list_graphs

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by explaining its role as the 'main tool for getting information about existing graphs' and detailing return data structure, including nested nodes. However, it lacks information on potential side effects, rate limits, or error handling, which are important for a tool with no annotations, preventing a perfect 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 with clear sections (e.g., Use cases, Usage recommendations, Return data), making it easy to scan. It is appropriately sized for the tool's complexity, but some redundancy exists (e.g., repeating filtering concepts in multiple sections), which slightly reduces efficiency. Overall, it's front-loaded with key information and avoids unnecessary 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 (listing with filtering), lack of annotations, and no output schema, the description provides comprehensive context. It covers purpose, usage guidelines, behavioral aspects, and detailed return data, compensating for the absence of structured output schema. This makes it complete enough for an AI agent to understand and invoke the tool correctly without gaps.

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 input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the purpose of filtering ('Filter graphs by status, such as viewing all drafts or published graphs') and linking parameters to use cases, which enhances understanding beyond the schema's enum descriptions. However, it doesn't provide additional syntax or format details, so it doesn't fully maximize the potential for parameter semantics.

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 explicitly states the tool's purpose as 'List all knowledge graphs with support for filtering by status and type,' which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'create_graph' or 'get_node_details' by focusing on listing existing graphs rather than creating new ones or querying specific nodes. The description clearly articulates what the tool does without being tautological.

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 usage recommendations, including when to use this tool (e.g., 'First call this tool to get the graph list and node information') and how it integrates with sibling tools (e.g., 'Use these IDs to call other tools like add_edge, get_node_details'). It also offers context on when to use it, such as confirming a graph's status before operations, making it clear when this tool is appropriate versus alternatives.

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