list_apps

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format ('app names, descriptions, and action summaries'), which is useful, but lacks details on potential limitations like pagination, rate limits, or authentication requirements. However, it does not contradict any annotations, and for a read-only list tool with zero parameters, this level of transparency is adequate but not comprehensive.

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 front-loaded with the core purpose in the first sentence, followed by usage guidance and examples, with no wasted words. Every sentence adds value, such as clarifying the return format and providing practical examples, making it efficient and well-structured for quick understanding.

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 simplicity (0 parameters, no output schema, no annotations), the description is mostly complete. It explains what the tool does, when to use it, and what it returns. However, it could improve by mentioning any behavioral traits like response format details or potential errors, but for a basic list tool, this is sufficient to guide an AI agent effectively.

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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the inputs. The description does not need to add parameter information, and it appropriately focuses on the tool's purpose and usage. A baseline of 4 is applied as it compensates adequately for the lack of parameters by providing clear context.

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 ('List all available apps/integrations') and resources ('in Agentled'), and distinguishes it from siblings like 'get_app_actions' by focusing on discovery rather than detailed action information. It explicitly mentions what is returned ('app names, descriptions, and action summaries'), making the purpose highly specific and well-defined.

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 ('Use this to discover what integrations are available before building a workflow'), which clearly differentiates it from other tools like 'create_workflow' or 'list_workflows'. It also lists common apps as examples, helping users understand the context and scope of its application without ambiguity.

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