data-transform

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

With no annotations, the description carries the full burden of behavioral disclosure. It states the core transformation but omits critical details such as whether the first row is treated as headers, handling of edge cases (quotes, newlines, empty values), and how the optional delimiter parameter behaves. This is a minimal description that leaves many behavioral aspects unspecified.

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 is front-loaded with the essential verb and resource. It contains no filler or redundant information, making it highly concise and well-structured.

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 is relatively simple, and the description states the input and output formats ('CSV text' to 'JSON array of objects'). However, it lacks important contextual details such as whether the CSV is expected to have a header row and how the delimiter is applied. Given no output schema or annotations, the description should provide more context to be fully complete, but it does cover the primary purpose.

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 documents both parameters (csv and delimiter) with descriptions, achieving 100% coverage. The tool description adds no extra meaning beyond what the schema already provides, but it does not need to since the schema is self-explanatory. Thus the baseline score of 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 'Parse CSV text into a JSON array of objects' clearly specifies the verb (parse), resource (CSV text), and output (JSON array of objects). It distinguishes the tool from siblings like json_to_csv (reverse operation) and json_to_xml (conversion to XML), making its unique purpose immediately apparent.

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 does not mention whether to use it for parsing CSV input or when not to, and there is no reference to sibling tools such as json_to_csv or filter. Users are left to infer the intended use from the name.

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 bears the full burden of behavioral disclosure. It mentions support for >, <, and ! prefixes, which is useful, but it does not state whether the original array is modified, what happens with non-matching elements, type coercion, or how invalid queries are handled. The return value (presumably a filtered array) is also not explicitly described.

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 concise sentences, front-loaded with the primary action ('Filter a JSON array') followed by the key syntax detail. Every word earns its place; no fluff or 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 tool's simplicity and full schema coverage, the description is minimally adequate. However, it lacks information about return values, edge cases (e.g., missing fields, type mismatches), and whether the operation is read-only or mutative. No output schema exists to clarify the result, so the description should have provided more behavioral context.

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 baseline is 3. The description adds the list of supported comparison prefixes, which provides some extra meaning beyond the schema example, but it largely repeats what the schema already explains (e.g., the query object example with '>30'). No significant new information is added for either 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 uses a specific verb ('Filter') and resource ('a JSON array') with a clear mechanism ('by a query object'). This distinguishes it from sibling tools like pick (selecting properties), sort (ordering), and stats (aggregating), making the purpose 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 for filtering JSON arrays by query criteria and gives details on comparison prefixes, but it does not explicitly state when to use this tool versus alternatives like pick or validate, nor does it mention any exclusions or prerequisites. The example in the schema provides context, but the description itself lacks explicit before/after guidance.

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 'dot-notation keys' but omits edge cases (arrays, key collisions, circular references), whether the input is mutated, or the exact return format. This is insufficient for a transformation tool without 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 a single, concise sentence with no wasted words. It is front-loaded with the action and outcome, making it immediately understandable.

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 (one parameter, no output schema, no annotations), the description covers the core purpose. However, it lacks details on output structure and array handling, which would be valuable for a complete understanding. It is minimally adequate but not thorough.

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% as the only parameter 'data' is described as 'Nested object to flatten'. The description repeats this concept without adding new semantics, so the baseline 3 applies.

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 ('flatten') and resource ('nested JSON object'), clearly stating the output format ('dot-notation keys'). It distinguishes itself from the sibling tool 'unflatten' by implying the opposite operation.

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 purpose is self-evident, but the description provides no explicit guidance on when to use this tool versus alternatives like 'pick' or 'unflatten'. Context suggests flattening is useful for converting nested structures, but no exclusions or alternative recommendations are given.

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 only states the conversion action and output type ('CSV text') but omits important behaviors such as how nested objects are handled, whether headers are generated from object keys, or how missing/null values are treated.

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 sentence, front-loaded with the primary action and output. It is appropriately short with 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?

This is a simple tool with only two parameters and no output schema, but the description does not fully clarify the output format (e.g., whether it includes headers, how it serializes nested objects). It gives just enough to understand the basic function, leaving some ambiguity for an 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?

Schema description coverage is 100% for both parameters, so baseline is 3. The description's mention of optional delimiter and default comma adds no new meaning beyond the schema already provides. Data parameter description in schema is adequate.

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 converts a JSON array of objects to CSV text, which is a specific verb+resource. It distinguishes this from sibling tools like json_to_xml and csv_to_json by naming the target 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 context that this tool is for converting JSON to CSV is implied by the name and description, but no explicit guidance is given about when to choose it over alternatives. It doesn't mention exclusions or alternative tools for related tasks like flattening nested objects first.

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 disclosing behavior. It does state input scope ('object or array') and output type ('XML string'), which are useful behavioral details. However, it omits information about error handling (e.g., invalid JSON), formatting specifics, or side effects. This is adequate 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?

The description is a single sentence of 10 words, front-loaded with the action and concise. Every word is meaningful, and there is no superfluous 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 simple conversion tool with complete parameter schema, the description adequately conveys input and output types. It doesn't explain return values in detail, but 'XML string' explicitly names the output. Minor omission: no mention of error cases, but overall it is sufficiently complete for the task.

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%: both 'data' and 'rootName' have descriptions, with rootName noting a default. The description itself adds no parameter-level detail beyond what the schema already provides, so the baseline of 3 applies.

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 'Convert a JSON object or array to XML string' uses a specific verb ('convert'), specifies the input resource ('JSON object or array'), and names the output format ('XML string'). This clearly distinguishes it from sibling tools like json_to_csv or csv_to_json, which target different formats.

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 implies when to use this tool: when you have JSON data and need XML output. While it doesn't explicitly mention alternatives or exclusions, the purpose context is obvious given the sibling tools, and no additional guidance is necessary for this straightforward conversion task.

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 only states the basic operation and omits edge cases like missing fields, output ordering, whether the input is mutated, or the exact output format.

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 one concise sentence that front-loads the operation and resource, with no filler. It is appropriately sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool, the description covers the core purpose but lacks edge-case behavior and usage context. Without annotations or an output schema, the agent receives no guidance on handling missing fields or the structure of the result, making it adequate but not fully complete.

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

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

The input schema already provides 100% coverage for both parameters ('Array of objects' and 'Field names to keep'). The description adds minimal extra meaning, essentially restating the selection behavior without additional syntactic or semantic detail.

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 function: selecting specific fields from each object in a JSON array. This verb+resource phrasing distinguishes it from siblings like filter (which selects objects) and flatten (which restructures hierarchy).

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 usage is implied by the verb 'pick'—one would use this when wanting to project only certain fields. However, there is no explicit guidance about when to use this tool versus alternatives, and no when-not-to-use context.

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, but it only states the basic operation. It does not disclose whether the sort is stable, whether the input array is mutated, how missing fields are handled, or the nature of the return value.

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, focused sentence that is front-loaded with the verb 'Sort' and directly states the target. Every word earns its place, and there is no unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple sort tool, the description is adequate for the core action but lacks details about the return value, mutation behavior, or error cases. With no annotations or output schema, this leaves notable gaps for an agent to predict behavior fully.

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 complete descriptions for data, field, and order, including an enum for order. The tool description adds minimal semantic value beyond echoing the 'field' parameter, so the schema carries the weight.

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 sorts a JSON array by a field, using a specific verb and resource. It is easy to distinguish from sibling tools like filter or pick, as sorting is a distinct operation.

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 sorting but provides no explicit guidance on when to choose this tool over alternatives, nor does it mention limitations or exclusions. No context is given for edge cases or integration with other 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool computes the listed statistics and implicitly that it does not modify the input, but it does not explain how missing or non-numeric values are handled, nor the exact return format. For a read-only computation, this is acceptable but leaves 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, concise sentence that front-loads the action and includes the essential detail of which statistics are computed. Every word earns its place, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (two required parameters, no output schema, no annotations), the description covers the core functionality well: computing specific stats on a numeric field. It lacks edge-case handling and explicit return details, but for a simple computation tool, this is largely sufficient. Sibling tool differentiation is not explicitly addressed, but the purpose clarity resolves this.

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 descriptions for both parameters ('data' as array of objects, 'field' as numeric field). The description does not add parameter-specific meaning beyond what the schema states; it only clarifies the output statistics, which is not parameter-level. With 100% schema coverage, baseline 3 is appropriate.

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

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

The description clearly states the tool's action ('Compute') and the resource ('statistics for a numeric field'), listing specific statistics (count, sum, mean, min, max, median). This distinguishes it from sibling tools like filter, sort, or flatten, which perform different transformations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when needing statistical aggregations on a numeric field), but it does not explicitly state when not to use it or mention alternatives. There is no comparison to sibling tools, so the agent must infer usage from the tool name and description.

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, but it only restates the core transformation. It does not disclose behavior for edge cases like key collisions, handling of arrays, or invalid inputs. The phrase 'back into nested structure' is essentially a restatement of purpose, not additional behavioral detail.

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 sentence with zero filler. It is front-loaded with the verb and resource, making it immediate and clear. No unnecessary words or 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?

The tool is simple with one parameter and no output schema, so the description is minimally adequate. However, it lacks details on return value format, error handling, or relationship to flatten, which would be useful given the absence of annotations and 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 coverage is 100% with the parameter 'data' described as 'Flat dot-notation object to unflatten'. The tool description adds no new semantics beyond the schema, so the baseline score of 3 applies.

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 'unflatten' with a distinct resource 'dot-notation JSON object' and clarifies the transformation to 'nested structure'. This clearly differentiates it from the sibling tool 'flatten', which performs the inverse operation.

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 flat dot-notation objects but does not explicitly state when to choose this over 'flatten' or other conversion tools. Context suggests it is the inverse of 'flatten', but no explicit when/when-not guidance is given.

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 full burden. It discloses that the tool returns a type, size, or error, but leaves ambiguity about what these mean (e.g., JSON data type, byte size) and does not state side effects or prerequisites. This is minimal but non-contradictory.

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?

A single, concise sentence with no wasted words. The verb 'Validate' is front-loaded, and every word adds value. 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?

There is no output schema, so the description must explain return values. It mentions 'type, size, or error' but does not define or structure them, leaving an agent uncertain about the exact output format. Given the tool's simplicity, this is adequate but has clear gaps.

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% (the single 'data' parameter is described as 'JSON string to validate'), so the description adds no additional parameter-level meaning. Baseline 3 applies when schema covers parameters well, which is the case here.

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 ('Validate') with a clear resource ('JSON string') and states the output ('return type, size, or error'). This clearly distinguishes the tool from its sibling conversion/manipulation tools, leaving no ambiguity about its function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the usage context: validating JSON strings. It does not explicitly name alternatives or exclusions, but among the sibling tools, none perform validation, so the context is clear. Lacks explicit when-not-to-use guidance, but the uniqueness of the tool makes that less critical.

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