HydraMCP

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool returns 'the model's response with metadata', which hints at output but lacks detail on rate limits, authentication needs, error handling, or performance characteristics. For a tool that interacts with external AI models, this is a significant gap in transparency.

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 extremely concise—two sentences that directly state the tool's function and output. Every word earns its place with zero waste. It's front-loaded with the core purpose and efficiently communicates essential information without 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 the complexity of querying AI models (which involves external resources, potential costs, and varied behaviors), the description is insufficient. With no annotations, no output schema, and minimal behavioral context, it doesn't provide enough information for safe and effective use. The agent lacks critical details about what 'metadata' includes or how different models might behave.

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%, so the schema already documents all 6 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain relationships between parameters or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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: 'Query any AI model with a prompt' specifies the verb (query) and resource (AI model). It distinguishes from siblings like 'list_models' or 'compare_models' by focusing on execution rather than listing or comparison. However, it doesn't explicitly differentiate from 'synthesize' or 'consensus' which might also involve model queries.

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 no guidance on when to use this tool versus alternatives like 'compare_models' or 'consensus'. It mentions what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions. This leaves the agent without direction for tool selection among siblings.

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