Versyka

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

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so the safety profile is covered. The description adds real behavioral context beyond that: it performs live lookups against the npm registry and PyPI (implying network dependency and latency) and specifies what is returned — current spec, latest version, release-notes link, and a Versyka migration guide when one exists. It stops short of stating rate limits, failure behavior on an unparseable manifest, or format-detection fallback.

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 action and scope, then the return payload. Every clause carries information: accepted formats, what is flagged, the registries consulted, and the output fields. 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?

For a read-only, single-required-parameter tool with no output schema, the description covers both the input side (formats) and the output side (spec, latest version, release note, migration guide). Minor gaps remain around result size limits and behavior when the manifest format cannot be detected or parsed.

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 all three parameters (manifest, file_name, all) are already documented in the schema, including the meaning of 'all'. The description adds the enumeration of supported manifest types, which is minor value, so the baseline of 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 (check) and resource (a dependency manifest) and enumerates exactly which formats are understood: package.json, requirements.txt, pyproject.toml, Pipfile. It also names the target of the check (third-party API SDKs that are behind, deprecated or replaced) and the data source (live npm registry and PyPI), which cleanly separates it from the model-oriented siblings like find_retired_models and lookup_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?

Usage is implied by the scope — you call this when you have a manifest to audit — but the description never states when to prefer it over siblings such as get_sdk_versions or get_migration_guide, nor any prerequisite or exclusion. It is adequate but leaves routing 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?

Annotations already carry the safety profile (readOnlyHint, idempotentHint, non-destructive, openWorldHint), so the bar is lowered. The description still adds real behavior: matching is exact and covers Bedrock, Vertex AI, LiteLLM and OpenRouter spellings, plus what each result contains. It does not mention input limits (200 files, size caps) but that is minor here.

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 dense sentences with zero filler, front-loaded with what is scanned and what matching rules apply. Every clause 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?

No output schema exists, but the description compensates by enumerating the return fields (shutdown date, days left, recommended replacement, source page, file:line locations). Inputs, matching rules, and outputs are all covered; only scan limits and error/empty-result behavior are unmentioned.

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?

One parameter with 100% schema description coverage, so the schema already documents path and content. The description adds no syntax or format detail beyond the schema, which is the expected baseline 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?

States a specific verb-and-resource pair: scanning source and config files for retired or scheduled-for-retirement AI model IDs. It also names the vendor set and spelling variants, which distinguishes it from list_model_retirements (a catalog listing) and lookup_model (a single-model lookup).

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 constraint 'exact matches only' implies intent, but the description never says when to pick this tool over siblings like list_model_retirements or lookup_model. Usage is inferable from the scanning framing rather than stated.

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, non-destructive and openWorld, so safety is covered. The description adds genuinely useful context beyond them by disclosing the return format (Markdown) and the guide's internal structure, which matters because 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?

A single front-loaded sentence naming the resource and format immediately, with the content breakdown following. The comma-separated inventory is dense but every clause conveys a distinct return element.

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 burden of describing the return value and does so thoroughly. It omits error behavior for an invalid or unknown slug, which is the only notable gap for this simple read tool.

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 slug parameter is fully documented with length bounds, a format example, and its source tool. The description adds no further parameter detail, so the baseline of 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 ('One migration guide as Markdown') and enumerates the returned contents, so an agent can distinguish it from list_migration_guides. It stops short of explicitly naming that sibling or contrasting the two, which keeps it at 4 rather than 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?

The description never says when to reach for this tool versus list_migration_guides, nor does it mention prerequisites such as needing a valid slug from the listing tool. Usage is only implied by the singular 'One migration guide' phrasing.

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 (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), which is the heavy lifting. The description adds valuable return-content context – that deprecation status, a release-notes link, vendor, and an optional migration guide come back – but says nothing about caching, rate limits, or behavior when a package is unknown.

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 with no padding. It is a field list rather than a sentence about the action, but every clause conveys a real return dimension.

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 burden of describing the return shape, and it does name the key fields an agent would want. It doesn't specify version format or error behavior for unknown packages, but it is sufficient for this simple two-parameter lookup.

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%, with both parameters documented and examples given for 'package' and an enum for 'ecosystem'. The description adds no syntax or format detail beyond the schema, so the 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 the specific resource (latest version of an npm/PyPI package) and enumerates the accompanying fields: deprecation flag, release-notes link, API vendor, and migration guide. Clear enough to select, but it does not contrast itself with siblings like get_migration_guide or list_migration_guides, so an agent must infer 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?

There is no explicit when-to-use statement, no prerequisites, and no named alternative. An agent can only infer that this is the lookup for version/deprecation status rather than migration guides or dependency checks.

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 the safety profile is covered. The description adds the concrete provider catalog, which is genuine context, but says nothing about result counts, ordering, or pagination 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?

One sentence, front-loaded with the resource, with the filter condition trailing. The provider list is long but serves as useful domain signal rather than 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?

For a one-parameter, read-only listing tool whose annotations already carry the safety profile, this is largely sufficient. The only real omission is any hint of the return shape or volume, which matters slightly since 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 100% and the single parameter already documents its purpose and gives an example ('openai'). The description only restates that filtering by package name is optional, adding no syntax or format detail 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?

Names a specific verb+resource (list migration guides) and enumerates the ten supported SDK providers, which lets an agent judge coverage without opening anything. It does not explicitly distinguish itself from the singular sibling get_migration_guide, though the plural/enumerating framing implies it.

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?

"Optionally filtered by package name" implies when the parameter is worth using, but there is no guidance on when to call this versus get_migration_guide or check_dependencies. Usage is only weakly implied.

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, idempotentHint, destructiveHint=false and openWorldHint=true, so the safety and external-source profile is covered. The description adds the sort order and the shape of returned records but says nothing about pagination, rate limits, or how 'sources' are obtained. Incremental value over annotations is modest.

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 no filler, and the most important content (what the tool returns and its ordering) is front-loaded. Slightly dense but 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?

There is no output schema, so the description carries the burden of describing returns, and it does so adequately by naming the record fields and their ordering. Combined with the annotation-covered safety profile, an agent has enough to call it, with only pagination and vendor enum semantics left undocumented.

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 67% and the description echoes 'vendor' and the days-ahead concept, mapping loosely onto two of three parameters. It adds no format or syntax detail and entirely omits include_retired, so it neither compensates for the uncovered vendor parameter nor extends what the schema already states.

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 ('AI model retirement calendar') and enumerates the returned fields (model IDs, shutdown dates, replacements, sources) with a defined ordering ('soonest first'). It is clear what the tool does, though it never explicitly distinguishes itself from the sibling find_retired_models, so sibling differentiation is left implicit.

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: 'Filter by vendor or by how many days ahead to look' tells the agent the two filtering modes but not when to reach for this tool over list_migration_guides or find_retired_models. No exclusions or alternatives are named, so routing between the six siblings 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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description goes further by disclosing accepted input families (plain IDs and gateway spellings such as openai/gpt-4-0613 or claude-3-opus@20240229) and the shape of the answer, including the negative case ('no retirement is listed').

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 question the tool answers, then inputs, then return contents. No filler and every clause is informative.

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-value burden and does so: shutdown date, status, replacement, source, or an explicit 'none listed'. Combined with the input-format guidance, 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% and the single parameter is already documented, so baseline is 3. The description adds value the schema lacks: concrete examples of the multiple naming conventions the parameter accepts, which is the main practical ambiguity for this 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?

States a specific verb and resource ('look up a model's retirement') and frames the exact question it answers (retired or scheduled for retirement). This clearly separates it from siblings like list_model_retirements and find_retired_models, which cover sets of models rather than one ID.

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 question form implies usage for a single model ID, and the mention of accepted ID spellings hints at when it applies. However, it never names an alternative or states when not to use it (e.g. prefer list_model_retirements for bulk listing), 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.