Server Quality Checklist

Tool Scores

• toolhub_healthA
  4.2/5.0
  Behavior4/5

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

  Annotations already indicate readOnlyHint=true, destructiveHint=false. The description adds value by detailing the return fields (chromadb, knowledgeGraph, toolCount, version), providing context on what the tool checks. No contradictions with 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 concise, consisting of a brief sentence and a bullet list of return fields. Every part is informative with no wasted words.

  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?

  For a simple health check tool with no parameters and no output schema, the description sufficiently explains the return structure. It could mention typical use or that it's a lightweight check, but overall it is complete.

  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 zero parameters and schema coverage is 100%. The description does not need to add parameter meaning. A baseline score of 4 is appropriate for a parameterless tool.

  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 checks service status and lists the returned fields. The verb 'Check' with resource 'Tool Hub service status' is specific and distinguishes from siblings like search, expand, and cluster.

  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 does not explicitly state when to use this tool versus alternatives. However, the purpose is clear and the tool has no parameters, making it suitable as a lightweight health check. No usage caveats or exclusions are mentioned.

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

• toolhub_searchA
  4.2/5.0
  Behavior4/5

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

  Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral details about the search mechanism (semantic similarity via ChromaDB) and knowledge graph expansion. No contradictions with annotations.

  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 well-organized with a purpose statement, args detail, and examples. While somewhat lengthy, it is front-loaded with the main purpose and each section adds value. Minor redundancy in repeating args that are in schema, but overall structure is good.

  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 no output schema, the description provides a detailed example of the return format, including JSON structure and stats. It explains the mechanism (ChromaDB, KG) and covers all relevant aspects for a tool with 4 parameters and no nested objects.

  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 coverage is 100%, so baseline is 3. The description includes an 'Args' section that repeats schema information and adds examples, but does not significantly enhance understanding beyond the schema. The example return format is helpful but not required for parameter semantics.

  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 searches for relevant tools using Vector Search and Knowledge Graph. It specifies the types of items found (MCP servers, skills, tools) and distinguishes from siblings like toolhub_expand, toolhub_health, and toolhub_cluster.

  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 explicitly says 'Use this tool when you need to find which tools are relevant for a task' and provides examples. However, it does not explicitly state when not to use it or contrast with siblings, though context implies that for expansion or cluster operations different tools should be used.

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

• toolhub_clusterA
  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, destructiveHint=false. The description adds behavioral context by explaining that the tool returns a complete set of tools including primary and dependencies, and provides a sample output structure. No contradictions with 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 concise and well-structured: a one-sentence title, a brief explanation of how it works, then clearly labelled Args and Returns sections. Every sentence adds value with no redundancy.

  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?

  The tool has three parameters with full schema coverage, and the description provides a detailed output example including fields like primary, dependencies, context, and stats. Although no output schema exists, the example sufficiently describes the return value for an agent to use the tool correctly.

  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 coverage is 100%, and the description repeats parameter definitions with slight elaboration (e.g., 'Natural language task description' for query). Since schema already provides descriptions, the description adds marginal value. Baseline 3 is appropriate.

  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: 'Get a complete tool cluster for a task query.' It explains it combines vector search and graph expansion to return primary tools, dependencies, and context. This distinguishes it from siblings like toolhub_search (likely semantic matches only) and toolhub_expand (graph expansion on a single tool).

  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 explicitly says 'Use this tool for complete task setup - returns everything needed to start working.' This gives clear guidance on when to use it, but does not explicitly mention when not to use it or compare with alternatives beyond the description of what it returns.

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

• toolhub_expandA
  4.6/5.0
  Behavior5/5

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

  Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds significant behavior context: traversal depth limits (default 2, max 4), relation types (REQUIRES, WORKS_WITH, etc.), example output, and explanation of how dependencies are returned. No contradiction with annotations.

  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 well-structured with sections for arguments, relation types, returns, and usage tip. It is informative but slightly verbose; could be more concise by merging the argument list with the existing schema documentation. Still, every sentence adds useful information.

  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 no output schema, the description provides an example JSON response, lists all relation types, and explains the depth and filter parameters. For a tool with 4 parameters, this is sufficiently complete for an agent to understand inputs and outputs.

  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?

  Although schema description coverage is 100%, the description adds value by providing an example of tool_name ('n8n-workflow-builder'), explaining relation types, and showing the response format. This goes beyond the schema's basic property descriptions.

  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: 'Expand a tool to find its dependencies via Knowledge Graph.' It uses a specific verb ('expand') and resource ('tool'), and distinguishes itself from sibling tools like toolhub_search, toolhub_health, and toolhub_cluster by focusing on dependency discovery.

  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 a clear usage guideline: 'Use this tool when you know a primary tool and need to find related tools.' However, it does not explicitly mention when not to use it or directly compare with alternatives, such as using toolhub_search for broader search instead of dependency traversal.

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