Model Tested

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 real value beyond them by disclosing what is returned (test scores, rank, memory needs) and the cardinality bound of 2-6 models, 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?

Two tight sentences with zero waste: usage trigger first, return contents second. Nothing redundant or padded.

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 no output schema, the description covers when to use it and what comes back, which is enough to invoke it correctly. Only the omission of sibling routing (vs. get_model/find_models) keeps it from fully 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 description coverage is 100% and the single param is simply an array of model names/slugs. The description's '2 to 6 models' merely restates the schema's minItems/maxItems, adding no new format or naming guidance beyond the schema. Baseline 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?

States a specific verb and resource (compare open-weight models) plus the exact output content: test scores, rank, memory needs. The 'two or more' framing implicitly separates it from single-model siblings like get_model, but no sibling is named explicitly, so it falls 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?

Gives a clear trigger condition ('whenever the user asks how two or more open-weight models compare, or which of them to pick'), which also rules out single-model lookups. It never names the alternative tools (get_model, find_models) or says when to prefer them over this one.

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 which metrics are returned (accuracy, calibration, cost) and what model classes are compared, but says nothing about pagination, result ordering, or refresh 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?

Three sentences, front-loaded with the usage trigger, then the metric list, then the coverage scope. Efficient overall, though the parenthetical task examples are a touch dense.

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 enumerates the returned metrics (accuracy, calibration, cost per 1,000 calls), which is the key missing structured data. For a two-param read-only ranking tool, this is nearly sufficient, with only pagination/ranking detail absent.

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 enum values are documented in the schema, so the description carries no additional burden. Its mention of 'dedicated decision models and regular LLMs' loosely mirrors the type enum but adds no syntax or format detail 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 names the resource (decision models and LLMs) and the specific data returned (accuracy on a decision test, calibration, API cost per 1,000 calls), so an agent knows exactly what this tool yields. It does not distinguish itself from siblings like compare_models or find_models, which could otherwise look similar.

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 this whenever the user asks about decision models or LLMs for classification, routing, triage or yes/no calls' — and enumerates concrete example tasks. However, it names no alternative tool or exclusion condition to route between this and its 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?

Annotations only cover safety (readOnlyHint, openWorldHint). The description goes well beyond that, disclosing that results are ranked by the provider's own test scores, returned best-first, that supplying hardware+memory switches to a fit-only filter, and that each result carries the quantization to download and its memory requirement. This is material behavioral context an agent cannot infer from annotations.

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 usage trigger, then return behavior, then three concrete examples that map user utterances to arguments. No sentence is redundant and the example list earns its length by disambiguating intent.

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 exists, and the description compensates by describing what comes back (ranking order, fitted models, quantization, memory needed). For a read-only, parameter-light lookup tool this covers everything an agent needs to call it 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 coverage is 100%, so baseline is 3, but the description adds real meaning: it explains the hardware/memory_gb coupling ('with a GPU or Mac memory size it only returns models that fit') and maps natural-language intents to the sort_by dimensions and max_params_b via its examples. It does not clarify the limit cap of 30.

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 ('find the best models') plus the exact domain (local/open-weight LLMs, coding/documents/decisions, VRAM-fittable). It separates itself from get_model and compare_models by framing itself as a ranked recommendation lookup, though it never names a sibling 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?

'Use this whenever the user asks which local or open-weight LLM is best...' gives an explicit trigger condition with several qualifying scenarios and sample queries. It lacks explicit exclusions or pointers to compare_models/decision_models when the user wants a head-to-head rather than a single ranking.

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 establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral value by enumerating what the response contains (test scores, rank, memory per quantization, smallest GPU/Mac, decision accuracy, Q4 Mac run), which matters because no output schema exists.

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?

It is front-loaded with the usage condition, then moves to return contents and argument examples in two dense sentences. Every clause carries information, though the return-value enumeration is long and slightly list-like.

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 fully compensates by describing the return payload, and it clarifies the single input format. For a one-parameter, read-only lookup tool, nothing an agent needs to call it correctly is missing.

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% and the single parameter is already documented, so baseline would be 3. The description adds format guidance beyond the schema — 'Accepts a name or slug' plus concrete examples like 'qwen3.5 9b' and 'gpt-oss-20b' — which helps an agent format the argument.

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+resource with explicit scope: 'a specific open-weight model' and enumerates the question types it answers (quality, benchmark scores, VRAM/memory, quantization choice). It implicitly contrasts with siblings like find_models (search) and compare_models (multi-model) by scoping to a single model.

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?

'Use this whenever the user asks about a specific open-weight model' gives clear triggering context and lists concrete user intents. It never names the alternatives (find_models, compare_models) or states when not to use it, which keeps it 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely new behavior: result ordering ('newest first') and the returned fields ('title, summary, date and link'), which the annotations and schema do not convey. Pagination/result-count behavior aside, this is solid added context.

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: verb+resource+ordering first, then content examples, then return shape. No filler or repetition of the title.

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?

There is no output schema, but the description compensates by naming the returned fields, so an agent knows what to expect. For a zero-required-parameter read tool with full schema coverage, only minor gaps (e.g. whether results are capped or how ranking pages differ from articles) remain.

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 both parameters are already documented in the schema, including the limit default and query examples. The description reinforces keyword semantics ('by keyword') but adds no syntax or format detail beyond what the schema supplies, making the baseline 3 correct.

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 (find/search) and resource (articles and ranking pages) plus the ordering ('newest first') and content examples ('quantization, Ollama and memory'). This cleanly separates it from the model-oriented siblings (find_models, get_model, compare_models) which operate on models, not articles.

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 content domain and keyword examples imply when to reach for it, and the sibling set (all model lookup/comparison tools) makes the contrast inferable. However, there is no explicit when-to-use or when-not-to-use statement naming an alternative, so routing is left to inference.

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