list_case_studiesList Cymosoft's client project case studies (industry, engagement model, team size, tech stack). Use get_case_study for the full write-up.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Response language (default: en) |
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.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: two text conversions (fullwidth/halfwidth and Japanese era), one character counter, and three company-related info tools (list/get case studies, get company profile). No overlap or ambiguity between tools.
All tool names follow a consistent verb_noun pattern in snake_case: convert_*, count_*, list_*, get_*. The verbs are appropriate for each action, and the pattern is predictable across the set.
With 6 tools, the server is well-scoped for its niche purpose of Japanese text utilities and company information. Each tool earns its place without redundancy or unnecessary expansion.
The tool surface covers common Japanese text operations (conversion and counting) and provides read-only access to case studies and company profile. Minor gaps exist (e.g., no hiragana/katakana conversion or contact details), but core workflows are covered without dead ends.