Todoist MCP Server

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. 'Create a new label' implies a write operation, but it doesn't disclose any behavioral traits such as authentication requirements, rate limits, error handling, or what happens on success (e.g., returns a label ID). For a mutation tool with zero annotation coverage, 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.

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

The description is a single, efficient sentence ('Create a new label') that is front-loaded with the core action. There is no wasted verbiage, repetition, or unnecessary elaboration, making it highly concise and well-structured for quick understanding.

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

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

Given the complexity of a mutation tool (creating a label) with no annotations and no output schema, the description is incomplete. It lacks information on behavioral aspects (e.g., permissions, side effects), usage context, and what the tool returns. While the schema covers parameters well, the overall context for safe and effective use is insufficient, especially compared to siblings that might have more detailed descriptions.

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

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

The input schema has 100% description coverage, with clear documentation for all four parameters (name, color, order, is_favorite). The description adds no parameter semantics beyond what the schema provides, as it doesn't explain the purpose or constraints of the parameters (e.g., color format, order significance). With high schema coverage, 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.

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

The description 'Create a new label' clearly states the verb ('Create') and resource ('label'), making the purpose immediately understandable. It distinguishes this tool from siblings like todoist_update_label or todoist_delete_label by specifying it's for creation rather than modification or deletion. However, it doesn't specify what kind of label (e.g., in Todoist's task management system), which keeps it from 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.

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 doesn't mention prerequisites (e.g., needing a Todoist account or authentication), compare it to similar tools like todoist_update_label for modifying existing labels, or indicate when not to use it (e.g., for batch operations). This lack of context makes it harder for an AI agent to select the right tool in a workflow.

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