Server Quality Checklist

Tool Scores

• list_decisionsA
  4.2/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries full burden. It describes scoping via read key, default time window (7 days), and sort order (newest first). However, it lacks detail on the exact return fields or behavior under errors, which slightly reduces transparency.

  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 long, front-loads the main purpose, and includes no redundant information. 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?

  Given 3 optional parameters, 100% schema coverage, and no output schema, the description covers the purpose, ordering, and tenant scoping well. It could be improved by mentioning the main fields in each decision record, but overall it is sufficiently complete.

  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 does not add additional meaning beyond what the schema already provides for parameters 'from', 'to', and 'limit'. No extra context about defaults or constraints beyond schema 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 lists AI decisions for the calling tenant within a time window, returned newest first. It distinguishes itself from siblings (record_decision, verify_decision) through its focus on listing, compliance, and audit.

  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 mentions use cases: compliance review, audit prep, or agent self-inspection. It also notes that cross-tenant reads require an admin read key. While it doesn't explicitly say when not to use, the context of siblings and clear purpose provides adequate guidance.

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

• verify_decisionA
  4.4/5.0
  Behavior5/5

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

  Describes the internal process: fetching from DynamoDB and S3 Object Lock COMPLIANCE mode and comparing them. Explains the return indicator 'integrity_verified=true'. No annotations provided, so description carries full burden and does it well.

  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?

  Two sentences, front-loaded with the purpose, no redundant information. Every word 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 tool with one parameter and no output schema, the description explains the verification process and return value. It implicitly covers what the agent needs to know. Could explicitly mention the output structure, but not necessary given the simplicity.

  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 single parameter event_id is fully described in the schema (100% coverage). The description does not add additional meaning beyond what the schema already provides, so 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 verb 'verify' and the resource 'recorded AI decision'. It distinguishes from siblings by specifying the verification of integrity, whereas sibling tools list or record decisions.

  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?

  Explicitly states usage contexts: 'satisfy a regulator request' or 'prove an audit trail'. No explicit when-not-to-use, but the use cases are well-defined and imply not for other purposes.

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

• record_decisionA
  4.6/5.0
  Behavior5/5

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

  The description reveals key behaviors: the record is immutably sealed in S3 Object Lock for 7 years, queryable for lifetime, and that raw inputs are hashed locally before transit (the raw text never leaves the server). It also warns that ai_decision_output should not contain raw PII.

  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 concise sentences cover purpose, key features, and usage guidance. No wasted words; every sentence adds value. Front-loaded with the main action.

  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 no annotations and no output schema, the description provides substantial context: data handling, retention, compliance, and typical use cases. It does not explicitly describe the return value, but the focus on audit trail and immutability implies a confirmation or ID. Overall, nearly complete for the tool's complexity.

  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?

  Schema already covers all parameters with detailed descriptions. The main description adds context like 'hashed locally' for raw inputs and mentions EU AI Act relevance for human_in_loop, which goes beyond the schema. Baseline 3 with extra value gives a 4.

  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 (Record) and resource (AI decision to the audit ledger), and it lists what is stored. It also implicitly distinguishes from siblings list_decisions and verify_decision by focusing on writing rather than reading or verifying.

  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?

  Explicitly says when to use: immediately after any AI decision that may need auditing (credit, hiring, etc.). Does not mention when not to use or alternatives, but the sibling tools are for different purposes, so guidance is clear enough.

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