Server Quality Checklist

Tool Scores

• extract_from_textA
  4.1/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context such as mapping extractions to source locations for grounding, default values for parameters, and error handling (Raises: ToolError). However, it does not cover aspects like rate limits, authentication needs, or performance characteristics, leaving some behavioral gaps.

  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, parameter explanations, and return/error details. It is appropriately sized and front-loaded, with most critical information (purpose and key parameters) presented early. Some minor verbosity exists in parameter descriptions, but overall it earns its place efficiently.

  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 (8 parameters, no annotations, but has output schema), the description is largely complete. It covers purpose, parameters, returns, and errors. With an output schema present, it need not explain return values in detail, but it could improve by addressing sibling tool differentiation more explicitly. The gaps are minor relative to the tool's scope.

  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 description coverage is 0%, so the description must compensate. It provides detailed semantics for all 8 parameters, explaining their purposes (e.g., 'text: The text to extract information from'), default values, and ranges (e.g., 'temperature: Sampling temperature 0.0-1.0'). This adds significant meaning beyond the basic schema, though it could be more explicit about parameter interactions.

  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 as extracting structured information from unstructured text using Large Language Models, specifying the method (langextract), and distinguishing it from sibling tools like extract_from_url by focusing on text input rather than URLs. It provides a specific verb ('extract') and resource ('structured information from text').

  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 usage context by mentioning 'user-defined instructions and examples' and 'unstructured text,' but does not explicitly state when to use this tool versus alternatives like extract_from_url or generate_visualization. It provides clear context for extraction tasks but lacks explicit exclusions or comparisons with siblings.

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

• save_extraction_resultsA
  4.1/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It discloses that the tool saves to a file, returns a dictionary with file path and confirmation, and raises ToolError on failure. However, it lacks details on permissions needed, file overwriting behavior, or rate limits. The description does not contradict annotations (none provided).

  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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by supporting details. The Args and Returns sections are structured but slightly verbose; every sentence adds value, though some redundancy exists (e.g., repeating JSONL format).

  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 parameters with 0% schema coverage, no annotations, and an output schema (implied by Returns), the description is fairly complete. It explains parameters, return values, and errors, but could improve by detailing file naming conventions or visualization integration. The output schema reduces the need to fully explain returns.

  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 description coverage is 0%, so the description must compensate. It adds meaning beyond the schema by explaining extraction_results comes from specific sibling tools, output_name excludes the .jsonl extension, and output_dir defaults to current directory. This covers all 3 parameters, though it could provide more detail on format or constraints.

  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 specific action ('Save extraction results') and resource ('to a JSONL file'), distinguishing it from sibling tools like extract_from_text, extract_from_url, and generate_visualization. It explicitly mentions the format (JSONL) and purpose (for later use or visualization), avoiding tautology with the tool name.

  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 context on when to use this tool: after extraction from text or URL, for saving results to a file. It implies usage by referencing sibling tools (extract_from_text, extract_from_url) as sources for the extraction_results parameter. However, it does not explicitly state when not to use it or name alternatives (e.g., vs. generate_visualization).

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

• generate_visualizationA
  4.4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it creates a self-contained HTML file, handles thousands of entities, includes color coding and hover details, and mentions error handling via ToolError. It doesn't cover aspects like performance, file size limits, or specific visual features, but provides substantial operational context.

  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 well-structured and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds value: capabilities, parameter details, return values, and error handling. There's no wasted text, and the bullet-like formatting for Args/Returns/Raises enhances readability without verbosity.

  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 moderate complexity (2 parameters, no annotations, but with output schema), the description is complete. It covers purpose, behavior, parameters, returns, and errors. The output schema existence means return values don't need detailed explanation in the description, and the description provides all necessary context for effective use.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 0%, so the description must compensate fully. It clearly explains both parameters: jsonl_file_path as 'Path to the JSONL file containing extraction results' and output_html_path as 'Optional path for the HTML output (default: auto-generated)'. This adds essential meaning beyond the bare schema, covering purpose, format, and default behavior.

  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 specific action ('Generate interactive HTML visualization') and resource ('from extraction results'), distinguishing it from sibling tools like extract_from_text/url (which extract) and save_extraction_results (which saves). It specifies the visualization type, content, and capabilities like handling thousands of entities with color coding and hover details.

  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 context by mentioning 'extraction results' and referencing JSONL files, suggesting it should be used after extraction tools. However, it doesn't explicitly state when to use this vs. alternatives or provide any exclusions. The connection to extraction is clear but not explicitly framed as guidance.

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

• extract_from_urlA
  4.5/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it downloads text from URLs, uses LLMs for extraction, mentions error conditions (invalid URL, download failure, extraction failure), describes the return format (dictionary with entities, source locations, metadata), and mentions parallel processing capability (max_workers). It doesn't cover rate limits or authentication requirements, but provides substantial behavioral context.

  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 clear sections (purpose, ideal use cases, Args, Returns, Raises) and front-loads the core functionality. While comprehensive, some sentences could be more concise (e.g., the second sentence could be merged with the first). Overall, it's appropriately sized for an 8-parameter tool with no annotations.

  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 complexity (8 parameters, no annotations, but has output schema), the description is remarkably complete. It covers purpose, usage context, all parameter semantics, return format, error conditions, and behavioral details. The presence of an output schema means the description doesn't need to exhaustively document return values, and it provides everything else needed for effective use.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  With 0% schema description coverage for 8 parameters, the description fully compensates by providing detailed semantic explanations for every parameter in the Args section. Each parameter gets clear meaning beyond just the schema's type information, explaining what 'prompt_description', 'examples', 'model_id', 'max_char_buffer', 'temperature', 'extraction_passes', and 'max_workers' actually do in the extraction context.

  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 with specific verbs ('extract structured information from text content at a URL') and distinguishes it from sibling tools by specifying it works with URLs rather than raw text (vs extract_from_text). It identifies the resource (text content at a URL) and method (using LLMs).

  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 context for when to use this tool ('ideal for processing web articles, documents, or any text content accessible via HTTP/HTTPS'), which implicitly distinguishes it from extract_from_text that works with raw text. However, it doesn't explicitly state when NOT to use it or name specific alternatives beyond the sibling tool names provided.

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