BBQ MCP Server

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

The description adds valuable behavioral context beyond annotations: it specifies the authentication prerequisite, describes the combination of real-time data with knowledge base, and outlines the return content. Annotations already cover safety (readOnlyHint=true, destructiveHint=false), so the bar is lower, but the description provides useful operational context 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 well-structured with clear sections (purpose, prerequisites, args, returns, examples) and front-loads the core functionality. It could be slightly more concise by removing redundant default values already in schema, but every sentence adds value for agent understanding.

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 (5 parameters, no output schema), the description provides good context: purpose, prerequisites, parameter overview, return content, and usage examples. It doesn't fully explain the 'analyze' algorithm or knowledge base details, but with annotations covering safety and the schema documenting parameters well, it's sufficiently complete for agent use.

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?

With 100% schema description coverage, the input schema already documents all parameters thoroughly. The description adds minimal additional semantics (e.g., 'default: '1'' for probe_id is redundant with schema, protein examples are helpful but not essential). It meets the baseline for high schema coverage without significantly enhancing parameter understanding.

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's purpose with specific verbs ('Get live temperature', 'analyze cooking progress') and resources ('connected ThermoWorks device', 'BBQ cooking knowledge base'). It distinguishes from siblings by combining real-time device data with analysis, unlike simpler tools like thermoworks_get_live_readings or generic bbq_analyze_temperature.

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 states when to use this tool ('Requires authentication first via thermoworks_authenticate') and provides clear examples showing context ('How's my brisket doing?', 'Check the turkey on probe 2'). It implicitly distinguishes from siblings by emphasizing live data analysis with protein-specific recommendations.

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