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canvas_get_conversation

Retrieve detailed information about a specific conversation using its unique ID to manage and track communications within the Canvas Learning Management System.

Instructions

Get details of a specific conversation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversation_idYesID of the conversation

Implementation Reference

  • src/index.ts:660-669 (registration)
    Registration of the 'canvas_get_conversation' MCP tool, including its name, description, and input schema requiring a conversation_id.
      name: "canvas_get_conversation",
      description: "Get details of a specific conversation",
      inputSchema: {
        type: "object",
        properties: {
          conversation_id: { type: "number", description: "ID of the conversation" }
        },
        required: ["conversation_id"]
      }
    },
  • Handler function that executes the core logic of fetching the conversation details from the Canvas API using the provided conversation ID.
    async getConversation(conversationId: number): Promise<CanvasConversation> {
      const response = await this.client.get(`/conversations/${conversationId}`);
      return response.data;
    }
  • TypeScript interface defining the structure of a CanvasConversation object returned by the tool.
    export interface CanvasConversation {
      id: number;
      subject: string;
      workflow_state: 'read' | 'unread' | 'archived';
      last_message: string;
      last_message_at: string;
      last_authored_message: string;
      last_authored_message_at: string;
      message_count: number;
      subscribed: boolean;
      private: boolean;
      starred: boolean;
      properties: string[];
      audience: number[];
      audience_contexts: {
        [key: string]: string[];
      };
      avatar_url: string;
      participants: CanvasConversationParticipant[];
      messages?: CanvasConversationMessage[];
    }
  • src/index.ts:1071-1073 (registration)
    Registration of the tool list handler that exposes all MCP tools, including canvas_get_conversation, via ListToolsRequestSchema.
    this.server.setRequestHandler(ListToolsRequestSchema, async () => ({
      tools: TOOLS
    }));
  • src/index.ts:1075-1083 (registration)
    Registration of the CallToolRequestSchema handler containing the switch case that dispatches to the canvas_get_conversation implementation.
    // Handle tool calls with comprehensive error handling
    this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
      try {
        const args = request.params.arguments || {};
        const toolName = request.params.name;
        
        console.error(`[Canvas MCP] Executing tool: ${toolName}`);
        
        switch (toolName) {
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Get details' which implies a read-only operation, but does not specify authentication requirements, rate limits, error handling, or what 'details' entail (e.g., metadata, messages, participants). For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence ('Get details of a specific conversation') that is front-loaded with the core purpose. There is no wasted verbiage or redundancy, making it appropriately concise and well-structured for its simplicity.

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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic action but lacks context on return values, error cases, or behavioral traits. Without an output schema, it should ideally hint at what 'details' include, but the simplicity keeps it from being severely incomplete.

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, with the parameter 'conversation_id' documented as 'ID of the conversation'. The description adds no additional meaning beyond this, such as format examples or sourcing instructions. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 'Get details of a specific conversation' clearly states the verb ('Get') and resource ('conversation'), making the purpose unambiguous. It distinguishes from siblings like 'canvas_list_conversations' by specifying retrieval of a single conversation rather than listing multiple. However, it lacks specificity about what 'details' include, which slightly reduces clarity.

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. It does not mention prerequisites (e.g., needing a conversation ID), exclusions, or comparisons to siblings like 'canvas_list_conversations' for bulk retrieval or 'canvas_create_conversation' for creation. Usage is implied by the name but not explicitly stated.

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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