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 the full sequence (generate, validate locally, create via POST), the return object, the abort condition (no LLM provider or validation failure), and the required credentials (ANTHROPIC_API_KEY or OPENAI_API_KEY plus CSVBox credentials). This is comprehensive and honest about side effects (it creates a sheet).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences deliver the purpose, flow, return value, abort conditions, and required credentials without waste. The main action is front-loaded, followed by essential caveats. Every sentence 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?

For a tool with one parameter, no output schema, and no annotations, the description covers the essential decision points: what it does, what it returns, when it aborts, and what credentials are needed. Missing is any detail on the validation failure behavior beyond aborting, and there is no example output or error handling guidance, but these are not critical for invoking the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description for 'prompt' in the schema already explains it as a natural-language description of the importer. The tool description adds no extra detail about how the prompt is used, format expectations, or examples beyond what the schema provides. Baseline 3 is appropriate when schema fully documents the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Generate ... then create') and a clear resource (CSVBox sheet via POST /1.1/sheet). It is distinct from siblings like create_sheet (which likely creates without generation) and generate_sheet_json (which likely generates without creation). The flow is explicit 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?

It clearly implies use when you have a natural-language prompt and want a complete, validated sheet. It notes that an LLM provider must be configured and that it aborts if not, which hints at alternative workflows, but it does not explicitly name alternatives or state when to prefer create_sheet or generate_sheet_json over this tool. The context is clear but exclusions are not 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?

There are no annotations, so the description bears full responsibility for disclosing behavior. It only states that it returns the API response, but gives no detail on side effects, error handling, idempotency, or what happens on duplicate creation. For a creation tool this is a significant 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 brief and front-loaded with the action, which is good. However, its brevity comes at the cost of essential context, so it is under-specified rather than appropriately concise. A few extra sentences clarifying behavior and usage would be justified.

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?

The tool has no output schema, so the description should clarify what the response contains, but it only says 'Returns the API response' without specifics. The nested 'sheet' object is intentionally open-ended, but the description doesn't hint at required fields or validation. Given the simplicity of the tool and sibling complexity, the description is incomplete.

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 already provides full coverage of the sole parameter, describing the 'sheet' as 'The full CSVBox sheet object to create.' The tool description adds nothing beyond restating the parameter name and format. Since coverage is 100%, the description meets the baseline but adds no extra 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 states a clear action: creating a new CSVBox sheet via a POST request, and identifies the resource explicitly. It distinguishes from siblings like update_sheet and patch_sheet by indicating creation, though it doesn't explicitly call out the distinction. The purpose is 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 provides no guidance on when to use this tool versus alternatives such as update_sheet or patch_sheet. It does not mention any prerequisites, when creation is appropriate, or when another sibling might be better. An agent would have to infer usage from the tool name 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 mentions replacing the license key placeholder, which is a useful note, but it does not state whether the tool has side effects, what the output format is, or any prerequisites. The agent is left guessing about the return value and if any state changes occur. This is a notable gap for a tool that generates code.

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, information-dense sentence. It front-loads the main action, immediately lists the supported frameworks with version clarifications, and includes the only crucial auxiliary instruction (replacing the license key placeholder). There is zero 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?

For a one-parameter tool with no output schema, the description covers the essential information: what it does, the input, and a critical post-processing step. While it doesn't explicitly describe the return format, the term 'integration code' makes it obvious. Minor omission: it doesn't state whether the code is returned as a string, file, or other, but this is not critical for invoking the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameter `framework` is already fully documented via enum and description. The tool description merely repeats 'Input: { framework }' without adding new parameter-specific details. The license key placeholder note concerns the output, not the input, so it doesn't enhance parameter understanding. 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 states a clear action ('generate complete CSVBox importer integration code'), a specific resource (CSVBox importer integration), and the parameter (framework). It lists all supported values, distinguishing it from sibling tools like generate_sheet_json or create_sheet. It is 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 is self-contained and clearly implies when to use it: whenever you need integration code for a listed framework. It does not explicitly mention alternatives or exclusions, but the context is clear enough that an agent can infer appropriate usage without confusion.

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 present, so the description carries the full burden. It discloses that the tool does not call the API, omits unmentioned collections rather than returning empty arrays, returns a specific JSON shape, requires ANTHROPIC_API_KEY or OPENAI_API_KEY, and names the error fallback. This is unusually transparent.

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 dense but every sentence earns its place: purpose, non-API behavior, output contract, authentication requirement, error handling, and input shape. The structure front-loads the main purpose before moving to constraints and fallbacks.

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?

Even though there is no output schema, the description provides the return contract, omission behavior, auth prerequisites, and the downstream patch_sheet step. With only two parameters and one required, nothing essential is left for the agent to infer.

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 well. The description only restates the input shape as '{ prompt, sheet? }' and adds no new meaning beyond what the schema's per-parameter descriptions provide. 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?

Uses a specific verb and resource ('Author CSVBox virtual_columns, validation_functions, and data_transforms') and clearly states it returns reviewable JSON rather than calling the CSVBox API. This distinguishes it from patch_sheet, create_sheet, and generate_import_code even before reading schemas.

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?

Establishes a clear workflow: generate functions, review the returned JSON, then apply with patch_sheet. It also says the tool does not call the API and warns about missing API keys, but it does not explicitly contrast itself with generation siblings like generate_sheet_json or generate_import_code.

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 behavioral burden. It discloses several key behaviors: no API call, key requirements, error fallback, and the return shape { sheet, source, validation }. However, it does not describe whether the call is synchronous, how long it may take, token/rate-limit considerations, or the meaning of the returned 'source' and 'validation' fields — moderate disclosure but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, all load-bearing: the exclusions ('Does NOT call...') and key requirement ('Requires ANTHROPIC_API_KEY...') are front-loaded before the input spec. The use of ALL-CAPS for emphasis is a minor stylistic distraction, but overall it is compact with no wasted sentences.

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 single-input-parameter tool with no output schema and no annotations, the description covers the essentials: what it returns, its prerequisites, a common failure mode, and what the prompt parameter expects. The only notable gap is the absence of a minimal output/JSON example structure, which would help the agent gauge the result format.

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 single parameter prompt is fully documented in the schema (100% coverage, with a concrete example). The description adds only 'natural-language prompt' context, which marginally reinforces but does not extend the schema. Baseline 3 is appropriate where the schema already carries the parameter meaning.

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+resource ('Generate a COMPLETE CSVBox sheet') and enumerates the exact fields produced (title, sheet_columns, destinations, webhooks, security_settings, steps). It distinguishes itself from the API-calling siblings by explicitly stating 'Does NOT call the CSVBox API,' which is the key differentiator among the sibling list.

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 states the enabling condition (ANTHROPIC_API_KEY or OPENAI_API_KEY required) and the failure behavior when absent (returns an error pointing to create_csvbox_sheet). It clearly implies this tool generates JSON only and is the predecessor to an API-calling tool, though it doesn't name an explicit 'when not to use' sibling or a direct comparison to create_importer_from_prompt.

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, the description carries full burden and discloses substantial behavior: PATCH merges, only listed items are touched, empty array is a no-op, matching by names with unmatched creating new items, _delete: true for removal, and that other fields on a _delete item are ignored. It also mentions the return is 'the API response.' This exceeds typical descriptions, though it omits error handling or permission nuances.

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 dense but each sentence earns its place: purpose, input/output, merge semantics, matching rules, deletion, and usage advice. It's front-loaded with the purpose, and while it's longer than average, the complexity of the operation justifies the length. No 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 complexity (nested objects, merge behavior, deletion), the description covers all critical aspects: how partial updates work, how to target items, how to delete, and the prerequisite validation step. It returns the API response but doesn't detail error cases, which is acceptable without an output schema. The description is sufficient for an agent to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds significant meaning to the 'changes' object by explaining the internal structure (virtual_columns, validation_functions, data_transforms), how items are matched by names, and the _delete flag semantics. This goes well beyond the schema's minimal 'Only the partial changes to apply to the sheet.'

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Partially update'), resource ('CSVBox sheet'), and the HTTP method (PATCH /1.1/sheet/{sheet_license_key}'). It clearly differentiates from sibling tools like update_sheet by emphasizing 'partial' and 'PATCH', and from create_sheet by 'update'. The purpose is 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?

Provides clear context: 'This is the safe verb for applying generated functions' and instructs to 'Run validate_schema with mode patch first.' While it doesn't explicitly name update_sheet as an alternative, the 'partial' vs full update distinction and the 'safe verb' guidance effectively imply when to prefer this tool over others.

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, the description carries the full burden and discloses key behavior: it explains the server-side decoding of file_base64 into true binary multipart content, the alternative JSON path, and that it returns the API response. It does not mention authentication or side effects, but the mutation is implied by 'submit for import' and the transport detail goes beyond basic 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 two sentences with no filler. It front-loads the endpoint and the primary decision (which submission mode), then presents the full input structure compactly. Every clause contributes 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 8 parameters, nested objects, and two mutually exclusive alternatives, the description is thorough: it maps the parameter combos, explains the encoding nuance, and notes the return value. It omits authentication prerequisites and error handling, but those are commonly left to the API docs and the schema already covers required fields.

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%, but the description adds real value beyond the schema by showing the exact input structure with alternatives and grouping, clarifying the mutual exclusivity already hinted in properties, and adding the file-size preference. This is more than the baseline 3 would require.

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 (submit a file for import), the resource (via POST /1.1/file), and the two distinct submission modes (JSON vs multipart). It distinguishes this tool from siblings like create_sheet or generate_import_code by focusing on the file submission action.

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 explicitly lays out the mutually exclusive parameter combinations ('Provide exactly one of...') and even adds a practical preference ('Prefer public_file_url for large files'). It does not explicitly contrast with sibling tools, but the sibling set is clearly different in function, so this gap is minor.

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 — and it excels. It discloses the destructive PUT semantics (unnamed items are DELETED), which collections are affected (virtual_columns, validation_functions, data_transforms), the critical empty-array-deletes vs omit-preserves distinction, and invalidates `_delete`. The safety warnings cover all dangerous behavior an agent needs to avoid.

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 long, but every sentence earns its place for a destructive operation — the PUT-authoritative semantics, collection scope, and empty-vs-omit warning are all essential safety information. Core purpose is front-loaded before the behavioral warnings. Slightly verbose, justified by the risk profile.

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 mutation with zero annotations and no output schema, this is complete. It covers what gets destroyed, the collection semantics, the invalid `_delete`, and pre-validates via validate_schema. Return value is minimally stated ('Returns the API response'), which is acceptable given no output schema exists. Nothing an agent needs to call this safely is missing.

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%, so both parameters (sheet_license_key, sheet) are already documented inline, warranting baseline 3. The description adds some value by clarifying the destructive behavior of collections inside the `sheet` object, but it doesn't add syntax or format details beyond the schema's 'full replacement CSVBox sheet object'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Replace'), resource ('an existing CSVBox sheet'), and HTTP method (PUT /1.1/sheet/{sheet_license_key}'). The title reinforces the replace-vs-patch distinction, and the description names patch_sheet for partial updates, making it distinguishable from siblings without opening schemas.

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?

Explicitly routes to alternatives with conditions: 'use patch_sheet to remove a single item', declares `_delete` is not valid here, and instructs 'Run validate_schema with mode put first'. This gives clear when-to/when-not guidance and names the safety pre-check, which is ideal for a destructive tool.

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 thoroughly describes what the tool does: it performs a local validation, enumerates all the checks (missing fields, duplicates, types, references, dependency rules), explains the mode-dependent behavior (empty array vs _delete), and states the return shape { valid, errors, warnings }. This is exceptionally transparent and leaves no ambiguity about side effects or limitations.

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, information-dense paragraph that front-loads the core purpose and then systematically covers prerequisites, specific checks, mode handling, and return format. Every sentence adds value; there is no filler or repetition. The structure is logical and allows an agent to quickly grasp the tool's behavior.

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 (many validation rules), the minimal schema, and absence of annotations, the description is remarkably complete. It covers all relevant inputs (sheet, mode), the exact validation logic, the behavior under different modes, and the output structure. There is no critical information missing for an agent to call this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema descriptions cover both parameters, the tool description adds significant meaning. For 'mode', it explains the impact of each enum value on validation rules, which the schema description only partially hints at. For 'sheet', since the schema only says 'object', the description is the sole source of truth for what the sheet object should contain (sheet_columns, virtual_columns, validation_functions, data_transforms). This goes well beyond the schema's generic definition.

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 opens with a specific verb and resource: 'Validate a CSVBox sheet schema locally (no API call).' This clearly distinguishes it from sibling tools like create_sheet or update_sheet, which perform mutations. The phrase 'no API call' further separates it from any network-dependent tool. The purpose is 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 clearly conveys when to use this tool: before an API call to validate the schema against the intended write verb, as shown by the mode parameter explanation ('Pass mode... so empty-array and _delete rules match the verb you are about to use'). However, it does not explicitly name alternative tools or state 'use this instead of X,' so it stops short of full exclusion guidance. The context is adequate for an agent to infer usage.

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