get_case_studyFull text of one project case study by slug (challenge / approach / impact). Call with an unknown slug to get the list of valid slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language of the returned content ('en' or 'zh'). | en |
| slug | Yes | Case study slug, e.g. "gisphere-llm". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral transparency burden. It discloses that an unknown slug returns a list of valid slugs, which is a valuable behavioral detail. The read-only nature is implied by 'Full text' and 'get,' but no explicit authorization or side-effect clarification is given.
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 long, front-loads the core purpose, and avoids redundancy with the schema. Every clause adds useful information, making it 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?
For a simple retrieval tool with no output schema, the description covers the return content, the identifier (slug), the language option (via schema), and the list-of-slugs fallback. This is sufficient for an agent to effectively select and invoke the 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?
Schema coverage is 100%, supplying baseline meaning for both 'slug' and 'lang'. The description adds extra semantics for 'slug' by explaining the unknown-slug behavior and the content structure, which goes beyond the schema's terse 'Case study slug' description.
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 this tool retrieves the full text of a single project case study identified by slug, and explicitly names the content sections (challenge / approach / impact). This distinguishes it from siblings like get_profile and list_experience, which cover different content types.
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 usage context, especially the edge-case instruction to call with an unknown slug to receive a list of valid slugs. It does not explicitly mention when to use this tool versus the sibling tools, but the purpose is distinct enough to infer appropriate use.
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 targets a distinct resource: case studies, profile, and CV. No overlap in purpose or functionality.
All tools follow a verb_noun pattern (get_* and list_*), which is consistent and predictable.
Three tools is slightly minimal but appropriate for a personal server covering profile, CV, and case studies.
Core personal information and case study retrieval are covered. The only minor gap is that case study listing relies on an invalid slug call rather than a dedicated list tool.