axom_mcp_analyze

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

Annotations indicate read-only, non-destructive, and idempotent behavior, which the description does not contradict. The description adds valuable context beyond annotations by detailing analysis types, focus areas, depth levels, and chain support, providing insight into how the tool behaves and what users can expect from its output. It doesn't mention rate limits or specific auth needs, but with annotations covering safety, this is sufficient for a high score.

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 well-structured and appropriately sized, using bullet points and clear headings to organize information efficiently. Every sentence and section earns its place by adding specific value, such as defining analysis types and depth levels, without unnecessary repetition or fluff.

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 with multiple parameters and no output schema, the description does a good job of explaining what the tool does and how to configure it. It covers analysis types, focus areas, depth levels, and chain support, which helps users understand the tool's capabilities. However, without an output schema, it could benefit from more details on return values or result formats, slightly limiting completeness.

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 adds some semantic context by explaining the meaning of analysis types, focus areas, and depth levels, which complements the schema's enum descriptions. However, it doesn't provide additional syntax or format details beyond what the schema offers, resulting in a baseline score of 3.

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 as 'Analyze code and data with configurable depth and scope,' which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'discover,' 'exec,' 'memory,' and 'transform' by focusing exclusively on analysis rather than discovery, execution, memory operations, or transformations.

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 for usage through detailed breakdowns of analysis types, focus areas, and depth levels, helping users understand when to apply different configurations. However, it lacks explicit guidance on when to use this tool versus its siblings, such as distinguishing analysis from transformation or execution tasks, which prevents a perfect score.

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