API Change Feed

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

No annotations exist, so the description carries the full burden; it does disclose a per-call cost of $0.02 via x402/USDC, which is genuinely important pre-invocation context. However, it omits whether a wallet/payment setup is required, what happens on payment failure, and gives only a hint ('grouped by source') about the shape of the result.

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, front-loaded with the content scope and followed by the price. No filler, no restating of the name, and the cost constraint is where an agent will look for it.

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 annotations, no output schema, and an undocumented 'kinds' parameter, the description should explain the accepted kinds values and the result grouping at least minimally. It covers the content scope and the price well, but leaves meaningful gaps for an agent to call it correctly on the first try.

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 50%: 'since' is documented in the schema, but 'kinds' is a free-form string with no description and no enum. The description's mention of 'breaking, deprecated or security-related' may hint at valid kinds values, but it never states that these are the accepted values for 'kinds', so the compensation is ambiguous at best.

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 and scope: breaking/deprecated/security items across all tracked sources since a date, grouped by source. That is far more specific than the bare name 'digest'. It stops short of differentiating itself from siblings like get_changes or preview_changes, so an agent must infer the relationship.

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 'across all tracked sources' plus 'grouped by source' implies an aggregate-summary use case versus a raw change feed, but no explicit when-to-use or when-not-to-use rule is given. With four overlapping siblings (get_changes, preview_changes, upgrade_check), the description should have routed the agent explicitly.

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, and it does disclose the most important trait: the call is paid ($0.005 via x402/USDC), which an agent must know before invoking. It does not state that the operation is read-only, how pagination/ordering works, whether the fee is per call or per record set, or what happens if payment is not settled.

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 resource and its categories come first, then the cost constraint. Nothing is repeated and nothing is 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?

For a no-annotation, no-output-schema, four-optional-parameter read tool, the description covers the resource, the filter dimensions, the scoping, and the price, which is the critical decision input. Return shape details are legitimately omitted since no output schema exists but the record kinds are enumerated, though ordering and volume are 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 coverage is 75%, so most parameters are already documented in the schema, and the description adds only the framing that results are filterable by date and kind and scoped to one source or all. The limit parameter and its 1-100 cap are not mentioned, and no syntax or default values are added 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?

"Normalized change records (breaking, deprecation, security, release) for one tracked API/SDK or all of them" gives a specific resource and enumerates the record categories, so the agent knows exactly what comes back. It does not distinguish itself from siblings like preview_changes or digest, which sound like near-overlapping change-retrieval tools.

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 relative to preview_changes, digest, or upgrade_check, which are the obvious alternatives for change data. The only routing hint is the implicit "omit source for all", which is parameter-level rather than use-case-level.

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 exist, so the description carries the full disclosure burden. It usefully states the list size (60) and that the call is 'Free', implying no quota or billing cost, and the nature of a list implies a read-only operation. It does not say whether the list is static or can change, nor anything about ordering or response format beyond 'package names'.

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 short sentence, front-loaded with the action and payload, and every clause earns its place: count, contents, and cost. There is 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 zero-parameter, no-output-schema tool, the description covers what the call returns (tracked APIs/SDKs and their package names) and its cost profile. Only minor gaps remain, such as stability of the list and ordering, which an agent can discover from the response.

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, which is the baseline-4 case; there is nothing for the description to disambiguate. The description correctly signals that no filtering or input is expected.

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 concrete verb ('List') and a concrete resource ('the 60 APIs/SDKs we track'), plus what each entry contains ('package names to query them by'). It is clearly distinguishable from siblings like get_changes or quick_check, which imply actual change analysis rather than a static inventory, though the description 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?

Usage is only implied: the phrase 'package names to query them by' hints that this is a discovery step preceding the query/digest tools, but there is no explicit 'use this first' or 'use this instead of X' statement. An agent can infer the role, but must do the inference itself.

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 must carry the full behavioral burden. It usefully discloses the five-item limit, the category scope, coverage across all sources, and that the call is free, but it does not describe authentication, rate limits, or the shape of the returned headlines.

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 a single efficient sentence with the scope and categories front-loaded. 'Free.' is terse but earns its place by clarifying cost, though the fragmentary style slightly reduces clarity.

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, no-output-schema tool, the description supplies enough context to call it correctly: what is returned, how many items, which categories, and the freshness-check use case. Only deeper return details are absent, which is acceptable here.

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. The description correctly implies that no filtering inputs are needed and that the call is a simple freshness check across all sources.

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 precisely: the latest five breaking/deprecation/security headlines across all sources. That is more specific than the sibling name get_changes implies, but it does not explicitly explain how it differs from digest or get_changes.

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 'to check freshness' gives a clear intended use case, and 'Free' signals that cost is not a concern. However, it does not state when to prefer get_changes or digest instead, nor does it give exclusions.

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?

Since there are no annotations, the description carries the full behavioral burden. It is highly transparent by detailing exactly what the agent can expect to find in the output, such as 'top three breaking headlines' and 'verdict.' This information greatly aids the agent's expectation setting.

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 efficiently written, dedicating each clause to a specific piece of crucial information. It avoids fluff and immediately communicates the core value proposition and its limitations.

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?

Given the tool's function as a summary check, it is extremely complete. It covers the purpose, the precise metrics returned, and the pathway to the deeper, paid analysis, leaving no ambiguity for an agent wishing to use 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?

Although the schema coverage is 100%, the description adds significant semantic value by framing the context—that this tool is designed for libraries potentially known only from 'older training data.' It also clarifies that 'package' should be an npm/PyPI or source id name.

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 clearly defines the tool's purpose as checking if a library has changed since a specific version. It provides concrete output types (verdict, breaking changes, security fixes), effectively distinguishing it from other sibling tools like upgrade_check.

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 specifies the ideal use case is checking libraries known only from older training data. Furthermore, it explicitly directs the user to the alternative, paid tool (upgrade_check) when full evidence is required, defining a clear 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?

With no annotations supplied, the description carries the full disclosure burden, and it does surface two genuinely useful behaviors: the response includes the source sentence behind each label, and the call costs $0.01 via x402/USDC. The payment requirement is the kind of detail an agent cannot get from structured fields and is well worth stating. It stops short of describing failure modes, what happens for unknown packages or versions, or whether results are cached, which matters for a paid call.

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 payoff, then the when-to-use trigger, then the cost. Every sentence carries distinct information with no filler. Slightly dense in the first sentence, 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?

For a two-parameter read tool with no output schema and no annotations, the description covers what it returns, when to call it, and its pricing. The main omission is any routing guidance relative to the four sibling tools, several of which appear to overlap in scope.

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 'package' and 'from' are already documented with examples in the schema. The description reinforces 'the version your code targets' for 'from' but adds no format, range, or validation detail beyond what is structured. Baseline 3 applies when the schema does the heavy lifting.

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 specific resource and scope: 'What changed in a package since the version your code targets,' and enumerates the categories returned (breaking changes, deprecations, security fixes, major versions crossed) plus the evidence format ('the sentence that triggered each label'). That is far more than a restatement of the name. It does not, however, distinguish itself from the overlapping sibling 'get_changes' or 'preview_changes', so an agent must guess at the boundary.

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?

'Call this before writing code against a library you may know only from older training data' gives a concrete triggering condition, which is strong positive guidance. There are no exclusions and no reference to the sibling tools, so the agent gets no signal about when a cheaper alternative like preview_changes or digest would be preferred.

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