Server Quality Checklist

Tool Scores

• list_registry_statsA
  3.8/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It describes what the tool returns (overview with counts) but doesn't disclose behavioral traits like whether it's read-only, if it requires authentication, rate limits, or error conditions. The description is accurate but lacks operational context.

  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 perfectly concise with two sentences that each earn their place. The first sentence states the purpose and return values, the second provides usage guidance. No wasted words, well-structured and front-loaded.

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

  Completeness3/5

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

  Given the tool has no parameters and no output schema, the description provides adequate context about what the tool does and when to use it. However, without annotations or output schema, it lacks details about return format, error handling, or operational constraints that would be helpful for a registry overview tool.

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

  Parameters4/5

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

  The tool has 0 parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist. It focuses on the tool's purpose and usage context, which is correct for a parameterless tool.

  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 the tool's purpose: 'Get an overview of the subgraph registry' with specific details about what it returns (total count, available domains, networks, and protocol types with counts). It distinguishes from siblings by mentioning this is for understanding available data before searching, but doesn't explicitly name alternatives.

  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 for when to use this tool: 'to understand what data is available before searching.' This implies it should be used as an initial overview before using search tools like 'search_subgraphs.' However, it doesn't explicitly state when NOT to use it or name specific alternatives.

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

• recommend_subgraphA
  3.9/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: automatic inference of domain/protocol type, inclusion of reliability scores and query URLs, and the need to replace [api-key] for live queries. However, it lacks details on error handling, rate limits, or authentication requirements, leaving gaps for a tool with no annotation coverage.

  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, with every sentence earning its place. It efficiently explains the tool's function, input processing, output components, and usage instruction without redundancy or unnecessary elaboration.

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

  Completeness3/5

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

  Given no annotations and no output schema, the description provides a solid foundation but has gaps. It covers the tool's purpose, input interpretation, and output structure, but lacks details on error cases, performance characteristics, or exact return format. For a recommendation tool with 2 parameters and no structured output documentation, this is adequate but not fully comprehensive.

  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 both parameters (goal and chain). The description adds marginal value by emphasizing the natural-language aspect of 'goal' and mentioning optional chain filtering, but does not provide additional syntax or format details beyond what the schema specifies. Baseline 3 is appropriate when schema does the heavy lifting.

  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 ('returns the best matching subgraphs') and resources ('subgraphs with reliability scores and query URLs'). It distinguishes from siblings by focusing on recommendation based on natural-language goals rather than detailed lookup (get_subgraph_detail), statistical listing (list_registry_stats), or general search (search_subgraphs).

  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 for when to use this tool ('Given a natural-language goal...'), but does not explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage for goal-based matching rather than direct queries or searches, offering good guidance without exclusions.

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

• get_subgraph_detailA
  4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the return format (listing specific data points like domain, protocol type, entities, reliability score, etc.), which is crucial for understanding output. However, it lacks details on error handling, rate limits, or authentication needs, leaving some behavioral aspects unspecified.

  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 efficiently structured in two sentences: the first states the purpose and parameter, and the second details the return values. Every element contributes directly to understanding the tool's function and output, with no redundant or unnecessary information, making it highly concise and well-organized.

  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 (single parameter, no output schema, no annotations), the description provides a complete overview of purpose and detailed return values, which compensates for the lack of output schema. However, it could improve by addressing potential errors or usage constraints, slightly limiting completeness for safe operation by an AI agent.

  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?

  The schema description coverage is 100%, with the single parameter 'subgraph_id' clearly documented in the schema. The description adds minimal value by restating that it accepts 'subgraph ID or IPFS hash', which is already covered in the schema's description. No additional syntax, format, or contextual details beyond the schema are provided, meeting 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 specific action ('Get full classification detail'), target resource ('specific subgraph'), and identification method ('by its subgraph ID or IPFS hash'). It distinguishes from sibling tools like list_registry_stats (aggregate statistics), recommend_subgraph (recommendations), and search_subgraphs (searching multiple subgraphs) by focusing on detailed retrieval for a single identified subgraph.

  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 when detailed classification information for a specific subgraph is needed, but does not explicitly state when to use this tool versus alternatives like search_subgraphs (which might return less detail) or recommend_subgraph (which suggests subgraphs). No explicit exclusions or prerequisites are provided, leaving some ambiguity about optimal use cases.

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

• search_subgraphsA
  4.3/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well: it discloses the registry size (15,500+ subgraphs), ranking method (by reliability score), return format (query URLs), and post-search workflow (how to actually query data with API key). It doesn't mention rate limits, authentication needs, or pagination behavior, but provides substantial operational context beyond basic functionality.

  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: the first sentence establishes core functionality, followed by filter options, return format, and post-usage instructions. Every sentence adds value, though the long list of example values in the first sentence could be slightly streamlined.

  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 7 parameters with full schema coverage but no annotations or output schema, the description provides good context: it explains what the tool does, how results are ranked, what's returned (query URLs), and crucial next steps for data querying. The main gap is lack of output format details, but the description compensates well with operational guidance.

  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 7 parameters thoroughly. The description adds minimal value beyond the schema by listing example values for filters (e.g., 'defi, nfts, dao' for domain) and clarifying that 'query' is 'free-text keyword' search. This meets the baseline 3 when schema does heavy lifting.

  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: 'Search and filter the classified subgraph registry (15,500+ subgraphs)' with specific verbs ('search', 'filter') and resource ('subgraph registry'). It distinguishes from siblings by focusing on search/filtering capabilities rather than detail retrieval (get_subgraph_detail), statistics (list_registry_stats), or recommendations (recommend_subgraph).

  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 vs alternatives: 'To query data: POST GraphQL to https://gateway.thegraph.com/api/[api-key]/subgraphs/id/[subgraph-id]' indicates this tool is for discovery/filtering, while actual data querying requires a different API call. It also implicitly contrasts with siblings by focusing on search/filtering rather than other operations.

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