Flippa MCP

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

The description adds valuable context beyond annotations, such as the tool's focus on aggregate statistics and the types of data returned (e.g., verification rates, revenue multiples), which are not covered by the read-only and non-destructive hints. However, it does not detail potential limitations like rate limits or data freshness, leaving some behavioral aspects unspecified. No contradiction with annotations exists.

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 and front-loaded, starting with a clear purpose statement, followed by concise sections for Args, Returns, and Examples. Each sentence adds specific value without redundancy, such as explaining aggregation behavior and providing usage examples, making it efficient and easy to parse.

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 tool's moderate complexity, lack of output schema, and rich annotations, the description is largely complete, covering purpose, parameters, return details, and examples. However, it could improve by explicitly mentioning any constraints like data recency or aggregation methods, slightly reducing completeness for advanced use cases.

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?

With 100% schema description coverage, the input schema already fully documents both parameters, including enums and defaults. The description adds minimal semantic value by briefly mentioning the optional 'property_type' and 'response_format' in the Args section, but does not provide additional insights beyond what the schema states, aligning with the baseline score for high coverage.

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 tool's purpose with a specific verb ('Get aggregate market statistics') and resource ('from the Flippa marketplace'), distinguishing it from siblings like 'flippa_analyze_listing' or 'flippa_search_listings' by focusing on aggregated data rather than individual listings. It specifies what the snapshot includes, such as total counts and price/revenue/profit statistics, making the scope explicit and unique.

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 explicit guidance on when to use this tool, including examples for full market overview and SaaS-specific overview, and implies alternatives by contrasting with sibling tools that handle individual listings or comparable sales. It clearly indicates that omitting 'property_type' aggregates across all types, offering practical usage scenarios without misleading information.

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