add_buffer

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

With no annotations and a terse description, the tool's behavioral traits are unknown. It implies mutation ('add') but provides no details on side effects, permissions, or what happens to existing elements.

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 short (4 words), but this brevity comes at the cost of clarity. It is under-specified rather than genuinely concise, scoring 2.

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 (many siblings, output schema present), the description is grossly incomplete. It lacks any context about the output, prerequisites, or how the buffer integrates into the network.

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 sole parameter 'name' has no description in the schema (0% coverage) and the tool description adds no meaning. The agent has no information on what the name represents or how to choose it.

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 'Add buffer pattern' clearly identifies the action (add) and the object (buffer pattern), but fails to explain what a buffer pattern is in this modeling context. It is minimally sufficient but vague, scoring 3.

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?

No guidelines provided on when to use this tool versus its many siblings (e.g., add_arc, add_machine). This absence makes it difficult for an AI agent to select the correct tool without trial and error.

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