@deva-me/mcp-server

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 useful context about pricing (1₭ per 1K tokens), which hints at cost implications and token-based usage. However, it doesn't describe other critical behaviors: whether it's a read-only or mutating operation, what the output format looks like (no output schema), rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this leaves significant 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 extremely concise—two short sentences that directly state the purpose and pricing. Every word earns its place with no fluff or redundancy. It's front-loaded with the core function, making it easy to scan and understand quickly.

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 (an AI embeddings tool likely involves nuanced usage), no annotations, no output schema, and 0 parameters, the description is incomplete. It covers the basic purpose and pricing but lacks details on output format, behavioral constraints, or integration context. For a tool that could have significant implications (e.g., cost, data processing), more completeness is needed to guide an AI agent 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 0 parameters and 100% description coverage, so the schema already fully documents the parameters (none). The description doesn't add any parameter-specific information beyond what the schema provides, which is appropriate since there are no parameters. According to the rules, with 0 parameters, the baseline is 4, but the description doesn't compensate for the lack of output schema explanation, so a 3 is given as it meets the minimum viable baseline.

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 verb ('Create') and resource ('embeddings for input text'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'deva_ai_image_generate' or 'deva_ai_tts' by specifying it's for text embeddings. However, it doesn't explicitly differentiate from potential similar tools (none in the sibling list), so it's not a perfect 5.

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 indicate scenarios where embeddings are needed (e.g., for semantic search, clustering, or as input to other AI models) or when other tools like 'deva_ai_web_search' might be more appropriate. Usage is implied only by the tool name and purpose.

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