Server Quality Checklist

Tool Scores

• score_conversationA
  3.6/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the disclosure burden. It clearly states the tool analyzes and produces derived metrics but does not reveal whether there are side effects (e.g., persistence, write-back to CRM), whether the raw transcript is retained, or what happens on unprocessable input. For an analysis tool this is moderate transparency, but no side-effect or input-limit behavior is mentioned.

  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 a single dense sentence that front-loads the action and enumerates outputs efficiently. It is complete without being verbose, though it could arguably be split for readability. No wasted words, earns its length.

  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?

  The tool has 4 required parameters, no output schema, and no annotations, so the description is the sole guide. It explains the broad outputs well, but with no output schema it does not communicate the return format or structure of the extracted intelligence, and the sibling get_lead_score suggests a related scoring ecosystem whose differentiation is unresolved. Adequate but leaves room for more.

  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 75%, so parameters are largely self-documenting (raw_outcome gets an example, call_duration gets units, source_system gets examples). The description adds that analysis is structured 'business intelligence' but does not clarify how call_duration or source_system influence the scoring, which would add value beyond the schema. One parameter has no description in schema, though the description partially covers the overall intent.

  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 uses a specific verb ('Analyze') with a clear resource ('a call transcript') and enumerates the exact structured outputs it produces: intent, sentiment, urgency, objections, outcome, topics, flags, revenue signal, churn risk, and a summary. This clearly distinguishes it from sibling tools like get_lead_score (score retrieval) and diagnose_workflow_error (error diagnostics).

  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 for call transcripts yielding business intelligence but provides no explicit when-to-use vs alternatives guidance. It doesn't exclude scenarios or direct the agent toward when the score_conversation tool is preferable over get_lead_score, which could be relevant given 'revenue signal' and 'churn risk' outputs overlap thematically.

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

• get_lead_scoreA
  4.1/5.0
  Behavior4/5

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

  The description discloses a key behavior: that it internally reuses score_conversation's analysis before applying weighting. This gives the agent insight into the tool's internal mechanics. It also notes the repeat_caller param depends on external lookup, adding context. No annotations exist, so the description carries the burden, and it discloses the reuse behavior that would otherwise be invisible.

  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?

  Two concise sentences that state purpose, output, and the key relationship to the sibling tool. Efficient and front-loaded with the outcome. Could potentially clarify the weighting scheme, but 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 scoring tool with 5 params (4 required) and no output schema, the description communicates the core output (0-100 score, hot/warm/cold classification) and the internal pipeline. Missing return-format detail (e.g., does it return a JSON object, structured fields?) but given no output schema and moderate complexity, this is reasonably 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 80%, with descriptions on raw_outcome, call_duration, repeat_caller, and source_system. The tool description doesn't elaborate on parameter semantics beyond what the schema provides, but that's acceptable given high coverage. The behavior it adds about repeat_caller being externally determined aligns with the schema's note. 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?

  Clear verb+resource ('Score a call transcript 0-100 and classify it hot/warm/cold'). Describes what it does, what it returns, and explicitly distinguishes from sibling score_conversation by noting it 'Internally runs the same analysis' then applies 'point-based weighting'.

  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 clearly distinguishes from score_conversation by noting the internal reuse and additional weighting step, implying get_lead_score builds on the base scoring. However, it doesn't explicitly state when a user should choose this tool over score_conversation or diagnose_workflow_error, leaving some ambiguity about the decision.

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

• diagnose_workflow_errorA
  4.2/5.0
  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 handles it well: it explicitly declares statelessness, no live fetching/verification/deployment, and the honest 'not fixable' output possibility. It also warns about applying fixedWorkflow without review. This is strong behavioral disclosure absent any annotation support.

  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, zero filler. The first sentence packs purpose, inputs, and outputs. The second clarifies the stateless boundary. The third advises on safe application of the result. Every sentence earns its place with front-loaded core purpose.

  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 2-param tool with nested objects but no output schema, the description covers purpose, behavioral scope, and honest-limit disclosures well. Missing output schema means the agent can't see the return shape (rootCause/fixedWorkflow/confidence structure), but the description names the return elements in prose, partially compensating. Could add parameter format specifics, but overall 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 50%: errorMessage has only a minLength constraint, and workflowJson has a description ('The full n8n workflow JSON object (nodes + connections)'). The tool description adds input context but doesn't elaborate much beyond the schema. The description implies how parameters are consumed (as inputs to diagnosis) but adds limited format/semantic detail beyond what the schema states.

  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 uses a specific verb ('diagnose') plus resource (n8n workflow error), states the inputs (workflow JSON + error message), and clearly lists the outputs (root cause, proposed fix, confidence level). It also distinguishes from siblings by being stateless diagnosis while get_lead_score and score_conversation imply scoring/analytics tools.

  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 clearly states what it does NOT do ('does not verify, deploy, or fetch anything live') and advises pairing with human or separate review. It doesn't explicitly name alternative tools for when-not-to-use, but the stateless scope and non-verification caveat provide clear contextual boundaries.

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