langfuse-mcp

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

With no annotations provided, the description carries the full burden. It clearly indicates this is a creation/mutation operation ('Create a new dataset'), but doesn't disclose behavioral traits like required permissions, whether creation is idempotent, rate limits, or error conditions. It does specify that the name 'must be unique', which is useful context beyond basic parameter documentation.

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 well-structured with clear sections (purpose, usage context, Args, Returns) and appropriately sized. Every sentence earns its place, though the Args section could be more concise since it largely repeats schema information. The front-loaded purpose statement is effective.

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 that this is a creation tool with no annotations but with a detailed output schema (which the description references), the description is reasonably complete. It explains what the tool does, provides parameter context, and documents the return structure. However, it lacks information about error conditions, permissions, or rate limits that would be helpful for a mutation operation.

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?

Schema description coverage is 100%, so the schema already fully documents all parameters. The description's Args section repeats what's in the schema without adding significant additional meaning (e.g., it doesn't explain format constraints for metadata or provide examples). The baseline of 3 is appropriate when the schema does the heavy lifting.

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 clearly states the specific action ('Create a new dataset') and resource ('in the project'), with additional context about what datasets are used for ('store evaluation test cases with input/expected output pairs'). It distinguishes this tool from siblings like 'create_dataset_item' (which adds items to datasets) and 'list_datasets' (which retrieves existing datasets).

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 implies usage context by explaining what datasets are used for, but doesn't explicitly state when to use this tool versus alternatives. For example, it doesn't mention whether to use this versus 'create_dataset_item' for adding data, or 'list_datasets' for checking existing datasets before creation. The guidance is present but not explicit about alternatives.

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