netintel_v1_embeddings

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

With no annotations, the description carries the full burden and does disclose real operational traits: batch ceiling (128 strings), total character budgets per model, the default model, and flat pricing ('$0.005 per…'). It omits auth/rate-limit behavior and the response shape, and the text is cut off mid-price, leaving some 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?

One front-loaded sentence that leads with purpose before the protocol and limits, with no filler. The em-dash chain is dense and the text terminates mid-sentence ('Flat $0.005 per…'), which hurts polish but not front-loading.

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?

For a 3-parameter tool with zero annotation coverage and no output schema, the description covers input and model constraints and cost but leaves the 'dimensions' parameter and the returned embedding structure unexplained. Adequate but with clear gaps.

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 0%, so the description must compensate. It does document 'input' well (string or batch up to 128, 64000 chars on text-embedding-3-small, 24000 on text-embedding-3-large) and implies 'model' values, but 'dimensions' is never mentioned and the truncation may hide more.

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?

States a specific verb+resource ('text embeddings API') and pins the protocol shape ('standard /v1/embeddings request shape'), so an agent knows exactly what it produces. It does not name any sibling (e.g. netintel_semantic_rank or netintel_v1_chat_completions) to disambiguate, so it stops short of a 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 explains the request shape and limits but never says when to choose embeddings over alternatives such as netintel_semantic_rank or netintel_v1_chat_completions, nor any when-not condition. Usage must be inferred entirely from the tool name.

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