MCP-Saptiva

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 the tool's behavior ('Shows the model's chain-of-thought reasoning process along with the final answer'), which is not covered by the schema. However, it lacks details on potential limitations, error handling, or performance traits (e.g., latency, rate limits), leaving gaps 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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without wasted words. Every sentence earns its place by providing essential information, making it highly concise and well-structured.

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 (reasoning tasks with multiple parameters) and the absence of annotations and output schema, the description is moderately complete. It covers the core functionality and use cases but lacks details on output format, error conditions, or advanced behavioral traits, which would be helpful for an AI agent to invoke it correctly.

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 parameters thoroughly. The description does not add any meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or provide examples). Baseline 3 is appropriate as the schema handles the heavy lifting, but no extra value is added.

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 with specific verbs ('Use Saptiva Cortex for complex reasoning tasks') and distinguishes it from siblings by specifying its unique function (reasoning with chain-of-thought) versus embedding, chat, OCR, etc. It explicitly mentions the resource (Saptiva Cortex) and the action (reasoning tasks).

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 clear context on when to use this tool ('Best for math, logic, analysis, and multi-step problems'), which helps differentiate it from alternatives like saptiva_chat or saptiva_embed. However, it does not explicitly state when NOT to use it or name specific sibling tools as alternatives, keeping it from a perfect score.

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