list_leadsList a chatbot's captured leads (visitors who submitted an email or phone via the lead-capture form). Newest first, paginated 50 per page.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number (50 per page). | |
| chatbot_id | Yes | The chatbot id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the type of data (leads), ordering (newest first), and pagination (50 per page). This is sufficient for a list tool, though it could mention authentication or scope.
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 a single, well-structured sentence that front-loads the core purpose and includes key behavioral details. No wasted words.
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 simple list tool without an output schema, the description adequately covers what the tool does, what it returns, and how results are ordered/paginated. Given sibling tools and low complexity, it is complete.
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 coverage is 100% with descriptions for both parameters. The description adds context about pagination and ordering but does not provide additional semantics beyond what the schema already conveys. Baseline score of 3 is appropriate.
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 lists captured leads (visitors who submitted email or phone via lead-capture form), specifies ordering (newest first), and pagination (50 per page). This is a specific verb+resource that distinguishes it from sibling tools like list_inbox_threads or list_chatbots.
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 a chatbot's leads are needed, and the context of sibling tools (e.g., list_inbox_threads, list_chatbots) provides differentiation. However, it does not explicitly state when to use this tool vs alternatives or include exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action (sources, suggested messages, chatbots, inbox, leads, analytics, insights). There is no ambiguity between tool purposes.
All tool names follow a consistent verb_noun pattern (add_, get_, list_, recrawl_, update_). The naming convention is uniform and predictable.
15 tools is well-scoped for a chatbot manager. The set covers all key functional areas without being excessive or insufficient.
The tool surface is largely complete but lacks delete operations: there is no 'remove_suggested_message' (though referenced in a description) and no way to delete a source. These are notable gaps.