Local AI Picks

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds that results are aligned side by side across models and capped at 4, but says nothing about ordering, what happens if a slug is invalid, or how the comparison is presented.

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 short sentence with the most useful information (equivalence to get_model, 2-4 models, side-by-side output) front-loaded. It is efficient, though slightly under-specified rather than genuinely tight.

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 one-parameter read-only tool with annotations, this is close to adequate. The gap is that with no output schema, the description defers the return shape entirely to 'the same facts as get_model' instead of naming what is compared.

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%, but the single parameter (slugs) is self-explanatory and the schema already encodes minItems 2 / maxItems 4. The description merely restates the '2 to 4' bound without explaining slug format or how to obtain them.

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?

Names a specific operation (side-by-side comparison) and anchors it to the sibling get_model, making the single-vs-multi distinction clear. The vague part is 'the same facts' — it never says what facts are compared, so the resource content is only defined by reference.

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 '2 to 4 models' clause implies when to reach for this over get_model, but there is no explicit 'use this instead of calling get_model N times' statement and no guidance on what to do outside that range. Usage is inferable rather than stated.

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?

Beyond the readOnly/non-open-world annotations, it discloses the ordering ('same order as the site'), the version-selection behavior ('latest version of each family'), how the memory filter is estimated and when it becomes "unknown", and the absence of ratings and speed estimates. That is meaningful behavioral context, although it doesn't describe pagination or the exact result shape.

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 definition is compact and front-loaded: the core scope comes first, followed by filtering semantics. It is dense but each set of clauses carries information; only the ordering detail is slightly buried.

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 does enough by explaining ordering, version selection, memory estimation behavior, and what the results deliberately omit. It could be slightly stronger by stating the general result shape, but an agent can call it correctly from this.

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 description adds real meaning beyond the 75%-covered schema: memory_gb filtering is explained as keeping models whose smallest listed weight option fits, with estimation rules and an "unknown" fallback. It also reinforces the device dependency (apple counting MLX weights), though task ordering is only implicitly referenced.

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: finding current open-weight models for one task, deduplicated to the latest version of each family. That scope is clear and meaningfully different from compare_models or get_model, though the sibling tools are not 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?

It does give a concrete usage condition for filtering (supply memory_gb and device to keep only models whose smallest listed weight option fits). However, it never says when to prefer compare_models, get_model, or list_tasks instead, so routing still requires inference from the names.

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 and openWorldHint=false, so safety and locality are covered. The description adds useful context that third-party scores carry their source and date and that the site's own testing status is reported, but says nothing about error behavior for a bad slug or about data freshness beyond the score dates.

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 opened by the scope ("One model:") followed by a compact field list; every clause adds information. The list format is slightly dense but no sentence 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 no output schema, the description usefully characterizes what a caller gets back — parameters, license, context length, weight options with size and memory, scored benchmarks with provenance, and links. That covers the main gaps; only failure modes and staleness handling are omitted.

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 single slug parameter already documents its origin (find_models) and max length. The description adds no further meaning about the slug that the schema does not already provide, so the baseline 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?

"One model" plus an enumeration of returned fields makes it clear this is a single-record detail lookup, which implicitly contrasts with the list-oriented find_models sibling. It lacks an explicit verb, but the resource and scope are 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 never says when to use this versus find_models or compare_models; the only routing signal ("slug from find_models") lives in the schema, not the description. An agent gets no explicit guidance about prerequisites 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine value by disclosing what the response contains (task names, current model counts, and a page reference per task) even though no output schema exists. It stops short of describing ordering or whether counts are live.

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 sentence that front-loads the resource and enumerates the categories compactly. It is slightly awkward grammatically ('The tasks Local AI Picks lists open-weight models for'), which costs it a point but not readability.

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 parameters and no output schema, the description carries the burden of explaining return content, and it does so (categories, counts, page links). Missing only secondary detail such as ordering or whether the category set is fixed, which is minor for a zero-arg discovery 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 takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate and it correctly does not invent parameter detail.

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 the resource clearly: the task categories (text LLM, coding, speech to text, etc.) that open-weight models are organized under, along with counts and pages. The phrasing is a noun phrase rather than a verb-led statement, and it never names the sibling tools, but the resource (tasks vs. models) is distinct enough from compare_models/find_models/get_model that an agent can place it.

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 explicit when-to-use or when-not statement, and no sibling is named as an alternative. However, the note that each task carries a model count and a page implies this is the browse/discovery entry point before drilling in with find_models or get_model, so usage is reasonably inferable.

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