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

With no annotations provided, the description carries full burden. It states this is a creation operation (implying mutation) but doesn't disclose behavioral traits like required permissions, whether the operation is idempotent, error conditions, rate limits, or what happens on success/failure. The mention of 'Extract key information from user-provided text' adds some context about input processing, but overall behavioral disclosure is minimal for a mutation tool.

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 concise (two sentences) and front-loaded with the core purpose. Both sentences add value: the first states the action and resource, the second provides implementation context. No redundant information or unnecessary elaboration is present.

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 mutation tool with 3 parameters (including complex nested objects), no annotations, and no output schema, the description is insufficient. It doesn't explain what happens after creation (e.g., returns record ID, success status), error handling, or important behavioral constraints. The schema handles parameter documentation well, but the description fails to compensate for the lack of annotations and output information.

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 parameters are well-documented in the schema. The description adds minimal value beyond the schema: it mentions 'Extract key information from user-provided text' which loosely relates to the 'fields' parameter, and references 'get_fields_schema' for structure. However, it doesn't explain parameter interactions or provide additional semantic context beyond what's already in the schema 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 action ('Create a new record') and resource ('in the datasheet'), with additional context about extracting information from user text. It distinguishes from siblings like 'list_records' by specifying creation rather than retrieval. However, it doesn't explicitly differentiate from potential similar creation tools (though none are listed).

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 mentions using 'get_fields_schema' for structure and 'upload_file_via_url' for attachments, providing some implementation guidance. However, it lacks explicit when-to-use criteria, prerequisites, or comparisons to alternatives like 'upload_attachment_via_url' for attachment handling. No guidance on when NOT to use this tool 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a JSON schema and its purpose for LLM understanding, which is useful behavioral context. However, it does not mention potential side effects, error conditions, or performance aspects (e.g., if it's a read-only operation, though implied by 'Returns'), leaving gaps in 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 concise with two sentences that directly state the tool's function and its intended use. It is front-loaded with the core purpose, and the second sentence adds value by explaining the context for LLM integration. There is minimal waste, though it could be slightly more structured for clarity.

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 is low (single parameter, no output schema), the description is somewhat complete by explaining the return type and purpose. However, it lacks details on output format, error handling, or integration with sibling tools, which could enhance completeness. Without annotations or output schema, it provides basic but not thorough contextual coverage.

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 100% description coverage, with 'node_id' documented as 'The ID of the database to fetch records from.' The description adds no additional parameter details beyond this, such as format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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: 'Returns the JSON schema of all fields within the specified database.' It specifies the verb ('Returns') and resource ('JSON schema of all fields'), making the function evident. However, it does not explicitly differentiate from sibling tools like 'search_nodes' or 'list_spaces', which might also involve database interactions, so it misses 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 implies usage by stating the schema 'will be sent to LLM to help the AI understand the expected structure of the data,' suggesting it's for data structure comprehension. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'list_records' for actual data or 'search_nodes' for node details), and does not mention prerequisites or exclusions, leaving usage context somewhat vague.

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 it's a read operation. It doesn't disclose authentication requirements, rate limits, error conditions, pagination behavior beyond mentioning support, or what happens with invalid inputs. For a 7-parameter tool with complex filtering/sorting capabilities, 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 that front-loads the core purpose. It wastes no words but could be slightly more structured by separating core function from capabilities. Every element earns its place, though it's somewhat dense for quick scanning.

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 complex read tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain return format, error handling, authentication needs, or practical usage patterns. The agent must rely entirely on the input schema and trial-and-error to understand this tool's full behavior and limitations.

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 7 parameters. The description adds minimal value beyond confirming pagination, filtering, and sorting capabilities - which are already evident from parameter names. It doesn't provide additional context about parameter interactions or usage patterns beyond what's in the schema 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's purpose as 'Read the records from a specified datasheet' with specific capabilities (pagination, field filtering, sorting). It distinguishes from siblings like 'create_record' (write vs read) and 'get_fields_schema' (metadata vs data), but doesn't explicitly differentiate from 'search_nodes' which might overlap in data retrieval functions.

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 'search_nodes' or 'create_record'. It mentions capabilities but doesn't specify scenarios, prerequisites, or exclusions. The agent must infer usage from the tool name and parameter set 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 fetches data, implying a read-only operation, but does not specify details like pagination, rate limits, authentication requirements beyond 'currently authenticated user', or error handling. 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 a single, clear sentence that efficiently conveys the core functionality without unnecessary details. It is front-loaded with the main action and resource, making it easy to parse and understand quickly, with zero 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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks depth on behavioral aspects like return format or error cases. For a read operation with no structured metadata, more context on output or usage scenarios would enhance 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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description adds value by clarifying that no inputs are needed, as it fetches based on the user's permissions. This compensates well for the lack of parameters, though it could briefly mention any implicit assumptions.

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 ('fetches') and resource ('workspaces'), specifying that it retrieves all workspaces accessible to the authenticated user. However, it does not differentiate from potential sibling tools like 'search_nodes' or 'list_records', which might also retrieve workspace-related data, leaving some ambiguity about uniqueness.

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 needing to access permitted workspaces but provides no explicit guidance on when to use this tool versus alternatives like 'search_nodes' or 'list_records'. It lacks context on prerequisites, exclusions, or comparisons with sibling tools, offering minimal directional help.

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 retrieving nodes based on types, permissions, and queries, but fails to detail critical behaviors: it doesn't specify if this is a read-only operation, what permissions are required, how results are returned (e.g., pagination, format), or any rate limits. The mention of 'permissions' is vague without elaboration, leaving significant gaps in understanding the tool's 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 appropriately sized and front-loaded, starting with the core purpose in the first sentence. The second sentence adds context about node types without redundancy. There's no wasted text, and it efficiently conveys key information in two sentences, making it easy to scan and understand.

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 search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., read-only nature, permissions, result format), usage guidelines, and output expectations. While it covers basic purpose and parameter context, it doesn't compensate for the missing structured data, leaving the agent with insufficient information for reliable tool 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?

Schema description coverage is 100%, so the schema already documents all three parameters (node_type, query, space_id) with clear descriptions. The description adds minimal value beyond the schema: it lists node types (datasheets, form, dashboard, folders) which partially overlaps with the schema's 'node_type' description, but doesn't provide additional syntax, format details, or usage examples. This meets 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 tool's purpose: 'Retrieve nodes based on specific types, permissions, and queries' with a specific verb ('Retrieve') and resource ('nodes'). It distinguishes itself from siblings like 'list_spaces' or 'list_records' by focusing on node retrieval with filtering. However, it doesn't explicitly differentiate from 'get_fields_schema' or 'create_record' in terms of search vs. creation operations.

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 mentions filtering by types and queries but doesn't specify scenarios, prerequisites, or exclusions. For example, it doesn't clarify if this should be used over 'list_spaces' for workspace-level operations or how it relates to 'list_records' for data retrieval. This lack of context leaves usage ambiguous.

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 discloses that the tool performs an upload (implying a write operation) and returns storage information, but lacks details on permissions required, rate limits, error conditions, or what happens if the URL is invalid. For a mutation tool with zero annotation coverage, this is a moderate gap.

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 with two sentences that are front-loaded and efficient. The first sentence states the purpose, and the second explains the return value and usage context, with zero 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 the tool's complexity (a mutation with 3 parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and usage context but lacks details on behavioral aspects like authentication needs, error handling, or return format specifics, which are important for a tool with no structured output.

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 three parameters (attachment_name, attachment_url, node_id) with clear descriptions. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, meeting the baseline score of 3.

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 ('Upload an attachment') and resource ('to the AITable server using its web URL'), distinguishing it from sibling tools like create_record or update_record. It explicitly mentions the upload mechanism (via URL) and the purpose (to get storage information for record association).

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 clear context by stating that the tool returns storage information that can be passed to create_record or update_record tools, indicating when to use it (as a precursor to those operations). However, it doesn't explicitly state when NOT to use it or mention alternatives for attachment handling, such as direct file uploads if available.

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