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 checks existence and gets basic info, but doesn't describe what 'basic info' includes (e.g., creation date, permissions), error handling for non-existent buckets, or any rate limits or authentication requirements. This is a significant gap for a tool with zero 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 a single, efficient sentence that front-loads the core functionality ('Check if a bucket exists and get basic info'). There is no wasted text, making it appropriately sized and well-structured for its purpose.

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 complexity of a read operation with no annotations and no output schema, the description is incomplete. It doesn't explain what 'basic info' entails in the return values, error conditions, or behavioral traits like idempotency. For a tool with zero structured data coverage, this leaves critical gaps for an AI agent.

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 schema description coverage is 100%, with the single parameter 'bucket' documented as 'Bucket name' in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as format constraints or examples, so it meets the baseline for high schema coverage without compensating value.

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 ('Check') and resource ('bucket'), explaining it verifies existence and retrieves basic information. However, it doesn't explicitly differentiate from sibling tools like 'list_buckets' (which lists all buckets) or 'get_object' (which retrieves object data), missing full sibling distinction.

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 scenarios like verifying bucket existence before operations, or contrast with 'list_buckets' for listing all buckets or 'get_object' for object-specific data, leaving usage context implied rather than explicit.

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 but only states the action without behavioral details. It doesn't disclose that this is destructive (permanent deletion), requires specific permissions, has no confirmation step, or mention error conditions (e.g., non-existent object), which are critical for safe usage.

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, direct sentence with zero waste—it states exactly what the tool does without fluff. It's front-loaded and appropriately sized for a simple tool, earning full marks for efficiency.

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 destructive tool with no annotations and no output schema, the description is inadequate. It lacks crucial context like irreversible effects, permission requirements, or what happens on success/failure, leaving significant gaps in understanding the tool's behavior and risks.

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 ('bucket' and 'key') clearly documented in the schema. The description adds no additional meaning beyond implying an S3 context, so it meets the baseline of 3 without compensating for gaps since 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 action ('Delete') and resource ('object from an S3 bucket'), making the purpose immediately understandable. However, it doesn't distinguish this from sibling tools like 'put_object' or 'get_object' beyond the verb difference, which prevents 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. It doesn't mention prerequisites (e.g., existing object), exclusions, or comparisons to siblings like 'delete_object' vs 'put_object' for updates, leaving the agent with minimal context for 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 the full burden. It discloses the tool's behavior as a read operation ('Download and read'), which is accurate for a get operation. However, it lacks details on permissions, rate limits, error handling, or whether it streams or loads entire content, leaving gaps in behavioral context.

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, efficient sentence with zero waste—it directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized for a simple 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 no annotations and no output schema, the description is minimal but covers the basic purpose. It lacks details on return values (e.g., text format, size limits) and behavioral traits like error cases, which are important for a read operation. It's adequate but has clear gaps in 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?

Schema description coverage is 100%, so the schema already documents both parameters (bucket and key) fully. The description does not add any additional meaning or syntax details beyond what the schema provides, such as examples or constraints, meeting the baseline for high schema coverage.

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 ('Download and read'), the resource ('contents of an S3 object'), and the format ('as text'), which distinguishes it from siblings like list_buckets (listing) or presigned_url (generating URLs). It uses precise verbs and specifies the output format.

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 retrieving object contents, but does not explicitly state when to use alternatives like presigned_url for sharing or list_objects for browsing. It provides clear context for reading data but lacks explicit exclusions or comparisons to sibling tools.

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 states it's a listing operation but doesn't mention whether it requires specific AWS permissions, how results are returned (pagination, format), rate limits, or error conditions. This leaves significant gaps for an agent to understand operational 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 a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple listing operation and front-loads the essential 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?

For a tool with no annotations, no output schema, and multiple sibling tools, the description is insufficiently complete. It doesn't explain what format the bucket list returns, how to handle large numbers of buckets, or how this differs from other listing operations. The agent would need additional context to use this effectively.

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 with 100% schema description coverage, so the schema already fully documents the input requirements. The description appropriately doesn't waste space discussing non-existent parameters, earning a baseline score of 4 for this zero-parameter case.

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 ('List') and resource ('all S3 buckets in your AWS account'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_objects' or 'bucket_info', which would require more specific scope clarification.

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 'list_objects' or 'bucket_info'. It mentions no prerequisites, exclusions, or contextual factors that would help an agent choose between similar listing 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 mentions optional filtering but fails to describe key behaviors: whether this is a read-only operation (implied but not stated), how results are returned (e.g., pagination, format), error conditions, or rate limits. For a tool with 3 parameters and no annotation coverage, this leaves significant 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 a single, efficient sentence that front-loads the core purpose ('List objects in an S3 bucket') and adds optional details concisely. Every word earns its place, with no redundancy or unnecessary elaboration, making it easy to parse quickly.

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 output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and optional parameters but lacks details on behavior, output format, or error handling. This makes it minimally viable but with clear gaps for effective agent use.

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 fully documents all parameters (bucket, prefix, maxKeys). The description adds marginal value by mentioning 'prefix' and 'limit count' (referring to maxKeys), but doesn't provide additional semantics beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate 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 clearly states the verb ('List') and resource ('objects in an S3 bucket'), making the purpose immediately understandable. It distinguishes this from siblings like 'list_buckets' (which lists buckets rather than objects) and 'get_object' (which retrieves a specific object). However, it doesn't explicitly mention that this lists ALL objects matching criteria, which would make it a perfect 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 implies usage through the optional filtering parameters ('Optionally filter by prefix and limit count'), suggesting when to use these features. However, it lacks explicit guidance on when to choose this tool over alternatives like 'list_buckets' (for buckets) or 'get_object' (for specific objects), and doesn't mention prerequisites 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. It mentions 'temporary access' but lacks details on security implications, authentication requirements, rate limits, or what happens after expiry. For a tool generating access URLs with no 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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity. Every word 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?

Given no annotations, no output schema, and a tool that generates access URLs (implying security and temporal behavior), the description is incomplete. It doesn't cover return values, error cases, or critical context like URL usage constraints. For this complexity level, more information is needed.

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 fully documents parameters (bucket, key, expiresIn). The description adds no additional meaning beyond implying URL generation for S3 objects, which is already inferred from the tool name. Baseline 3 is appropriate as the schema handles parameter 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 tool's purpose with a specific verb ('Generate') and resource ('presigned URL for temporary access to an S3 object'). It distinguishes from siblings like 'get_object' by focusing on URL generation rather than direct object retrieval. However, it doesn't explicitly differentiate from all siblings (e.g., 'put_object' might also involve URLs).

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 scenarios like sharing temporary access, bypassing direct authentication, or when to choose this over 'get_object' for object access. No exclusions or prerequisites are 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Upload text content' which implies a write/mutation operation, but doesn't disclose critical traits: whether it overwrites existing objects, requires specific permissions, has rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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, efficient sentence that front-loads the core action ('Upload text content to an S3 object'). It wastes no words and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by conveying essential information without redundancy.

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 complexity (a mutation tool for cloud storage), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like overwrite behavior, error handling, or return values, which are crucial for safe usage. The high schema coverage helps with parameters, but overall context is insufficient for an agent to use this tool confidently.

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 all four parameters (bucket, key, content, contentType) with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or constraints. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is added.

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 ('Upload') and resource ('text content to an S3 object'), making the purpose immediately understandable. It distinguishes from siblings like 'get_object' (download) and 'delete_object' (remove), though it doesn't explicitly mention the S3 context which siblings imply. The description is specific but could be slightly more precise about the S3 service.

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 prerequisites (e.g., bucket must exist), when not to use it (e.g., for binary data if limited to text), or refer to sibling tools like 'presigned_url' for alternative upload methods. Usage is implied by the action but lacks explicit context.

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