list-owned-botsList owned and shared bots for the logged-in user via POST /v1/get-bots.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes | JSON or plain text body returned by the Botsify HTTP API |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey readOnly, idempotent, and non-destructive behavior, so the description does not need to repeat that. It adds value by clarifying that the result includes both owned and shared bots, which is a subtle but important behavioral detail, and by exposing the underlying POST endpoint.
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, front-loaded sentence that efficiently packs the action, resource, scope, and endpoint without unnecessary words. It is a model of conciseness.
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 simplicity of the tool (no parameters, an output schema exists, and annotations cover safety), the description is fully sufficient. It names the endpoint and scope, making it complete for an agent to invoke 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?
The tool has zero parameters, so the baseline for parameterless tools is 4. The description correctly adds no parameter information, and the empty schema confirms there is nothing to explain.
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 action (List), the resource (bots), and the scope (owned and shared for the logged-in user), and even specifies the HTTP endpoint (POST /v1/get-bots). This distinguishes it from sibling tools like list-workspace-agents by focusing on user-level bots.
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?
It provides clear context by specifying 'for the logged-in user', which helps an agent decide when to use it. However, it does not explicitly compare against alternatives like list-workspace-agents or get-bot-details, nor does it mention exclusions or when-not-to-use conditions.
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.
Several tools have overlapping purposes, such as send-converse-message, send-inbox-message, send-user-message, and stream-user-message, which all deliver messages but with subtle differences. Similarly, list-bot-messenger-users and list-messenger-users both fetch messenger users, and start-builder-chat, clear-builder-conversation, and store-builder-response all manage builder chat state. Descriptions help, but the boundaries are still confusing.
Tool names consistently use hyphenated lowercase verb-noun format, but the verbs and nouns vary significantly in specificity. For example, 'get-query-response' vs 'query-mcp-agent' vs 'stream-user-message' all imply querying but with different styles. The pattern is readable but not highly predictable, with some names like 'change-user-activation' and 'patch-instruction-section' deviating from the simple verb_object structure.
With 44 tools, this server feels overloaded. The breadth covers bots, messaging, templates, whitelabel, and versions, but many tools could be consolidated (e.g., multiple message-sending variants). The count exceeds the 25-tool threshold for 'too many', making it difficult for agents to select the right tool without extensive context.
The tool set covers the main lifecycle: agent creation, versioning, deployment, deletion, messaging, conversation history, user management, template management, and whitelabel operations. Minor gaps exist, such as missing delete for WhatsApp templates or update operations for user attributes, but these are workable edge cases. Core workflows are well supported.