get_interaction_partners

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return format (list with confidence scores) which is helpful, but doesn't address important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or how results are ordered/paginated. For a tool with 5 parameters and no annotations, this leaves significant gaps in understanding its behavior.

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?

Two concise sentences with zero waste. The first sentence states the core purpose, the second describes the return format. Every word earns its place, and the information is front-loaded with the most important details first.

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?

For a tool with 5 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and return format, but lacks behavioral details that would be crucial for an AI agent to use this tool effectively. The absence of output schema means the description should ideally provide more detail about the return structure, which it only does at a high level.

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 5 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'confidence scores' which relates to the required_score parameter, but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Get all STRING interaction partners'), the target resource ('for your proteins'), and the return format ('Returns a list of interacting proteins with confidence scores'). It distinguishes itself from siblings like get_network or get_ppi_enrichment by focusing specifically on retrieving interaction partners rather than broader network analysis or enrichment calculations.

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 implies usage when you need interaction partners for proteins, but provides no explicit guidance on when to use this tool versus alternatives like get_network (which might provide broader network context) or get_ppi_enrichment (which focuses on enrichment analysis). No exclusion criteria or prerequisites are mentioned.

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