings like find_models or get_model, though it does not name or explicitly contrast any sibling.

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?

"For a model you use today" implies the usage context (cost-reduction for an existing model), which is reasonable implied guidance. However, it never states when not to use it or names an alternative such as recommend_models or best_for_task, leaving routing to inference.

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?

Annotations already declare readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds genuine behavioral context beyond them: pricing is at 'base rates' (implying no discounts/committed-use tiers), the figure is monthly, and the result includes a word/page translation of the volume.

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 dense sentence with the core scope (cost per month, base rates) front-loaded and no filler. Slightly overloaded by bundling the words/pages output detail into the same clause.

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 no output schema, the description usefully sketches what comes back (translated volume), and annotations cover safety. However it omits how input vs output tokens are handled and the default token volumes, which an agent would need to call it confidently.

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

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

Schema coverage is 0%, so the description must compensate. It clarifies the models parameter ('up to five models', matching maxItems=5) and references a token volume, but does not distinguish input_tokens from output_tokens or explain that both are counted, leaving a real gap.

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?

States a specific verb and resource ('Monthly API cost... for up to five models and a token volume') plus the output scope (cost translated into words and pages). It is clearly a pricing estimator and distinguishable from find_models/get_model siblings, though it never names an alternative explicitly.

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 when-to-use guidance and no routing against siblings such as cheaper_alternative or best_for_task, which plausibly overlap with cost estimation. The intended context is only implied by the word 'Monthly'.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered and the description is not contradicted. The description adds the filterable/sortable surface, but discloses nothing about result size, the 25-item ceiling, or whether results are paginated.

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 with no filler; the verb and resource come first and the qualifiers follow. It is efficient, though a good portion of it restates enum values already visible in the schema.

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 no-required-parameter search tool with annotations covering safety this is close to adequate, but with no output schema the description should say something about what comes back and how many results (the schema's minimum 1 / maximum 25 limit is unexplained). The gap is noticeable but not crippling.

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 only 20% (only 'query' is documented), so the description must compensate, and it partially does by naming the facets — name, provider, licence, privacy — and the five sort keys. It maps well to license/privacy/sort but omits the 'limit' parameter entirely and only loosely implies that 'query' also matches ids.

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 specific verb pair and resource ('Searches and filters the catalog of language models') and enumerates the filterable facets and sort keys, so the tool's function is unmistakable. It never distinguishes itself from siblings like recommend_models or get_model, which do related model-selection work, so it stops short of 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 statement of when to reach for this tool versus recommend_models, best_for_task, or get_model, and no prerequisites or exclusions are given. The only hint is the verb 'searches and filters', which implies exploratory browsing but leaves the agent to infer the choice among six 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?

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safe-read profile is covered by structured data. The description adds the scoping constraint (single model, one usage volume) but says nothing about unknown/misspelled model handling, catalog size or data freshness. With annotations carrying the safety burden, a 3 is appropriate.

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 dense sentence with no filler, and the most decision-relevant facts (price, context window, cost) are front-loaded. It reads as a run-on inventory rather than structured prose, but nothing is wasted.

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 three parameters, no output schema, and only one-third of parameters documented, the description leaves concrete gaps: units/defaults for the token inputs and behaviour for an unresolvable model id. Annotations cover the safety profile, so the omission is not severe, but the definition is only minimally sufficient 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?

Schema description coverage is only 33%: only 'model' is documented in the schema, while input_tokens and output_tokens carry defaults but no descriptions. The phrase 'cost for a usage volume' hints that token volumes drive the cost figure, but the description never states that these are token counts, that defaults are 1M/250k, or what happens if they are omitted.

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 enumerates exactly what facts are returned (price, context window, indices with rank, licence, privacy, computed cost, alternatives) and scopes it to 'one language model', which implicitly contrasts with the plural search tools (find_models, recommend_models). It is clear, but it is framed as a return-value list rather than a verb+resource statement and never names a sibling to differentiate itself.

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 explicit when-to-use or when-not-to-use guidance, no prerequisites, and no routing to alternatives such as estimate_cost or cheaper_alternative. The mention of 'cheaper or open alternatives' inside the return list arguably overlaps with the sibling cheaper_alternative without clarifying the boundary.

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?

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so safety is covered. The description adds useful behavior context: results are ranked by a specific composite score (performance, cost, context, features) and constrained by privacy/licence needs. It does not say how many models come back or in what shape, which matters since there is no output 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?

A single dense sentence that front-loads the outcome and then the scoring basis and optional constraints. No filler, though the parenthetical list slightly compresses what could be clearer framing.

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 9-parameter tool with no output schema, the description covers the ranking method and budget/privacy framing but omits the result format (how many candidates, fields returned) and the semantics of the priority and vision toggles. Adequate but with real gaps for an agent invoking it blind.

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 only 33%, so the description must carry extra weight. It explains the use_case + monthly_budget_usd pairing, privacy, and indirectly licence (open_weights_only), but leaves priority, include_free, and needs_vision undefined in both schema and prose, so compensation is only partial.

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?

Specific verb ('Returns') plus resource ('language models') scoped by use case and monthly API budget, and it names the AgentKernel Fit Score as the ranking methodology. The purpose is clear, but it never distinguishes itself from the very similar sibling 'best_for_task' or from 'find_models' / 'cheaper_alternative', so an agent cannot easily tell which of these to pick without opening schemas.

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 a use case and monthly API budget') but gives no explicit when-to-use, when-not-to-use, or alternatives. With three similarly named siblings (best_for_task, find_models, cheaper_alternative) this is a meaningful routing gap.

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