Server Quality Checklist

Tool Scores

• flippa_search_listingsA
  3.8/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond this: it specifies the tool returns paginated results with a 'has_more' flag, describes the content of listings (price, revenue, profit, bid count, verification status), and mentions response format options. This provides useful behavioral details not covered by annotations.

  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 well-structured with clear sections (purpose, Args, Returns, Examples) and every sentence earns its place. The front-loaded purpose statement is followed by organized parameter documentation and practical examples. No wasted words while maintaining clarity.

  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 complexity (7 parameters, search functionality) and rich annotations, the description provides good completeness: it explains what the tool does, documents all parameters, describes the return format and content, and includes helpful examples. The main gap is lack of explicit sibling differentiation, but overall it's quite complete for a search tool without an output schema.

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

  Parameters3/5

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

  With 100% schema description coverage, the schema already documents all 7 parameters thoroughly with enums, defaults, and descriptions. The description's 'Args' section essentially repeats what's in the schema (though organized differently), adding minimal semantic value. The examples help illustrate parameter combinations but don't add new parameter meaning beyond the schema.

  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 'Search and browse listings on the Flippa marketplace' - a specific verb ('search') and resource ('listings') that distinguishes it from siblings like 'analyze_listing' or 'get_listing'. However, it doesn't explicitly differentiate from 'market_overview' which might also involve searching/browsing.

  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 through examples (searching SaaS listings, most profitable websites, ending soon auctions) but doesn't explicitly state when to use this vs. alternatives like 'flippa_get_listing' for specific listings or 'flippa_market_overview' for aggregated data. No explicit when-not-to-use guidance is provided.

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

• flippa_analyze_listingA
  3.9/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds valuable behavioral context beyond annotations by specifying it's a 'computed tool' that fetches and calculates metrics, and details the return structure (financial metrics, verdict, risk factors). However, it doesn't mention potential limitations like rate limits or data freshness.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

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

  The description is appropriately sized and front-loaded with a clear purpose statement. It uses bullet points for returns and examples for readability, but the 'Args' and 'Returns' sections slightly duplicate schema information. Every sentence adds value, though some trimming could improve efficiency.

  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 complexity (computational analysis) and lack of output schema, the description provides a complete overview of what the tool does, including specific metrics and verdict categories. It compensates for the missing output schema by detailing return values. However, it doesn't cover all contextual aspects like error handling or data sources.

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

  Parameters3/5

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

  Schema description coverage is 100%, with both parameters well-documented in the schema. The description adds minimal value beyond the schema by restating parameter purposes in the 'Args' section and providing examples. It doesn't explain parameter interactions or edge cases, so it meets the baseline for high schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description clearly states the tool's purpose with specific verbs ('analyze', 'compute', 'assess') and resources ('Flippa listing's valuation', 'financial metrics', 'risk'). It distinguishes itself from siblings like 'flippa_get_listing' (which likely fetches raw data) and 'flippa_comparable_sales' (which focuses on market comparisons) by emphasizing computed analysis rather than data retrieval.

  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 examples and return values (e.g., analyzing a specific listing ID), but it does not explicitly state when to use this tool versus alternatives like 'flippa_get_listing' (for raw data) or 'flippa_comparable_sales' (for market context). The examples provide practical guidance but lack explicit comparative instructions.

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

• flippa_comparable_salesA
  4/5.0
  Behavior3/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds useful behavioral context: it explains the logic (e.g., using property_type and revenue range 0.5x-2x for matching when listing_id is provided, and the override behavior). However, it does not disclose potential limitations like rate limits, authentication needs, or error conditions.

  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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by logical flow on usage, a clear Args/Returns/Examples structure, and no redundant information. Every sentence earns its place by adding clarity or guidance.

  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 moderate complexity (4 parameters, no output schema, but with rich schema descriptions and annotations), the description is largely complete. It covers purpose, usage logic, parameters, returns, and examples. However, without an output schema, it could benefit from more detail on return structure (e.g., format of 'list of comparable listings'), though the examples partially compensate.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it briefly mentions the revenue range logic for listing_id and the override behavior for property_type, but does not provide additional syntax, format details, or examples beyond what is in the schema and examples section.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description clearly states the tool's purpose: 'Find comparable listings on Flippa for valuation comparison.' It specifies the verb ('find') and resource ('comparable listings'), and distinguishes it from siblings by focusing on valuation comparison rather than analysis, retrieval, overview, or general search.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

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

  The description provides clear context on when to use the tool: for valuation comparison, with options to search by listing_id or property_type. It mentions that property_type overrides the target listing's type if both are provided, but does not explicitly state when to use this tool versus sibling alternatives like flippa_search_listings or flippa_analyze_listing.

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

• flippa_get_listingA
  4.3/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds value by specifying the return content (price, revenue, etc.) and response format options, which are behavioral traits not covered by annotations, though it doesn't mention rate limits or auth needs.

  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 front-loaded with the core purpose in the first sentence, followed by structured sections (Args, Returns, Examples) that are efficient and informative. Every sentence earns its place without redundancy, making it appropriately sized and well-organized.

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

  Completeness5/5

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

  Given the tool's low complexity (2 parameters, no nested objects), rich annotations (covering read-only, idempotent, non-destructive), and 100% schema coverage, the description is complete enough. It adds useful context on return content and examples, compensating for the lack of an output schema, making it fully adequate for agent use.

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

  Parameters3/5

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

  Schema description coverage is 100%, with both parameters well-documented in the schema (listing_id as ID, response_format with enum and default). The description adds minimal semantics beyond the schema, only restating the parameter names and providing examples, so it meets the baseline for high schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description clearly states the verb 'Get' and resource 'full details for a specific Flippa listing by ID', making the purpose explicit. It distinguishes from siblings like 'flippa_search_listings' (which searches multiple listings) and 'flippa_analyze_listing' (which likely analyzes rather than fetches raw details), providing specific differentiation.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

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

  The description implies usage context by specifying it's for a 'specific' listing by ID, suggesting it should be used when you have a known listing ID. However, it doesn't explicitly state when not to use it (e.g., vs. 'flippa_search_listings' for unknown IDs) or name alternatives, so it lacks full exclusion guidance.

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

• flippa_market_overviewA
  4.4/5.0
  Behavior4/5

  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.

  Conciseness5/5

  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.

  Completeness4/5

  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.

  Parameters3/5

  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.

  Purpose5/5

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

  The description clearly states the tool's purpose 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.

  Usage Guidelines5/5

  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.