The three published case studies with real, verifiable outcomes: Smarter Services (1.5 days of admin freed weekly), 1NCE (multilingual AI support assistant), Slimstock (AI chat on their own content).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Optional: return just this case study; one of "smarter-services", "1nce", "slimstock" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose that the tool is read-only, idempotent, and non-destructive. The description adds useful content scope (three specific case studies with verifiable outcomes), but does not reveal additional behavioral details such as response shape or slug-omission behavior. It does not contradict the 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?
A single, front-loaded sentence that efficiently names the resource and enumerates the available case studies in parentheses. There is no filler or redundant repetition of schema 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?
This is a simple read-only tool with no required parameters, so the annotations and schema cover most of the contract. The description names all available items and their focus areas, though it does not explicitly describe the response structure or confirm behavior when no slug is provided.
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 has full description coverage for the single optional 'slug' parameter, listing the three valid values. The description adds no new parameter semantics, 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 tool name 'get_case_studies' combined with the description clearly identifies the resource: the three published case studies. The description lists each case study and its outcome, distinguishing this tool from siblings like list_services and get_contact_info.
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 no explicit guidance on when to use this tool versus alternatives, no exclusions, and no mention of sibling tools. The intended usage is only implied by the tool name and the case-study content.
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: retrieving assessment questions, scoring assessment answers, listing services, retrieving case studies, searching resources, and providing contact info. The only related pair—get_ai_readiness_questions and score_ai_readiness—are complementary rather than overlapping, with one delivering the questions and the other processing the answers.
All tool names follow a consistent snake_case verb_noun pattern: get, list, score, and search. The naming style is uniform and predictable, making it easy for an agent to infer behavior from the tool name alone.
Six tools is a well-scoped size for a company information and readiness-assessment server. Each tool contributes a distinct function with no redundancy, and the count is comfortably within the ideal range.
The tool surface covers the server's apparent purpose comprehensively: services, case studies, resources, contact information, and a complete AI readiness assessment flow. The explicit note that the server has no write tools appropriately sets expectations, and users can obtain URLs/markdown for deeper content.