Skip to main content
Glama
folderr-tech

Folderr

Official
by folderr-tech

ask_assistant

Ask questions to a specific Folderr assistant using its ID to get targeted responses for your queries.

Instructions

Ask a question to a specific assistant

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assistant_idYesID of the assistant to ask
questionYesQuestion to ask the assistant

Implementation Reference

  • The main handler function for the 'ask_assistant' tool. Checks authentication, POSTs question to /api/agent/{assistant_id}/message, returns JSON response or error.
    private async handleAskAssistant(args: any) {
      if (!this.config.token) {
        throw new McpError(ErrorCode.InvalidRequest, 'Not logged in');
      }
    
      try {
        const response = await this.axiosInstance.post(
          `/api/agent/${args.assistant_id}/message`,
          { message: args.question }
        );
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(response.data, null, 2),
            },
          ],
        };
      } catch (error: any) {
        return {
          content: [
            {
              type: 'text',
              text: `Failed to ask assistant: ${error.response?.data?.message || error.message}`,
            },
          ],
          isError: true,
        };
      }
    }
  • Input schema defining required 'assistant_id' (string) and 'question' (string) for the tool.
    inputSchema: {
      type: 'object',
      properties: {
        assistant_id: {
          type: 'string',
          description: 'ID of the assistant to ask',
        },
        question: {
          type: 'string',
          description: 'Question to ask the assistant',
        },
      },
      required: ['assistant_id', 'question'],
    },
  • src/index.ts:152-169 (registration)
    Tool registration object added to the MCP server's tools list, with name, description, and input schema.
    {
      name: 'ask_assistant',
      description: 'Ask a question to a specific assistant',
      inputSchema: {
        type: 'object',
        properties: {
          assistant_id: {
            type: 'string',
            description: 'ID of the assistant to ask',
          },
          question: {
            type: 'string',
            description: 'Question to ask the assistant',
          },
        },
        required: ['assistant_id', 'question'],
      },
    },
  • Dispatch case in the CallToolRequest handler that routes 'ask_assistant' to its handler function.
    case 'ask_assistant':
      return await this.handleAskAssistant(request.params.arguments);
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('ask') but doesn't reveal key traits: whether this is a read-only or mutating operation, if it requires authentication, what the response format might be, or any rate limits. The description is minimal and lacks essential context for safe and effective use.

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?

The description is extremely concise—a single, clear sentence that directly states the tool's function without any fluff. It's front-loaded and wastes no words, making it easy to parse quickly. This efficiency is commendable, though it comes at the cost of depth in other dimensions.

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

Completeness2/5

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

Given the complexity of interacting with an assistant (which could involve queries, responses, or mutations), the description is incomplete. With no annotations and no output schema, it fails to explain what the tool returns or its behavioral implications. The description alone is insufficient for an agent to understand the full context of use, especially compared to siblings like 'execute_workflow'.

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

Parameters3/5

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

The input schema has 100% description coverage, clearly documenting both parameters ('assistant_id' and 'question'). The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or usage examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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

Purpose3/5

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

The description 'Ask a question to a specific assistant' clearly states the action (ask) and target (assistant), but it's vague about what 'ask' entails—whether it's a conversational query, a command, or something else. It doesn't distinguish this tool from potential siblings like 'list_assistants', which is a different operation, but the purpose is understandable though not highly specific.

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?

The description provides no guidance on when to use this tool versus alternatives. For example, it doesn't specify if this is for general queries, debugging, or if other tools like 'execute_workflow' might be better for certain tasks. There's no mention of prerequisites, such as needing an assistant ID from 'list_assistants', leaving usage context implied at best.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/folderr-tech/folderr-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server