ontomics

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 mentions that the tool 'uses logic embeddings' and 'returns similarity scores,' which adds useful context about the method and output. However, it doesn't address potential limitations, such as accuracy of embeddings, computational cost, or how similarity is calculated, leaving gaps in behavioral understanding.

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 concise and well-structured: it starts with the core purpose, explains the method, states the output, and provides usage examples. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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 (behavioral similarity analysis) and lack of annotations or output schema, the description does a good job of covering purpose, method, and usage. However, it doesn't fully explain the output format (e.g., structure of similarity scores) or potential edge cases, which could be important for an agent to interpret results 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 both parameters ('name' and 'top_k') with descriptions. The description adds no additional parameter semantics beyond what the schema provides, such as format examples for 'name' or constraints for 'top_k.' The baseline score of 3 is appropriate when the schema handles parameter documentation effectively.

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: 'Find entities with similar behavioral patterns — uses logic embeddings to find functions/classes that do similar things regardless of naming.' It specifies the verb ('find'), resource ('entities'), method ('logic embeddings'), and scope ('similar behavioral patterns'), distinguishing it from naming-based tools like 'check_naming' or 'suggest_name'.

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 explicitly states when to use this tool: 'Use when asked 'what other functions do the same thing', 'find similar implementations', or 'what behaves like X'.' It provides concrete examples of user queries that should trigger this tool, offering clear guidance on its application context.

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