List the 13 services of Shipshape Data (a London AI consultancy): AI strategy, data consultancy, AI development, data migration, and nine specialisms including MCP connections and RAG. Returns names, descriptions, and page URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Optional: return just this service, e.g. "ai-strategy" or "mcp-connections" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds useful non-obvious details: the exact service count, the inclusion of MCP connections and RAG, and that the result includes names, descriptions, and page URLs. This meaningfully exceeds annotation-only context.
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 concise sentences with no filler. The action, scope, examples, and return content are all front-loaded and every phrase 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, read-only list tool with one optional parameter and strong annotations, the description is sufficiently complete. It names the return fields and the optional filter behavior. The absence of an explicit output shape is a minor gap, but acceptable given no output schema exists.
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 slug parameter is fully documented in the input schema with an example, so the description carries little parameter burden. The list of service categories provides hints about valid values, but the schema already supplies the core semantics.
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 lists Shipshape Data's 13 services, identifies the verb and resource precisely, and even previews service categories and return fields. It does not explicitly differentiate from sibling tools like search_resources, so it stops short of a 5.
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 this is the canonical way to list the company's services but provides no explicit when-to-use guidance or alternatives among siblings. An agent can infer usage from the catalog framing, but it is not directly stated.
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.