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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information (a read operation) and specifies the return format ('Dictionary containing the latest version number and published date'), which is helpful. However, it doesn't mention potential errors (e.g., if the module doesn't exist), rate limits, authentication needs, or whether the data is cached/fresh.

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 well-structured and front-loaded with the core purpose, followed by organized sections for arguments and returns. Every sentence earns its place by providing essential information without redundancy. The two-sentence format with bullet-like sections is 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?

Given the tool's moderate complexity (3 required parameters), no annotations, and the presence of an output schema (implied by the 'Returns' section), the description is reasonably complete. It covers purpose, parameters, and return format. However, for a tool with no annotations, it could benefit from more behavioral context like error handling or usage constraints.

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 has 0% description coverage, so the description must compensate. It provides clear semantic meaning for all three parameters (namespace, name, provider) in the 'Args' section, explaining what each represents in the context of Terraform modules. This adds significant value beyond the bare schema, though it doesn't specify format constraints or examples.

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 specific action ('Get the latest version') and resource ('a Terraform module'), distinguishing it from siblings like 'list_module_versions' (which returns multiple versions) and 'get_module_details' (which provides broader information). The verb+resource combination is precise and unambiguous.

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 context through the parameter names (namespace, name, provider), suggesting it's for retrieving version information about specific Terraform modules. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_module_versions' or 'get_module_details', nor does it provide exclusion criteria or prerequisites.

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 provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns a dictionary with version number and metadata, which is helpful, but lacks details on error handling, rate limits, authentication requirements, or whether it's a read-only operation. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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?

The description is efficiently structured with a clear purpose statement followed by bullet points for arguments and returns. Every sentence adds value without redundancy, making it easy to scan and understand quickly. It's appropriately sized for a simple tool with two parameters.

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 low complexity (2 simple parameters), no annotations, and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose and parameters well, but could improve by adding usage guidelines or behavioral details like error cases. The output schema reduces the need to explain returns in the description.

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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'namespace' is the provider namespace with an example ('hashicorp') and 'name' is the provider name with an example ('aws'), clarifying the purpose and format of each parameter. This compensates well for the schema's lack of descriptions, though it could include more details like constraints or valid values.

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 with a specific verb ('Get') and resource ('latest version of a Terraform provider'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'list_provider_versions' or 'get_provider_details', which might offer similar functionality, preventing a perfect score.

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 provides no guidance on when to use this tool versus alternatives. With sibling tools like 'list_provider_versions' and 'get_provider_details' available, there's no indication of when this specific tool is preferred, leaving the agent to guess based on tool 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 provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implied read-only) and describes the return format, but lacks details on error handling, rate limits, authentication requirements, or whether it's a safe operation. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 well-structured and front-loaded: the first sentence states the core purpose, followed by clear 'Args' and 'Returns' sections. Every sentence adds value—no redundancy or fluff. The bullet-point format for parameters and returns enhances readability without wasting space.

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 moderate complexity (4 parameters, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, parameters with semantics, and return structure. However, it lacks context on error cases or operational constraints, which would be beneficial despite the output schema.

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 0%, so the description must compensate. It adds meaningful context for all 4 parameters: clarifying 'namespace' as 'Module namespace/publisher', 'name' as 'Module name', 'provider' as 'Provider name', and 'version' with its default value 'latest'. Examples (e.g., 'terraform-aws-modules', 'vpc', 'aws') provide practical guidance beyond the bare 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 clearly states the tool's purpose with a specific verb ('Get detailed information') and resource ('a specific Terraform module'). It distinguishes from siblings like 'list_module_versions' (which lists versions) and 'search_modules' (which searches broadly) by focusing on detailed info for a specific module. However, it doesn't explicitly contrast with all siblings like 'get_provider_details'.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention when to choose this over 'search_modules' for finding modules or 'list_module_versions' for version lists, nor does it specify prerequisites like authentication needs. Usage is implied but not explicitly 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?

With no annotations provided, the description carries the full burden. It discloses that documentation is fetched from GitHub and includes specific content types (e.g., 'Example usage code'), which adds useful behavioral context. However, it does not cover aspects like rate limits, authentication needs, or error handling, leaving gaps for a tool with external dependencies.

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 well-structured and front-loaded with the core purpose, followed by bullet points detailing fetched content and a clear parameter/return section. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 moderate complexity (4 parameters, external data source), no annotations, and an output schema present, the description is largely complete. It covers purpose, parameters, and return format. However, it lacks details on behavioral aspects like error cases or performance, which could enhance completeness for a tool interacting with GitHub.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose with examples (e.g., 'namespace: Provider namespace (e.g., 'hashicorp')'), clarifies the default for 'version', and ties them to the tool's function, fully compensating for the schema's lack of documentation.

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 specific action ('Get detailed documentation') and target resource ('for a specific provider data source'), distinguishing it from siblings like 'get_provider_docs' or 'get_provider_resource_docs' by focusing on data sources rather than providers or resources. It provides concrete examples (e.g., 'aws_ami') to illustrate scope.

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 when detailed documentation is needed for a data source, but does not explicitly state when to choose this tool over alternatives like 'search_provider_docs' or 'get_provider_docs'. It mentions fetching from GitHub, which provides some context, but lacks explicit guidance on use cases or 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implying read-only), but doesn't mention potential errors (e.g., if the provider doesn't exist), rate limits, authentication needs, or the format of the returned dictionary. This leaves significant gaps in understanding how the tool behaves in practice.

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 well-structured with clear sections for Args and Returns, and each sentence adds value without redundancy. It's front-loaded with the core purpose, though it could be slightly more concise by integrating the default note into the Args section more seamlessly.

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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines. The presence of an output schema mitigates the need to explain return values, but overall, it doesn't provide enough information for confident tool selection and invocation.

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 description adds minimal semantics beyond the input schema: it explains that 'namespace' and 'name' identify the provider with examples, and notes the default for 'version'. However, with 0% schema description coverage, it doesn't fully compensate by detailing constraints (e.g., valid namespace formats) or the meaning of 'latest'. The examples are helpful but insufficient for complete parameter understanding.

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 verb ('Get') and resource ('detailed information about a specific Terraform provider'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_provider_docs' or 'get_provider_data_source_docs', which might also retrieve provider information but with different scope or detail.

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 provides no guidance on when to use this tool versus alternatives like 'get_provider_docs' or 'search_providers'. It lacks context about prerequisites, such as whether the provider must be installed or available in a registry, and doesn't mention any exclusions or specific use cases.

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 provided, the description carries the full burden of behavioral disclosure. It reveals the source ('from GitHub') and typical content structure, which adds useful context beyond basic functionality. However, it doesn't mention potential failure modes, rate limits, authentication requirements for GitHub access, or what happens with invalid inputs.

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 efficiently structured with a clear purpose statement first, followed by bullet points of typical content, then explicit parameter and return value sections. Every sentence earns its place by providing necessary information without redundancy or fluff.

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 3 parameters with no schema descriptions and no annotations, the description does an excellent job explaining parameters and return format. The presence of an output schema reduces the need to fully document returns. Minor gaps include lack of error handling information and no explicit sibling tool differentiation, but overall it's quite complete for this complexity level.

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?

With 0% schema description coverage, the description fully compensates by explaining all three parameters in the Args section with clear examples (namespace: 'hashicorp', name: 'aws', version default: 'latest'). This adds essential meaning beyond the bare schema, making parameter purposes and formats immediately understandable.

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 specific action ('Get the main documentation page') and resource ('for a Terraform provider'), distinguishing it from siblings like get_provider_data_source_docs or get_provider_resource_docs that focus on specific documentation types. The first sentence provides a complete, unambiguous purpose statement.

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 context through the examples of what the documentation includes (e.g., version information, authentication), but doesn't explicitly state when to use this tool versus alternatives like get_provider_details or search_provider_docs. No explicit guidance on when-not-to-use or direct sibling comparisons is provided.

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 provided, the description carries the full burden of behavioral disclosure. It describes what content is fetched (documentation sections) and the return format (dictionary in markdown), but does not mention potential limitations like rate limits, authentication requirements, error conditions, or whether this is a read-only operation. It adds some behavioral context but leaves gaps for a tool with no annotations.

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 well-structured and appropriately sized. It starts with a clear purpose statement, then lists the fetched documentation sections in bullet points, followed by parameter explanations and return format. Every sentence adds value with no wasted words, and information is front-loaded effectively.

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 moderate complexity (4 parameters, no annotations, but with output schema), the description is mostly complete. It explains parameters thoroughly and describes the return format, though the output schema existence means it doesn't need to detail return values. However, without annotations, it could benefit from more behavioral context about limitations or prerequisites.

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?

With 0% schema description coverage, the description fully compensates by explaining all 4 parameters with clear semantics and examples. It defines namespace, name, resource_name, and version (including default value), adding essential meaning beyond the bare schema. This exceeds the baseline expectation when schema coverage is low.

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 specific action ('Get detailed documentation') and target resource ('for a specific provider resource'), distinguishing it from siblings like get_provider_docs (general provider docs) or get_provider_data_source_docs (data source docs). It includes a verb+resource combination that is precise and unambiguous.

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 context through the parameter explanations and return format, but does not explicitly state when to use this tool versus alternatives like get_provider_docs or get_provider_data_source_docs. It provides clear context about what documentation is fetched, but lacks explicit when/when-not guidance relative to siblings.

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 provided, the description carries full burden for behavioral disclosure. It mentions the return type ('Dictionary containing all available versions') but doesn't address important behavioral aspects like pagination, rate limits, authentication requirements, error conditions, or whether this is a read-only operation (though implied by 'list').

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 well-structured with clear sections for Args and Returns, and each sentence serves a purpose. It could be slightly more concise by combining the first sentence with the parameter explanations, but overall it's efficiently organized without wasted 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 that there's an output schema (though not shown here), the description doesn't need to fully explain return values. However, for a tool with 3 parameters, no annotations, and 0% schema description coverage, the description should provide more behavioral context about how the tool works, error handling, and usage patterns relative to sibling tools.

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 description explicitly documents all three parameters (namespace, name, provider) with brief explanations, which is valuable since schema description coverage is 0%. While the explanations are minimal ('Module namespace/publisher', 'Module name', 'Provider name'), they provide essential semantic context beyond the bare parameter names in 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 clearly states the action ('List all available versions') and resource ('of a Terraform module'), which provides specific verb+resource pairing. However, it doesn't explicitly differentiate from sibling tools like 'get_latest_module_version' or 'list_provider_versions', which would be needed for a perfect score.

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 provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'get_latest_module_version' and 'search_modules' available, there's no indication of when this specific listing function is preferred over other related operations.

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 provided, the description carries the full burden of behavioral disclosure. It states the tool lists versions but does not describe how the list is returned (e.g., sorted, paginated), error conditions, rate limits, or authentication needs. This is a significant gap for a tool with no 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?

The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for arguments and returns. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 moderate complexity (2 required parameters), no annotations, and the presence of an output schema (which handles return value documentation), the description is mostly complete. It covers purpose and parameters well but lacks behavioral details like error handling or usage context, which would be beneficial for full completeness.

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 description explicitly documents both parameters ('namespace' and 'name') with brief explanations, adding meaning beyond the input schema which has 0% description coverage. This compensates well for the schema's lack of parameter descriptions, though it could provide more context on valid values or examples.

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 with a specific verb ('List') and resource ('all available versions of a Terraform provider'), distinguishing it from siblings like 'get_latest_provider_version' which returns only the latest version. The description is precise and unambiguous about what the tool does.

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 provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_latest_provider_version' for retrieving only the latest version or 'get_provider_details' for provider metadata, leaving the agent without context for tool selection.

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 provided, the description carries full burden and adds valuable behavioral context: it specifies the search scope ('public registry'), discloses pagination behavior with defaults and limits (offset: 0, limit: 20, max: 50), and describes the return format. It doesn't mention rate limits or authentication needs, but covers core operational aspects well.

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?

Perfectly structured and front-loaded: purpose statement first, followed by organized Args and Returns sections. Every sentence adds value with no redundancy. The two-sentence format with bullet-point-like clarity makes it highly scannable and 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?

Given the tool's moderate complexity (4 parameters, 1 required), no annotations, but with output schema present, the description provides complete context: clear purpose, detailed parameter explanations, behavioral constraints, and return format description. The output schema existence means return values needn't be fully detailed, making this description comprehensive enough.

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 0%, so the description must compensate fully. It provides detailed parameter semantics beyond schema titles: explains query accepts 'module name or keywords', gives provider examples ('aws', 'azurerm', 'google'), clarifies offset/limit defaults and constraints. This adds substantial meaning not in 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 clearly states the specific action ('Search for Terraform modules') and resource ('in the public registry'), distinguishing it from siblings like search_providers or search_provider_docs that target different resources. It uses precise terminology that aligns with the tool's domain.

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 searching Terraform modules, but provides no explicit guidance on when to use this tool versus alternatives like get_module_details or list_module_versions. It mentions the public registry scope, which helps narrow context, but lacks sibling differentiation or exclusion criteria.

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 provided, so the description carries the full burden. It mentions searching for errors and troubleshooting, implying read-only behavior, but doesn't disclose critical details like whether it requires authentication, has rate limits, returns paginated results, or what format the 'matched sections' take. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 well-structured and front-loaded with the core purpose, followed by usage examples and parameter details. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 no annotations and no output schema, the description does a decent job covering purpose, usage, and parameters, but lacks details on behavioral traits (e.g., authentication, rate limits) and the structure of the returned dictionary. For a search tool with 4 parameters and rich sibling context, it's adequate but has clear gaps in transparency.

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 0%, so the description must compensate. It lists all four parameters (namespace, name, query, version) with examples (e.g., 'hashicorp', 'aws') and clarifies that version defaults to 'latest'. This adds meaningful context beyond the bare schema, though it doesn't explain parameter constraints or relationships in depth.

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 within provider documentation for errors, topics, or troubleshooting info, using a specific verb ('search') and resource ('provider documentation'). It distinguishes from siblings like 'get_provider_docs' (which presumably retrieves full docs) by focusing on search functionality, though it doesn't explicitly contrast with 'search_modules' or 'search_providers'.

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 provides explicit usage examples in a 'Useful for questions like' section, covering specific scenarios such as error interpretation, configuration, and troubleshooting. This gives clear context for when to use this tool, though it doesn't explicitly state when not to use it or name alternatives among siblings.

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 provided, the description carries full burden for behavioral disclosure. It mentions the tool searches a public registry and describes pagination behavior (offset/limit with defaults), but doesn't cover important aspects like rate limits, authentication requirements, error conditions, or what the search results dictionary contains. The description provides basic operational context but misses key behavioral traits.

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 well-structured with clear sections (Args, Returns) and uses bullet-like formatting. Every sentence adds value, though the opening statement could be more front-loaded with key constraints. It's appropriately sized for a 4-parameter search tool.

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 an output schema (which handles return value documentation) and the description covers all parameters well, the description is reasonably complete. However, for a search tool with no annotations, it should ideally mention more about the search behavior (e.g., fuzzy matching, ranking) and result format beyond just 'dictionary containing provider search results'.

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 description adds significant value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'query' for provider name/keywords, 'tier' with valid values ('official', 'partner', 'community'), 'offset' for pagination, and 'limit' with default and maximum values. This fully compensates for the schema's lack of descriptions.

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 for Terraform providers in the public registry, providing a specific verb ('search') and resource ('Terraform providers'). It distinguishes from most siblings (e.g., 'get_provider_details', 'list_provider_versions') by focusing on search functionality, though it doesn't explicitly differentiate from 'search_modules' which searches a different resource type.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over other search tools like 'search_modules' or when to use it instead of direct lookup tools like 'get_provider_details'. There's no context about prerequisites or typical use cases.

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