AI Signals (Signals API)

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 says nothing about how many dates are returned, ordering, whether the list is exhaustive or paginated, or how stale it might be — meaningful gaps for a discovery tool whose whole value is the returned set.

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, front-loaded with what the tool returns and followed by the immediate use case. There is no filler or restatement of the tool 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?

For a zero-parameter, read-only listing tool with no output schema, the description adequately covers what is returned ('dates') and why. Adding ordering or completeness guarantees would round it out, but nothing essential for invocation 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 and the description introduces none, so there is nothing to mis-specify. Per the zero-parameter baseline, a 4 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?

States a specific verb ('List') and resource ('archived daily editions (dates)') with the added scope qualifier 'from the free digest'. The purpose is immediately graspable, though it never explicitly distinguishes itself from siblings like daily_signals or what_changed.

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 to find which dates can be cited' gives a concrete, actionable use case rather than leaving intent implicit. It stops short of naming alternatives or when-not-to-use conditions, so it is clear context but not full routing guidance.

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 and no output schema, the description carries the full behavioral burden, and it does disclose the fixed cardinality (exactly 4 items), guaranteed source diversity, and that each item links to the original. It says nothing about freshness/timezone for "today", auth requirements, caching, or what happens when a source has no signal, which are real gaps for a daily-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: the first front-loads what is returned and the second states the trigger. Every clause (source names, cardinality, original links) carries information an agent would otherwise have to guess.

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?

Because there is no output schema, the description must describe the return value, and it does so well (exactly 4 items, one per source, linked to originals). The only missing piece is how "today" is scoped temporally and how empty-source days are handled.

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; schema coverage is 100% and the empty object is self-explanatory. Baseline 4 applies for a no-parameter tool.

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 a concrete resource (today's AI signals) and goes further by specifying the exact return contract: four items, one per named source, each linking to the original. That is enough to distinguish it from siblings such as archive_days, what_changed, top_models, and top_repos without opening any schema.

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 the user asks what's new in AI today" gives a clear triggering condition. It stops short of naming alternatives or exclusions (e.g. when to prefer archive_days for older dates or what_changed for diffs), so the agent must infer the boundary from sibling names alone.

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 burden. It does disclose a meaningful data-quality trait: unranked entries are returned as 'not ranked' with a reason rather than a fabricated value, which tells the agent how to interpret nulls. It omits ordering criteria, default limit behavior, and read-only/mutation profile.

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, front-loaded with what is returned and second with the null-handling rule. No filler; the second sentence is somewhat compressed but still parses cleanly.

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 one-parameter read tool with no output schema, the description covers the returned fields and the unranked fallback, which is respectable. It still fails to define what makes a model 'top' (ranking basis and time window) or the pagination default, leaving a real gap given no output schema exists.

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% and the single 'limit' parameter (1-50) is undocumented in both schema and description. Nothing explains what the limit bounds, what the default is, or whether the response is truncated, so the description does not compensate for the coverage 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?

The description states a concrete resource ('Hugging Face models we track') and the payload it returns (rank, rank change, 7-day momentum), so the agent knows what it gets. It does not explicitly contrast itself with the closest sibling, top_repos, leaving the models-vs-repos distinction to inference from the name.

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, no mention of prerequisites, and no named alternative among top_repos, daily_signals, or what_changed. Usage is only implied by the noun 'models we track'.

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 supplied, so the description carries the burden. It does disclose the return payload (rank, rank change, momentum), which is genuine contract information, but says nothing about ordering, data freshness, how many items are returned by default, or that this is a read-only snapshot. Useful but incomplete for a tool with zero annotation coverage.

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 filler. The content of the result is front-loaded before the usage trigger, so the most decision-relevant information comes first.

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 fields, which covers most of what an agent needs to know about the response. The remaining gap is the limit parameter's default and the ordering of results, which are left 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?

There is exactly one parameter, 'limit', with 0% schema description coverage, so the description must compensate — yet it never mentions limit, its default, or the 1-50 bounds. An agent cannot tell from the description whether omitting limit means 'all' or 'top 10'.

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 concrete resource ('GitHub repositories we track') and the exact data returned ('rank, rank change and momentum'), which is far more specific than the bare tool name top_repos. It implicitly separates itself from top_models by saying repositories, but never names the sibling explicitly, so sibling differentiation is left to inference.

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 invocation trigger: 'Use when the user asks which open-source AI projects are trending.' That is a clear positive-use condition. However, it offers no when-not-to-use guidance and never routes the agent to top_models or what_changed when the question is about models or changes rather than trending repos.

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 supplied, so the description carries the behavioral burden, and it does disclose the shape of what comes back (new entries, disappeared entities, rank movers). It doesn't state that the tool is a side-effect-free read or clarify what 'previous snapshot' means as a comparison window.

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 front-loaded and content-bearing: the first defines the output delta, the second defines the trigger. There is no redundancy with the title or restatement of the 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 annotations, the description usefully names the return categories and the trigger condition, which is most of what an agent needs for a zero-argument query. The only soft spot is the undefined comparison window implied by 'previous snapshot'.

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 for a no-param tool is 4. No argument syntax or defaults are needed.

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 ('the AI landscape') and operation ('what changed since the previous snapshot'), then enumerates the three result categories: new entries, dropped entities, and rank movers. This is clearly distinguishable from siblings like top_models and top_repos, which are static rankings rather than deltas.

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 an explicit trigger — 'Use when the user asks what's moving, rising, or different lately' — which maps well onto vague user phrasings. It does not name alternatives or when not to use it (e.g., versus daily_signals for a single day's picture), so it stops short of full routing guidance.

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