@deva-me/mcp-server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds pricing information ('20₭ ($0.02) per image'), which hints at cost implications, but fails to describe critical traits such as rate limits, authentication needs, output format, or error handling. For a tool with zero annotation coverage, 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 highly concise and front-loaded: the first sentence states the core purpose, and the second adds pricing as supplementary context. Every sentence earns its place with no wasted words, making it efficient and easy to parse.

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 (vision analysis with potential behavioral nuances), lack of annotations, and no output schema, the description is incomplete. It covers purpose and pricing but omits essential details like response format, error cases, or usage constraints, leaving the agent under-informed for effective tool invocation.

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 0 parameters with 100% description coverage, so the schema fully documents the payload. The description adds no parameter-specific information beyond implying image/video input, which is already covered by the tool's purpose. Baseline 3 is appropriate as the schema handles all parameter semantics, but the description doesn't compensate for any gaps (none exist).

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 image/video content using vision models.' It specifies the verb ('analyze') and resource ('image/video content'), distinguishing it from siblings like 'deva_ai_image_generate' (creation) or 'deva_ai_embeddings' (text processing). However, it doesn't explicitly differentiate from potential vision-related siblings, though none are listed, keeping it from a perfect score.

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 pricing but doesn't specify use cases, prerequisites, or comparisons to other tools like 'deva_ai_web_search' for visual queries. This lack of contextual direction leaves the agent without clear usage cues.

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