Featureflow 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. It mentions filtering capabilities but fails to describe critical behaviors such as pagination, sorting, default limits, error conditions, or the format of returned data. For a list tool with no 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 that front-loads the core action ('List all features') and succinctly enumerates the filtering options. Every word serves a purpose, with no redundant or vague phrasing, making it highly concise and well-structured.

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 lack of annotations and output schema, the description is incomplete for a tool with four parameters and list functionality. It doesn't address behavioral aspects like response format, pagination, or error handling, which are crucial for an AI agent to use the tool effectively. The high schema coverage doesn't compensate for these missing contextual elements.

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 input schema already documents all four parameters thoroughly. The description adds minimal value by listing the filter types ('maintaining, bookmarked, recent') and hinting at the purpose of filtering, but it doesn't provide additional semantic context beyond what the schema offers, aligning with the baseline score.

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 verb ('List') and resource ('all features'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from potential sibling list tools like 'list_projects' or 'list_targets' beyond the resource type, 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.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through the mention of filtering options ('Can filter by project key, search query, or predefined filters'), suggesting when this tool might be appropriate. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get_feature' for single features or how it relates to other list tools, leaving some ambiguity.

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