MAGG

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 effectively describes the workflow steps (collect metadata, use LLM sampling, add server), clarifies the dependency on LLM context for optimal configuration, and explains the fallback behavior to heuristics. However, it doesn't mention potential side effects like configuration changes or system impacts.

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 front-loaded with the core purpose, followed by bullet points for the workflow and notes on requirements and alternatives. Every sentence adds value without redundancy, 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 (involving LLM sampling and configuration workflows), the description is complete enough. It explains the process, dependencies, and alternatives clearly. With an output schema present, it doesn't need to detail return values, and the 100% schema coverage handles parameter documentation adequately.

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%, providing full documentation of all three parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 without compensating for any gaps.

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 ('configure and add a server from a URL') and distinguishes it from siblings by mentioning the LLM sampling approach. It explicitly differentiates from 'configure_server_prompt' for generating prompts without sampling.

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 explicit guidance on when to use this tool versus alternatives: it specifies that LLM context is required for intelligent configuration, and without it, it falls back to heuristics. It also names the alternative tool 'configure_server_prompt' for generating configuration prompts without sampling.

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