HydraMCP

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

With no annotations provided, the description carries full burden but only partially discloses behavior. It mentions the voting mechanism and output format (consensus answer with confidence score), but doesn't address critical aspects like rate limits, authentication needs, error handling, or what happens when consensus isn't reached. The description doesn't contradict annotations (none exist), but leaves significant behavioral gaps.

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 perfectly concise with two sentences that efficiently convey core functionality. Every word earns its place: the first sentence explains the query-aggregate mechanism, and the second specifies the return format. No redundant information or unnecessary elaboration.

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 7 parameters, no annotations, and no output schema, the description is incomplete. While it covers the high-level operation and output, it lacks details about behavioral constraints, parameter interactions, error conditions, and the format/meaning of the confidence score. For a complex tool with multiple configuration options, more context would be helpful.

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 57% (4 of 7 parameters have descriptions). The description adds minimal parameter semantics beyond the schema, only implying that 'models' are polled and 'strategy' determines agreement thresholds. It doesn't explain the purpose of judge_model, system_prompt, temperature, or max_tokens. With moderate schema coverage, the baseline is 3 as the description provides limited additional value.

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 ('query', 'aggregate', 'returns') and resources ('3-7 models', 'responses', 'consensus answer with confidence score'). It distinguishes from siblings by specifying the multi-model voting approach versus single-model queries (ask_model) or comparisons (compare_models).

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 usage context through 'aggregate responses using voting strategy', suggesting this tool is for multi-model consensus rather than single-model queries. However, it doesn't explicitly state when to use this versus alternatives like ask_model or compare_models, nor does it mention prerequisites or exclusions.

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