Cymosoft

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 does an above-average job by explicitly scoping the conversion to ASCII letters, digits, punctuation, and spaces, which tells the agent how characters outside these classes (e.g., kana) would be handled. However, it does not explicitly state that non-target characters remain unchanged, nor describe any edge cases like handling of already-converted characters, which would add further 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, succinct sentence that is front-loaded with the verb 'Convert'. Every part adds value: it states the operation, the object, and the character scope. There is no redundancy or filler, 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?

For a simple transformation tool with only two clearly named parameters, no output schema, and no annotations, the description covers the core requirements: what it converts, into what, and for which character classes. It could mention that the output is the converted text, but that is implied. It does not need to elaborate on return values since there is no output schema, and the behavior is deterministic. Overall, it is complete enough for low-complexity 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 0%, so the description must compensate for missing parameter documentation. It fails to explicitly explain the 'text' and 'direction' parameters. Although the enum values 'to_halfwidth' and 'to_fullwidth' are self-explanatory, the description does not confirm that 'text' is the input string and 'direction' selects the conversion direction. This is a clear gap given the low 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 uses a specific verb 'Convert' and names the exact resource ('Japanese text between fullwidth and halfwidth forms'), clearly distinguishing this tool from siblings like era conversion or character counting. It also specifies the character classes covered (ASCII letters, digits, punctuation, spaces), leaving no ambiguity about what the tool does.

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

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

The description clearly implies when to use the tool: whenever conversion between fullwidth and halfwidth forms is needed. While it does not explicitly mention alternatives or exclusions, the sibling tools are sufficiently different (era conversion, character counting, case studies) that the intended use case is unambiguous. No alternative is named, but none is necessary given the unique function.

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. It discloses the two operating modes and their input requirements, which is significant behavioral information for a pure conversion tool. It doesn't detail edge cases or output format, but the core behavior is well 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?

Two sentences, front-loaded with the primary verb and subject, followed by precise usage instructions. No redundant words or phrases.

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 converter, the description covers the essential purpose, inputs, and modes. The lack of an output schema means the description could ideally specify the return format, but the conversion output is generally inferable. Overall, it is nearly 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?

Schema coverage is 100%, but the description adds crucial combinatorial semantics: it specifies which parameters should be used together (year alone vs. era+era_year) for each direction. This goes beyond the isolated parameter descriptions and clarifies the intended usage patterns.

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: converting between Japanese era years and Western calendar years, and lists the supported eras. It is distinct from sibling tools, which handle character width conversion and case studies.

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 gives explicit input instructions for each conversion direction (year for 西暦→和暦, era+era_year for 和暦→西暦), providing clear context for use. It doesn't mention exclusions or alternatives, but the sibling tools are unrelated, so no ambiguity exists.

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 counting behavior (total, excluding whitespace, pages) but does not explicitly mention that it is read-only or has no side effects. Since it is a counting tool, this is mostly inferable, but some edge cases (e.g., whitespace definition) are not addressed.

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 well-structured sentence that front-loads the core purpose and enumerates the output types efficiently. No wasted words or repetition of schema details.

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 (one parameter, no output schema, no annotations), the description explains the key return values (total, excluding whitespace, pages). It is mostly complete but could benefit from clarifying what counts as whitespace or how 'pages' is formatted, though the 400 chars/page note helps.

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 has zero description coverage and only a single 'text' property. Although the description does not mention the parameter by name, it refers to 'text' directly, making clear the input is the string to be counted. This adds essential meaning beyond the bare schema 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 clearly states the tool counts characters in text and specifies three distinct counting modes: total, excluding whitespace, and manuscript pages. The verb 'count' and resource 'Japanese (or any) text' are explicit, and it is easily distinguished from sibling tools which handle conversion or lookup tasks.

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 sets clear context by noting it works on Japanese or any text, implying when to use it. However, it does not explicitly mention when not to use it or name alternative tools, though the sibling tools are clearly different in purpose.

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 states the output is 'full case study text' and that ids come from list_case_studies, which are helpful behavioral hints. However, it does not mention the language parameter's effect on the response or any other potential nuances, though the schema partially covers this. The description is not misleading but lacks depth.

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 core function and includes the most useful usage hint. Every word earns its place; no filler 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?

For a simple get-by-id tool with a full input schema and no output schema, the description adequately conveys the core purpose and id source. It could be slightly more complete by mentioning the language parameter's default, but that is covered in the schema. Overall, it is sufficiently complete for the tool's complexity.

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 covers both parameters (id and language) with descriptions, so the baseline is 3. The description adds value by explaining the provenance of the id parameter ('ids come from list_case_studies'), which helps the agent understand valid values. This extra semantic context justifies a point above baseline.

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 indicates the resource (case study) and the access method (by id), and it distinguishes itself from list_case_studies by specifying that it returns the full text. It lacks an explicit verb like 'returns' or 'retrieves', but the tool name and phrase 'Full case study text' make 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 explicitly states that ids come from list_case_studies, giving the agent a clear prerequisite and directing it to a sibling tool for finding valid ids. It does not explicitly state exclusions or alternatives, but the hint is valuable and sufficient for this simple read operation.

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 disclaims the full burden. It only provides company background and does not disclose behavior such as return format, language effect, or whether it is a read-only operation.

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?

One concise sentence with no wasted words. It is front-loaded with the core purpose and includes relevant company context.

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?

With no output schema, the description should explain what the agent will receive. It only describes the company, leaving the return value and behavior unspecified. The description is more of a company bio than a tool specification.

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 a detailed enum for the language parameter. The description adds no extra meaning beyond the schema, so 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 title uses 'Get' and the description clearly identifies the resource (Cymosoft company profile). It is distinct from sibling tools (conversion and case study tools), though the description itself lacks an explicit verb.

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?

Usage is implied by the tool name and context, but there is no explicit when-to-use guidance or mention of alternatives. Since siblings are clearly unrelated, the intended use is reasonably obvious.

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 behavioral burden. It indicates a read-only listing operation but does not disclose details such as response format, ordering, or language behavior. Still, for a simple list tool, the core behavior is conveyed adequately.

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 sentence, front-loaded with the main purpose, and the pointer to get_case_study adds value without bloat. 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?

For a simple list tool with one optional parameter and no output schema, the description provides sufficient context: it names the resource and the attributes included. It could mention return format or pagination, but those are implied by 'List' and are not critical for such a tool.

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 already describes the single 'language' parameter fully (enum, default), and the description adds no extra param semantics. Baseline of 3 is appropriate given 100% 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 identifies the tool as listing Cymosoft's client project case studies with a specific set of fields (industry, engagement model, team size, tech stack). It uses a specific verb ('List') and resource, and directly distinguishes itself from the sibling tool get_case_study.

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 points to get_case_study for the full write-up, which clarifies when to use this list tool versus the alternative. This provides clear usage guidance.

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