Server Quality Checklist

Tool Scores

• groundlens_sgiA
  4.1/5.0
  Behavior5/5

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

  The description goes beyond annotations by disclosing a critical behavioral trait: the tool measures provenance and can pass answers that borrow vocabulary but change facts. It advises surfacing the 'handoff' field and escalating fact checks, adding valuable context beyond the readOnlyHint, idempotentHint, and destructiveHint 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 a clear purpose statement upfront, followed by explanation of SGI, usage importance, and examples. It is slightly verbose (e.g., the ratio formula could be concise), but overall efficient and front-loaded.

  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 tool's simplicity (3 inputs, clear output schema present), the description fully covers its purpose, usage, return value (JSON with CHECK, score, distances), and example use cases. No gaps are evident.

  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 input schema already provides detailed descriptions for all three parameters (question, context, response). The description repeats this info with minimal added value ('Args: params (SGIInput): ...'), so it does not significantly enhance parameter understanding beyond the schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool checks whether an LLM response engaged a source document, with emphasis on provenance. However, it does not explicitly distinguish from sibling tools (groundlens_check, groundlens_dgi), though it implies a unique use case for RAG pipeline verification.

  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 on when to use this tool ('for RAG pipeline verification') and hints at when not to (escalate fact-level verification elsewhere). However, it does not explicitly name alternative tools, missing an opportunity to differentiate from siblings.

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

• groundlens_dgiA
  4.4/5.0
  Behavior4/5

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

  Discloses limitations: 'weakest signal', measured AUROC ceiling 0.606, ranking signal, not detector. Adds behavioral context beyond annotations (readOnlyHint, idempotentHint).

  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?

  Well-structured with sections, front-loaded summary, and each sentence adds value. Slightly verbose but efficient overall.

  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?

  Covers use cases, limitations, and return format. Output schema exists, so return details are optional. Given tool complexity, description is complete and appropriate.

  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?

  Description mentions 'params (DGIInput): The question and LLM response' but adds little beyond schema. Schema descriptions already cover parameters, so no major gap, but description doesn't compensate for low schema_description_coverage (0%).

  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: 'coarse context-free grounding signal' for when no source is available, and distinguishes it from sibling tools by recommending groundlens_sgi when a source exists.

  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 states when to use (no source available), when not to use ('Never report as evidence'), and suggests alternative (groundlens_sgi).

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

• groundlens_checkA
  4.8/5.0
  Behavior5/5

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

  Disclosures beyond annotations include deterministic nature, no model in scoring path, and limitation that plausible wrong facts can pass. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

  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, examples, and warnings. It is slightly lengthy but every sentence adds value. Front-loaded with core purpose.

  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 complexity (two methods, output with multiple fields) and available annotations/schema, the description provides complete guidance on behavior, limitations, and expected output. No output schema provided but output fields are explained.

  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 descriptions cover individual fields (question, response, context), the description adds meaning about parameter behavior (auto-mode selection based on context presence) and output interpretation. The 0% schema coverage for the top-level params property is compensated by rich tool description.

  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 explicitly states the tool checks grounding against a source and distinguishes itself from siblings (groundlens_dgi, groundlens_sgi) by being the main tool that auto-selects method. It clearly identifies the verbs 'check' and 'drawn from'.

  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?

  Provides explicit guidance on when to use context (SGI) vs without (DGI), and warns about interpretation of results. Includes examples of appropriate use cases and alternative tools.

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