Claude Registry Plugin Catalog

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, covering the safety profile. The description adds content-level details (install commands, component counts, security-audit result) but does not disclose additional behavioral traits such as error handling or rate limits, which is acceptable given the simple read-only nature.

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 description is a single, well-structured sentence that front-loads the action and resource, then packs in the relevant return details. Every phrase contributes value, with no redundancy or 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 simple retrieval tool with one parameter, an output schema, and read-only annotations, the description provides sufficient information about the tool's purpose and primary return contents. It does not need to explain return fields since an output schema exists, and it covers the key aspects of the 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?

The input schema fully documents the 'id' parameter with an example format, so the description adds no new parameter semantics beyond saying 'by its id'. Schema description coverage is 100%, so the baseline of 3 applies; no additional parameter guidance is provided.

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 uses a specific verb ('Get') and identifies the resource ('single ClaudeRegistry plugin by its id'), distinguishing it from siblings that list categories or search. It also enumerates what details are returned (install commands, component counts, security-audit result), making the purpose unmistakable.

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 implies usage for fetching a single plugin when you have its id, but does not explicitly contrast with alternatives like search_plugins or list_categories. No when-to-use or when-not-to-use guidance is given, leaving the context to be inferred.

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 indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds that it returns counts and a total, which is useful context beyond annotations but lacks deeper behavioral disclosure (e.g., data freshness).

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 description is a single, front-loaded sentence that conveys the essential information without any extraneous words.

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 has no parameters, a straightforward output (which is covered by an output schema), and no nesting, the description is complete. It explains what is returned (categories, counts, total).

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 has zero parameters, so the schema coverage is 100% by default. Per rubric, no parameters means baseline score of 4. The description adds no parameter details because there are none.

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 states the tool lists distinct plugin categories with counts and total count. It uses a specific verb ('List') and resource ('plugin categories'), and differentiates from siblings 'get_plugin' (single plugin) and 'search_plugins' (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?

The description implies the tool is for getting a category summary, but it does not explicitly state when to use it versus alternatives or provide any exclusions. The purpose is clear from the verb 'List', but explicit guidance is absent.

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 the tool as read-only and idempotent. The description adds valuable context beyond these: the seven-check security audit and the preference for verified plugins when recommending an install. This gives insight into the quality/verification aspects of results, exceeding 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, front-loaded with the core action, and no filler. Every clause contributes to understanding the tool's function, return values, and best practice. Highly efficient.

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 an output schema present, the description doesn't must list return fields, but it still mentions install command and verification status, plus the audit context. It lacks examples or handling of edge cases, but given the tool's simplicity and rich annotations/schema, it is sufficiently complete.

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% for both query and category parameters, so the schema already documents their meanings. The description reiterates that searching is by keyword and/or category but adds no extra semantic detail beyond what the schema provides. Baseline 3 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?

The description clearly states the tool searches the ClaudeRegistry marketplace by keyword and/or category, distinguishing it from sibling tools get_plugin and list_categories. It specifically mentions returning install command and verification status, which clarifies its unique role.

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 implies when to use the tool (when searching for plugins to recommend) and even advises preferring verified plugins, but it does not explicitly mention alternatives like get_plugin for specific lookups or list_categories for browsing categories. Usage context is clear but not contrasted with alternatives.

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?

The description discloses key behavioral traits beyond the annotations: verification runs entirely locally via a dependency-free Node script, the plugin code never leaves the user's computer, and the server performs no computation. It also clarifies that the tool returns a recipe (instructions) rather than performing the verification itself, which is crucial for setting correct expectations.

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 description is composed of three sentences, each serving a distinct purpose: stating what the tool does, explaining the local/private execution model, and giving explicit call instructions. It is front-loaded with the core purpose and avoids unnecessary detail despite the complexity.

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 complexity (seven checks, local script, pre-publish workflow), the description is complete: it covers the checks, the local execution, the fact that no data leaves the machine, the user's next steps (download, run, fix failures), and the optional parameter. An output schema exists, so return values need not be detailed. No gaps remain.

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% (only optional pluginPath is fully described in the schema). The description does not add significant new meaning beyond the schema, but it references the plugin directory and filling the run command, which is consistent. Baseline 3 applies since the schema already documents the parameter well.

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 states the tool's purpose: to provide a recipe for running the ClaudeRegistry verification methodology against a plugin. It uses a specific verb phrase ('Get the recipe to run') and specifies the resource (verification methodology) and the seven static checks, distinguishing it from sibling tools that list or search plugins.

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 gives explicit guidance on when to call the tool ('Call this when the user wants their plugin or skill checked') and notes it is for pre-publishing verification. It does not explicitly mention alternative tools or when-not scenarios, but the context is clear and differentiates from siblings without needing to name them.

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