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

Despite annotations already covering the safe-read profile (readOnly, idempotent, openWorld, non-destructive), the description adds substantial context: freshness (~4 min checks), quota/pricing model, an alias, and the shape of the returned verdict fields. This goes well beyond the structured data.

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 purpose and usage in the first two sentences; the provider list, return fields, pricing and alias are dense but each earns its place. Slightly crammed but no filler.

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 naming the key return fields (verdict, recommended_action, open incidents, affected components, checked_at, freshness). For a single-param read tool, an agent has everything needed to call and interpret it.

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 there is only one parameter, so the baseline is 3. The description adds value by enumerating ~40 supported providers and giving a concrete call example, which helps the agent pick a valid provider id rather than guessing.

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 (check), resource (API/provider status) and scope (up, degraded or down right now), and names the covered providers (~40). It is clearly distinguishable from siblings like list_providers or check_model_deprecation.

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?

Explicitly says when to call it ('BEFORE calling a provider's API, or right after you get 5xx/429/timeout errors') and the decision it supports (retry, wait or switch to a fallback). Nothing about timing 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.

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

Adds substantial context beyond the readOnly/idempotent/openWorld annotations: cost behavior ('Free within the daily quota, then $0.003 via x402'), the provenance of the data (provider's own deprecation page), and the response ordering with freshness. For a read-only lookup with no output schema, this is rich behavioral disclosure.

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 purpose, then usage triggers, then return fields and pricing — a logical order with little waste. The return-field enumeration and alias/cost sentences make it dense but each carries information the agent needs.

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 compensates by enumerating the return fields (summary, verdict, recommended_action enum values, replacement, shutdown_date, evidence, freshness). Combined with annotations, triggers, cost and alias coverage, an agent has everything needed 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%; the schema already documents both 'model' and its deprecated alias 'id' with examples. The description reinforces this with an inline example and the alias note, but adds little that the schema does not already say, 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 and resource — check an LLM model id's lifecycle status — plus the concrete outputs (shutdown date, replacement) and the authority it reads from (the provider's own deprecation page). An agent can immediately tell this apart from siblings like check_api_status or list_recent_api_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?

Gives explicit trigger conditions: 'Use before hard-coding or calling a model, or when a model call fails with "model not found / deprecated".' That is clear context for invocation. It stops short of naming when not to use it or which sibling to prefer in adjacent cases, so it is not 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?

Annotations already carry the full safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), so the bar is lower. The description usefully adds the content scope of the digest (incidents, deprecations, pricing changes, MCP breaking changes) and that it is free, but says nothing about freshness semantics, size, or whether history is bounded.

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 digest contains, then when to use it, then a call example. No filler, and nothing important 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-required-parameter, read-only tool with annotations covering the safety profile, the description covers what the agent needs to decide and call it. There is no output schema, so a brief note on return shape (e.g. entries grouped by date) would have closed the last gap.

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% — the single date parameter is already documented as 'YYYY-MM-DD; omit for the latest issue'. The description's example call ('{} for the latest, or {"date":"2026-10-14"}') restates the same default behavior with concrete syntax, adding a small amount of value 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 gives a specific verb+resource ('Daily digest of AI/dev API incidents, model deprecations/retirements, pricing-page changes and breaking MCP server changes') and states the 'free' qualifier. It is clearly broader in scope than the sibling tools check_api_status or check_model_deprecation, but it never names them, so the differentiation is inferred 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 a once-a-day catch-up instead of polling' tells the agent the intended cadence and implicitly rules out the polling-style siblings. It gives a clear context for use but stops short of naming an alternative tool or stating when this aggregation is the wrong choice.

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 readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds behavior the annotations do not: the approximate result size (~40 providers), the possible state values (operational/degraded/outage), the checked_at field, and that the call is free (no auth/cost concern).

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, followed by routing guidance. No filler or restatement 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?

With no output schema and no parameters, the description carries the return-shape burden and does so: it names the fields (state values, checked_at) and result scale. Combined with the sibling routing hint, an agent has everything needed 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?

The tool takes zero parameters, so the baseline is 4. There is nothing for the description to disambiguate, and it correctly implies no filtering inputs are accepted.

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?

Specific verb ('List') plus resource ('~40 AI and developer API providers Upstream tracks') and the returned fields (state, checked_at). It explicitly names the sibling check_api_status, so an agent can distinguish this discovery tool from the status-checking tool without opening either 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?

Gives two concrete use cases: finding a provider id for check_api_status and getting an all-providers health overview. It names the alternative tool and the chaining condition, but does not state any when-not conditions (e.g., when to prefer list_recent_api_changes or get_digest instead).

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 cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds real context beyond them: return shape (summary first, then change list with timestamps and source links), a quota/cost model, and an alias (get_changes). That is substantive behavioral disclosure, not repetition.

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-loads what the tool lists, then usage, return shape, example, cost, and alias — a logical order with little waste. Slightly dense and the trailing alias/pricing lines are tacked on, but each sentence carries information.

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 carries the return burden and does so (summary plus timestamped, source-linked change list). All four optional parameters are schema-documented, the use cases and cost are stated, and there are no required params an agent could miss.

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 the schema already documents kind, limit, since, and provider including defaults and the enum. The description adds only an example object ({"provider":"anthropic","kind":"model_retirement"}) that reads more like a response item than a filter syntax, so it neither compensates nor extends meaningfully. 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 (list recent breaking/notable changes across AI and developer APIs) and enumerates the covered categories: outages/recoveries, deprecation-page edits, model retirements, pricing-page edits. That enumeration implicitly distinguishes it from the single-purpose siblings (check_api_status, check_model_deprecation) — this is the aggregate change feed.

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 concrete usage triggers: answer 'did anything change that could break my integration?' or explain sudden errors. It also flags the cost model (free within daily quota, then $0.01 via x402). It does not, however, name an alternative or state when a narrower sibling like check_api_status is the better pick.

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