Skip to main content
Glama

TodoRead

Retrieve the current task list to view pending and completed items, enabling organized workflow tracking across coding sessions.

Instructions

Read the current task list.

Returns a list of todos with their current state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for TodoRead: calls todo_read() and wraps with error handling.
    @mcp.tool
    async def TodoRead() -> dict[str, Any]:
        """
        Read the current task list.
    
        Returns a list of todos with their current state.
        """
        try:
            return await todo_read()
        except Exception as e:
            return {
                "error": {
                    "code": "READ_ERROR",
                    "message": f"Failed to read todos: {str(e)}",
                }
            }
  • Core helper function that reads the todo list from the store and returns it as TodoReadResponse.
    async def todo_read() -> TodoReadResponse:
        """
        Read the current task list.
    
        Returns:
            TodoReadResponse with list of todos
        """
        # Read todos
        todos = await store.read_todos()
    
        # Return response
        return {"todos": todos}
  • Type definition for the TodoRead response schema.
    class TodoReadResponse(TypedDict):
        todos: list[Todo]
  • Todo item schema used in TodoReadResponse.
    class Todo(TypedDict):
        id: str
        content: str
        status: TodoStatus
        priority: TodoPriority
        created_at: str
        updated_at: str
        metadata: dict[str, Any] | None
  • src/server.py:44-44 (registration)
    Registration of the TodoRead tool using @mcp.tool decorator.
    @mcp.tool
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. It discloses that the tool returns a list of todos with their current state, which adds some behavioral context beyond the basic purpose. However, it lacks details on permissions, rate limits, error handling, or whether the operation is safe (e.g., read-only). For a tool with no annotations, this is a significant gap in transparency.

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 with two sentences that directly state the purpose and return value. It is front-loaded with the core function and wastes no words, making it easy for an AI agent to parse quickly. Every sentence earns its place by providing essential information.

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

Completeness4/5

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

Given the tool's low complexity (0 parameters, simple read operation), an output schema exists (which covers return values), and no annotations, the description is reasonably complete. It specifies what the tool does and what it returns, which is sufficient for basic understanding. However, it could benefit from more behavioral details given the lack of annotations, but the output schema mitigates this gap.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. Baseline is 4 for zero parameters, as it avoids redundancy and focuses on the tool's purpose.

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's purpose as 'Read the current task list' (verb+resource), which is specific and unambiguous. It distinguishes from the sibling TodoWrite by focusing on reading rather than writing. However, it doesn't explicitly mention what distinguishes it from potential other read operations beyond the sibling context.

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 through the phrase 'current task list,' suggesting this tool is for retrieving existing todos. It doesn't provide explicit guidance on when to use this versus alternatives (though the sibling TodoWrite is clearly for writing), nor does it mention any prerequisites or exclusions. Usage is implied 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.

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/joehaddad2000/claude-todo-emulator'

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