BBQ MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds valuable context beyond annotations: it explains the purpose of resting ('allows juices to redistribute and temperature to equalize') and specifies what the tool provides ('rest time recommendations and predicts final temperature after carryover'). This enhances understanding without contradicting 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?

The description is front-loaded with the core purpose in the first sentence. Each subsequent section (explanation, args, examples) is concise and directly relevant. There is no wasted text; every sentence earns its place by clarifying usage or providing practical examples.

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 moderate complexity (4 parameters, no output schema), the description is reasonably complete. It covers purpose, usage context, and examples. However, it lacks details on output format specifics or error handling, which could be useful since there's no output schema. Annotations provide good behavioral coverage, but the description could add more on result interpretation.

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 100%, with each parameter well-documented in the schema (e.g., protein_type enum values, current_temp range). The description adds minimal param semantics: it lists args and provides examples that illustrate usage but does not explain parameter interactions or constraints beyond what the schema already provides. Baseline 3 is appropriate given high 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 explicitly states the tool's purpose: 'Calculate recommended rest time and expected carryover cooking.' It uses specific verbs ('calculate,' 'provides,' 'predicts') and clearly identifies the resource (rest time and temperature predictions). It distinguishes from siblings like bbq_estimate_cook_time or bbq_get_target_temperature by focusing on post-cooking resting rather than cooking itself.

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 context for when to use the tool: 'Resting allows juices to redistribute and temperature to equalize.' The examples illustrate typical scenarios (e.g., 'Brisket is at 200°F, how long to rest?'). However, it does not explicitly state when NOT to use it or name alternatives among siblings, such as bbq_analyze_temperature for real-time analysis.

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