The Neural Net

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

With no annotations, the description carries the full burden. It does disclose the return content, including the unusual provenance detail of per-field source endpoints, which is real added value. However, it says nothing about behavior when the id is unknown, record size, or auth requirements, leaving meaningful gaps for an unannotated 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 short sentences with zero filler; the content statement comes first and the usage trigger second. Every clause earns its place and nothing is repeated from 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?

With no output schema and no annotations, the description must explain the payload, and it does so by enumerating fields and provenance. For a one-parameter read tool this is nearly complete; only error/missing-record behavior is unaddressed.

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 id parameter is documented in the schema with a concrete example. The description adds no syntax, format, or lookup semantics beyond that, so the baseline 3 for schema-covered parameters 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 (Get) and resource (one model's full record) and enumerates what the record contains: every field plus the endpoint each field was sourced from. It implicitly contrasts with search_models via 'full record' and 'complete detail', but never names the sibling, 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?

'Use when you need provenance or complete detail' gives a concrete selection trigger and implicitly routes the agent away from summary/search tools. It offers no explicit when-not condition and never names the alternative (search_models), so it is clear context without exclusions rather than 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?

No annotations are provided, so the description carries the full burden. It does disclose meaningful behavioral context absent from structured fields: the one-day recency window and the grouped-by-source, link-preserving return shape. It stops short of stating permissions, rate limits, result caps, or pagination, which matters for a harvest-style feed 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, no filler, and the substantive content description leads ahead of the usage hint. Every clause earns its place.

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 zero-parameter read tool with no output schema, the description adequately covers what comes back, how it's grouped, and the time scope. It would be fully complete with a note on result volume or whether the window is adjustable, but nothing critical 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The implicit fixed 'last day' window is the only parameter-like constraint and it is stated.

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 unpacks the opaque name 'get_wire' into something concrete: items harvested in the last day that weren't written up, grouped by source, linked to originals. An agent can form a clear picture of the payload. It doesn't explicitly distinguish itself from the nearby 'search_news' sibling, though the 'not written up' framing implicitly separates it from editorial content.

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 for a broad view of current activity' gives one positive use case, so usage is implied rather than spelled out. There is no when-not-to-use guidance and no mention of alternatives such as search_news for targeted queries, which an agent would need to route correctly among five 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?

No annotations are provided, so the description carries the full disclosure burden. It implies a read-only, non-destructive listing and defines the semantics of the result set (models newly entered the register), but says nothing about ordering, pagination behavior, or what a record contains.

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 short sentences: the definition of the result set comes first, the use cases second. No filler, no repetition of schema content, and it stays front-loaded.

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 two-optional-parameter listing tool with no output schema, the description plus schema is enough to invoke it correctly. The remaining gap is return-format/ordering, which the description could reasonably have mentioned given there is no output schema and 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 description coverage is 100%, so both parameters (days, limit) and their defaults are already documented in the schema. The description's phrase 'within the last N days' merely echoes the days parameter and adds no format, bounds, or interaction detail (e.g., how limit interacts with days). 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 resource (models) and a precise selection criterion (first recorded in the register within the last N days), which is more than a restatement of the name. It does not explicitly name or contrast with siblings like search_models, so an agent must infer that this is a recency-scoped listing rather than a keyword search.

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?

Provides concrete triggering questions ('what shipped recently', 'what is new in open-weight models'), which tells the agent the intended query shape. It stops short of stating when NOT to use it or naming search_models as the alternative for filtered/keyword lookups.

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 behavioral burden. It implies a passive informational read by describing only summary content, but never explicitly states that it is read-only or side-effect-free, and says nothing about auth or rate limits. The implied safety profile is inferable from the content described, which is why this is not lower.

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, both earning their place: the first enumerates the returned content, the second front-loads the use case. No redundancy with the title or name.

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 and no parameters, the description is the sole source of return-value information, and it delivers by naming exactly what the summary contains (model counts, field coverage, organisations, inclusion rule). It omits nothing critical for a simple stats call, though a note on read-only behavior would round it out.

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 there is nothing for the description to disambiguate; the baseline of 4 applies. The description correctly does not waste words on argument syntax.

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 resource and scope: a coverage summary with counts of models, field coverage, organisations, and the inclusion rule. It is clearly distinguishable from the sibling tools (get_model, search_models, search_news, etc.), which are retrieval tools rather than a register meta-summary. No explicit sibling naming, but the purpose is 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?

It gives a clear usage context: use this to judge whether the register can answer a question before querying it. That is a real when-to-use signal, not filler. It does not name alternatives or exclusions, so it falls short of a full 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?

No annotations are provided, so the description carries the full burden. It does disclose the record shape (parameter counts, architectural context length, licence, release date) and thereby implies a read-only, list-returning operation, but it says nothing about result ordering, pagination behaviour beyond the schema's limit field, or whether filters combine with AND. Solid on content coverage, thin on behaviour.

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, zero waste. The subject matter is front-loaded and the usage clause follows immediately, so an agent gets the what and the when in one pass.

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 13-parameter tool with no output schema and no annotations, the description covers purpose well but leaves real gaps: nothing about return shape, ordering, or how the many boolean filters interact. The 92% schema coverage absorbs most parameter risk, keeping this at minimum-viable rather than poor.

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 92%, so the schema already documents nearly every parameter, and the baseline is 3. The description adds a little by tying the covered data (size, context, licence, dates) to the generation of questions the tool answers, but it does not explain filter mechanics such as AND-combination or what 'task' values are valid.

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 ('Search') and resource ('the model register'), then enumerates the covered fields: parameter counts, context length, licence, release date. An agent can tell it apart from get_model (single-record lookup) and register_stats (aggregates), but the description never names those siblings and its 'what shipped recently' phrasing overlaps with recent_releases, 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?

'Use this to answer questions about which models exist, how big they are, what they are licensed under, or what shipped recently' gives concrete intake questions an agent can pattern-match against. There is no explicit when-not guidance, and no routing to recent_releases or get_model for the overlapping cases.

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 the full behavioral burden. It usefully discloses that results are narrative stories drawn from and linking to primary sources, which sets expectations about return content, but says nothing about read-only status, result counts, pagination, or ranking/recency 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 short sentences, front-loaded with what the tool does, followed by content provenance and usage routing. No filler or repetition.

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 three-parameter, no-annotation, no-output-schema search tool, the description covers purpose and use case but omits result shape details (fields returned, ordering, pagination) that 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 description coverage is 100%, so query (substring match), topic, and limit are already documented in the schema. The description adds no syntax, format, or default guidance beyond that, so 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 (Search) and resource (stories published by The Neural Net), and hints at differentiation from sibling fact-lookup tools with 'rather than raw model facts.' It does not name the alternatives (get_model, search_models) directly, so the distinction is implied rather than explicit.

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 for narrative context on a release rather than raw model facts' gives a clear selection condition and an implicit counter-case that routes fact queries elsewhere. There are no explicit exclusions or named sibling alternatives, but the when-to-use is clear.

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