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list_forms

Retrieve all signup forms from the Keila newsletter platform to manage subscriber collection methods.

Instructions

List all signup forms.

Returns: A dict with 'data' (list of forms).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The implementation of list_forms within the KeilaClient class that performs the actual API call.
    def list_forms(self) -> dict:
        """List all forms."""
        resp = self.session.get(f"{self.url}/api/v1/forms", headers=self._headers(), timeout=30)
        resp.raise_for_status()
        return resp.json()
  • mcp_server.py:280-288 (registration)
    The MCP tool registration and handler wrapper for list_forms.
    @mcp.tool()
    def list_forms() -> dict:
        """
        List all signup forms.
    
        Returns:
            A dict with 'data' (list of forms).
        """
        return _client.list_forms()
Behavior3/5

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

Documents return structure ('dict with data') necessary since no output schema exists, but lacks critical behavioral context: pagination behavior, rate limits, and read-only safety confirmation (no annotations provided).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero waste. Front-loaded with purpose statement followed by return format, appropriate for a zero-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Return structure is documented compensating for missing output schema, but lacks pagination guidance and domain-specific context expected for a list operation in an email marketing API (evidenced by campaign/contact siblings).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters with 100% schema coverage. Description correctly adds no parameter details (none exist), meeting baseline expectations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb ('List') and specific resource ('signup forms'). Differentiates from siblings by resource type (campaigns, contacts, segments), though could better define what a 'signup form' is in this marketing context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides no guidance on when to use this versus sibling list operations (list_campaigns, list_contacts) or whether pagination/filtering is needed for large result sets.

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

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