Server Quality Checklist

Tool Scores

• list_framesA
  4/5.0
  Behavior2/5

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

  No annotations are provided, so the description must fully disclose behavior. It describes the main action (navigate and return frame info) but does not mention side effects, auth requirements, rate limits, or error handling. The word 'diagnostic' hints at safety but is insufficient.

  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 'Diagnostic', no filler. Every word adds value. Highly efficient.

  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 diagnostic tool with 2 parameters and no output schema, the description covers the action, return fields, and usage hints. It lacks detail on error cases or empty frame trees, but is sufficient for basic understanding.

  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 coverage is 100%, so baseline is 3. The description adds value by specifying that the URL must be http/https and allowlisted, and lists the return fields. This extra context improves understanding 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: navigate to a URL and return its frame tree with specific fields. It is distinct from sibling tools (check_auth, extract_semantic_dom, extract_semantic_dom_after) which focus on authentication or extraction, not frame diagnostics.

  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 includes context for when to use it ('before extraction') and labels it as a diagnostic tool. It implies alternatives by mentioning extraction in the context, but does not explicitly state when not to use it.

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

• extract_semantic_domA
  4.2/5.0
  Behavior3/5

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

  With no annotations provided, the description must disclose behavioral traits. It mentions navigation to a staging URL, returning semantic JSON, and flags truncation behavior. However, it does not explicitly state that the operation is read-only or address potential side effects, authorization needs, or rate limits. The description adds some behavioral context but lacks completeness.

  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 extremely concise with two sentences. The first sentence delivers the core purpose and key output details, and the second provides clear usage guidance. Every sentence serves a purpose, and the key information is front-loaded.

  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 and no output schema or annotations, the description covers the tool's purpose, usage timing, and parameter behaviors well. It lacks details about the exact JSON output structure and error handling, but the overall context is sufficient for an agent to use the tool effectively.

  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 coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema by providing behavioral details, such as truncation being flagged not silent (max_nodes), mobile viewport size and touch context (viewport), SPA content hint (wait_selector), and heuristics for cursor:pointer elements (include_click_targets). This elevates the score above baseline.

  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 ('Navigate to a staging URL and return factual Semantic JSON') and clearly identifies the resource (interactive/test-relevant elements with Playwright-native locators). It also distinguishes from the sibling tool 'extract_semantic_dom_after' by indicating this is for use before writing tests, making the purpose unmistakable.

  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 states when to use this tool: 'Use this before writing any Playwright test so selectors are real, not guessed.' This provides clear context, though it does not explicitly mention when not to use it or name alternative tools (e.g., extract_semantic_dom_after) as exclusions.

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

• extract_semantic_dom_afterA
  4.2/5.0
  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 that uniqueness reflects capture time and that accumulating UI can multiply matches later. It also mentions the allowlist restriction. It does not explicitly describe side effects of actions, but the actions are declarative and the tool focuses on extraction, so the transparency is adequate.

  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 with four sentences, each adding value. It front-loads the comparison to extract_semantic_dom and efficiently conveys the tool's purpose and key constraints.

  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 complexity (10 parameters, no output schema), the description explains the core functionality, use cases, and behavioral nuances. It implies the return format is the same as extract_semantic_dom, which is sufficient. Minor gaps like explicit return format details are covered by the sibling reference.

  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 description adds limited value beyond the schema. It does provide context for the actions parameter (derive from prior extraction, main frame). 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 that this tool is like extract_semantic_dom but performs actions first and returns Semantic JSON of the resulting state. It specifies use cases like seeing success/error toasts, validation messages, and opened dialogs, which distinguishes it from the sibling extract_semantic_dom.

  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 when to use this tool (for post-interaction UI) and provides guidance on deriving action locators from a prior extract_semantic_dom call. It also notes the allowlisted host requirement. However, it does not explicitly state when not to use it or compare to other siblings like check_auth or list_frames.

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

• check_authA
  4.9/5.0
  Behavior5/5

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

  Discloses key behaviors: navigates with QA_MCP_STORAGE_STATE session, checks for login-looking path, reports result. With no annotations, the description fully informs about nondestructive diagnostic nature.

  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 'Diagnostic', no wasted words. Every sentence adds essential 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 the simplicity of the tool, the description covers all needed aspects: function, when to use, and behavioral traits. No missing details.

  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 covers both parameters comprehensively (100%). The description adds context about session usage and the purpose of the URL parameter, providing incremental value beyond 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 function: it navigates with a session and checks for login page bounce. It distinguishes itself from sibling extraction tools by being a diagnostic for session expiration.

  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?

  Explicitly specifies use case: 'when extractions unexpectedly return login forms instead of the requested page.' No ambiguity about when to apply.

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