Skip to main content
Glama
ainote-dev

AI Note MCP Server

by ainote-dev

list_tasks

Retrieve and filter tasks from AI Note based on status, keyword search, or limit. Manage and organize tasks efficiently for enhanced productivity.

Instructions

List tasks from AI Note

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of tasks to return (default: 25, max: 500)
searchNoSearch keyword in task content
statusNoFilter by task status

Implementation Reference

  • Handler function for the 'list_tasks' tool. Proxies the tool call to the backend service via apiClient.callTool, which sends a JSON-RPC request.
    {
      definition: listTasksDefinition(),
      handler: async (args, { apiClient }) => {
        const result = await apiClient.callTool('list_tasks', args);
        return result;  // Return full result with { content: [...] }
      }
    },
  • Schema definition for the 'list_tasks' tool, including input schema with optional parameters for status, limit, and search.
    function listTasksDefinition() {
      return {
        name: 'list_tasks',
        description: 'List tasks from AI Note',
        inputSchema: {
          type: 'object',
          properties: {
            status: {
              type: 'string',
              enum: ['pending', 'completed'],
              description: 'Filter by task status'
            },
            limit: {
              type: 'number',
              description: 'Maximum number of tasks to return (default: 25, max: 500)'
            },
            search: {
              type: 'string',
              description: 'Search keyword in task content'
            }
          }
        }
      };
    }
  • Registration of shared tools (including 'list_tasks') into the ToolRegistry via registerMany(getSharedTools()). Called during server creation.
    function registerTools(registry, { includeChatGpt }) {
      registry.registerMany(getSharedTools());
    
      if (includeChatGpt) {
        registry.registerMany(getChatGptTools());
      }
    }
  • Helper method 'callTool' on apiClient that implements the proxy to backend by posting JSON-RPC 'tools/call' request to /api/mcp endpoint.
    // JSON-RPC helper 메서드 추가
    axiosInstance.callTool = async (toolName, arguments_) => {
      const rpcRequest = createToolCallRequest(toolName, arguments_);
      const response = await axiosInstance.post('/api/mcp', rpcRequest);
    
      if (response.data.error) {
        throw new Error(`JSON-RPC Error [${response.data.error.code}]: ${response.data.error.message}`);
      }
    
      return response.data.result;
    };
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits like pagination, rate limits, authentication needs, or what 'AI Note' entails. It misses key details for a read operation.

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 with zero waste, clearly front-loaded with the core purpose. It's appropriately sized for a simple list tool.

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 no annotations and no output schema, the description is incomplete. It lacks context on return format, error handling, or the 'AI Note' system, leaving gaps for a tool with three parameters and behavioral unknowns.

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?

Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or default behaviors, meeting the baseline for high coverage.

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 action ('List') and resource ('tasks from AI Note'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_categories' or specify what 'AI Note' refers to, which prevents a perfect score.

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?

No guidance is provided on when to use this tool versus alternatives like 'list_categories' or 'search' parameters. The description lacks context about prerequisites or typical use cases, offering minimal usage direction.

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

Related 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/ainote-dev/ainote-mcp'

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