MCP HydroCoder Vision

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds context about using a local model ('Qwen3 VL 4B model via LM Studio'), which hints at offline processing and potential performance or capability limits. However, it lacks details on error handling, rate limits, authentication needs, or output format, which are important for a tool with no output schema. The description doesn't contradict annotations, but it's insufficiently detailed for a tool with no annotation coverage.

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 concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds useful context about the model. Both sentences earn their place, but it could be slightly more structured by explicitly separating purpose from implementation details.

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 complexity (image analysis with a specific model), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values (e.g., format of the 'detailed description'), error conditions, or limitations of the local model. This leaves significant gaps for an AI agent to use the tool effectively.

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 input schema has 100% description coverage, clearly documenting both parameters ('imagePath' and 'prompt'). The description adds no additional parameter semantics beyond what's in the schema, such as file format support for 'imagePath' or examples for 'prompt'. According to the rules, with high schema coverage (>80%), the baseline is 3 even without param info in the description.

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: 'Analyze an image and return a detailed description.' It specifies the verb ('analyze'), resource ('image'), and output ('detailed description'). However, it doesn't explicitly differentiate from sibling tools like 'describeForCode' or 'extractText' beyond mentioning the specific model used, which is relevant but not a clear functional distinction.

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. It mentions the model ('Qwen3 VL 4B model via LM Studio'), which implies local processing, but doesn't specify use cases, prerequisites, or exclusions compared to siblings like 'describeForCode' or 'extractText'. This leaves the agent without clear decision-making criteria.

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