Server Quality Checklist

Tool Scores

• lookup_macA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description provides good behavioral context: it returns vendor, OUI, kind, randomized flag, confidence, and source. It also mentions detection of randomized addresses. Only minor gaps like rate limits or prerequisites are missing.

  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 two sentences, front-loading key information. No unnecessary words, every sentence adds value.

  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 lookup tool with one parameter and no output schema, the description covers core behavior and return fields. Missing details like error handling, but still adequate.

  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 the parameter 'mac' is well-documented in the schema. The tool description adds global context but does not enhance parameter meaning beyond the schema.

  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: identify the manufacturer of a MAC address and detect randomized addresses. It uses specific verbs and resources, and distinguishes itself from the sibling tool 'submit_correction'.

  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 when to use this tool (when you need vendor info from a MAC) and contrasts with the sibling tool for corrections. However, it lacks explicit when-not-to-use or alternative guidance.

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

• submit_correctionA
  4.3/5.0
  Behavior4/5

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

  Without annotations, the description carries the full burden. It discloses key behavioral traits: submissions are queued for verification, never applied automatically, and must include an official source. This is sufficient for a non-destructive proposal action.

  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?

  Three sentences, front-loaded with the primary purpose, no redundant information. Every sentence earns its place.

  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?

  For a submission tool without an output schema, the description covers all necessary context: purpose, required source, queuing behavior, and slug format. It is complete for an agent to decide and invoke 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 description coverage is 100%, so baseline is 3. The description adds value by explaining the slug's source ('Use the router's slug from ssid.ai/routers/<slug>') and emphasizing the requirement for an official source URL. This goes slightly beyond the schema but does not fundamentally change the agent's understanding.

  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: proposing a fix to a router model's default-login fields on ssid.ai. It specifies the exact fields (default gateway IP, username, password, etc.) and distinguishes from the sibling tool 'lookup_mac' which serves a different purpose.

  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 guidance: it requires an official manufacturer source URL (excluding aggregators/forums) and instructs users to obtain the slug from the router's page. It also explains that submissions are queued for verification and never applied automatically, which sets expectations. However, it does not explicitly state when not to use this tool or mention alternatives.

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