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PhononX

Carbon Voice

by PhononX

get_ai_action_responses

Read-only

Retrieve AI-generated responses by filtering for specific prompts, messages, or conversations to review previous interactions and outputs.

Instructions

Retrieve previously generated AI Action (Prompt) responses by filtering for a specific prompt, message, or conversation ID. Combine filters to narrow results and view all AI-generated responses related to a particular prompt, message, or conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
message_idNo
prompt_idNo
channel_idNo
limitNo
directionNo
dateNo

Implementation Reference

  • The inline handler function that executes the 'get_ai_action_responses' tool logic by calling the Carbon Voice simplified API to retrieve AI action responses based on provided parameters.
    async (
      args: AIResponseControllerGetAllResponsesParams,
      { authInfo },
    ): Promise<McpToolResponse> => {
      try {
        return formatToMCPToolResponse(
          await simplifiedApi.aIResponseControllerGetAllResponses(
            args,
            setCarbonVoiceAuthHeader(authInfo?.token),
          ),
        );
      } catch (error) {
        logger.error('Error getting ai action responses:', { error });
        return formatToMCPToolResponse(error);
      }
    },
  • TypeScript type definition for the input parameters of the 'get_ai_action_responses' tool, used for validation and typing.
    export type AIResponseControllerGetAllResponsesParams = {
      message_id?: string;
      prompt_id?: string;
      channel_id?: string;
      limit?: number;
      direction?: AIResponseControllerGetAllResponsesDirection;
      date?: string;
    };
  • src/server.ts:917-928 (registration)
    MCP server registration of the 'get_ai_action_responses' tool, including description, input schema reference, and annotations.
    server.registerTool(
      'get_ai_action_responses',
      {
        description:
          'Retrieve previously generated AI Action (Prompt) responses by filtering for a specific prompt, message, or conversation ID. ' +
          'Combine filters to narrow results and view all AI-generated responses related to a particular prompt, message, or conversation.',
        inputSchema: aIResponseControllerGetAllResponsesQueryParams.shape,
        annotations: {
          readOnlyHint: true,
          destructiveHint: false,
        },
      },
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds useful context about filtering capabilities and the scope of retrieval (previously generated responses), but doesn't disclose behavioral traits like rate limits, authentication needs, pagination behavior, or response format details beyond what annotations provide.

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

Conciseness4/5

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

The description is appropriately sized with two sentences that are front-loaded with the main purpose. The first sentence establishes the core functionality, and the second adds useful detail about filtering combinations. There's minimal waste, though it could be slightly more structured with bullet points for clarity.

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?

Given the tool's moderate complexity (6 parameters, no output schema) and annotations covering safety, the description provides a basic understanding of purpose and filtering. However, with 0% schema coverage and no output schema, it lacks sufficient detail on parameters and return values. The description is adequate but has clear gaps in parameter explanation and behavioral context.

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

Parameters2/5

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

Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions filtering for 'prompt, message, or conversation ID' and 'combine filters to narrow results', which hints at parameters like message_id, prompt_id, and possibly channel_id, but doesn't explain the 6 parameters (including limit, direction, date) or their semantics. The description adds some value but doesn't adequately compensate for the complete lack of schema documentation.

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?

The description clearly states the tool retrieves previously generated AI Action responses by filtering for specific IDs (prompt, message, or conversation). It uses the verb 'retrieve' with the resource 'AI Action responses' and mentions filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'list_ai_actions' or 'get_message', which could provide similar functionality.

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

Usage Guidelines3/5

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

The description implies usage context by mentioning filtering to narrow results and view responses related to prompts, messages, or conversations. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_ai_actions' or 'get_message', nor does it provide exclusions or prerequisites for usage.

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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