Server Quality Checklist

Tool Scores

• parse_documentC
  2.7/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool 'preserves the original formatting and layout' and converts to 'a structured format', it lacks critical behavioral details such as API rate limits, authentication requirements, error handling, or what the output looks like (since there's no output schema).

  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 and appropriately sized with four sentences. It front-loads the core purpose and efficiently lists supported file formats. There's minimal redundancy, though the file format list could be more concise.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity of a parsing tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on parameter usage, behavioral constraints, and output expectations, leaving significant gaps for an AI agent to use it correctly.

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

  Parameters2/5

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

  With 0% schema description coverage for 2 parameters, the description fails to compensate. It doesn't explain what 'file_path' should contain (e.g., local path, URL, supported file systems) or what 'output_formats' are (e.g., JSON, XML, specific structured formats). The mention of 'structured format' is vague and doesn't map to parameters.

  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's purpose: 'Parse a document using Upstage AI's document digitization API' and 'extracts the structure and content from various document types'. It specifies the verb ('parse', 'extracts') and resource ('document'), but doesn't explicitly differentiate from sibling tools like 'classify_document' or 'extract_information'.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus the sibling tools ('classify_document', 'extract_information', 'generate_schema'). It mentions what the tool does but offers no context about appropriate use cases, prerequisites, or alternatives.

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

• classify_documentA
  4.3/5.0
  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 of behavioral disclosure. It effectively adds context beyond basic functionality: it specifies supported file formats (JPEG, PNG, etc.), max file size (50MB), max pages (100), and details about default and custom categories. However, it does not mention rate limits, authentication needs, or error handling.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness3/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is appropriately sized but could be more front-loaded. Key information like supported formats and limits is included, but the extensive listing of default categories and custom schema examples, while useful, makes it slightly verbose. Every sentence earns its place, but structure could be tighter.

  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 (3 parameters, 0% schema coverage, no output schema, no annotations), the description is largely complete. It covers purpose, usage, parameters, and behavioral details like file constraints. However, it lacks information on output format or error responses, which would enhance completeness.

  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?

  The schema description coverage is 0%, so the description must compensate. It thoroughly explains the parameters: 'file_path' for the document to classify, 'schema_path' and 'schema_json' for custom categories, with detailed examples and formatting guidelines. This adds significant meaning beyond the bare input 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: 'Classify a document into predefined categories using Upstage AI's document classification API.' It specifies the verb ('classify'), resource ('document'), and distinguishes it from siblings like 'extract_information' or 'parse_document' by focusing on categorization rather than data extraction or parsing.

  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: for document classification into categories like invoice, receipt, contract, etc. It mentions using default or custom categories, but does not explicitly state when to choose this over sibling tools (e.g., 'extract_information') or when not to use it (e.g., for non-document files).

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

• extract_informationA
  4.6/5.0
  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 of behavioral disclosure and does so effectively. It describes the tool's capabilities (extraction from multiple document types without pre-training), constraints (file formats, size limits, page limits), and operational modes (schema-provided vs auto-generated). It doesn't mention rate limits or authentication requirements, but covers the core behavioral aspects well for a tool with no 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 appropriately sized and well-structured, starting with the core purpose, then usage guidelines, technical constraints, schema format details, and an example. Every section earns its place, though the schema format explanation is quite detailed (which is necessary given the complexity). It could be slightly more front-loaded with the most critical information.

  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 tool's complexity (4 parameters with 0% schema coverage, no annotations, no output schema), the description provides comprehensive context about what the tool does, how to use it, technical constraints, and parameter semantics. The main gap is the lack of information about return values or output format, which would be helpful since there's no output schema. However, it covers most other aspects thoroughly.

  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 4 parameters, the description compensates excellently by explaining all parameters' purposes and relationships. It clarifies that file_path is required, explains the schema_path vs schema_json options, details the auto_generate_schema default and behavior, and provides a comprehensive schema format example with concrete syntax. This adds substantial meaning beyond the bare input 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 specific action ('extract structured information'), the resource ('documents'), and the technology used ('Upstage Universal Information Extraction'). It distinguishes from sibling tools like classify_document, generate_schema, and parse_document by focusing specifically on information extraction rather than classification, schema generation, or general parsing.

  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?

  The description provides explicit guidance on when to use this tool vs alternatives: it explains the two modes (schema-provided vs auto-generated) and mentions sibling tools like generate_schema that could be alternatives for schema creation. It also specifies technical constraints (file formats, size limits, page limits) that help determine when the tool is applicable.

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

• generate_schemaA
  4.6/5.0
  Behavior4/5

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

  Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: supported file formats (JPEG, PNG, etc.), constraints (max file size: 50MB, max pages: 100), and the return format (both a readable schema object and a schema_json string). However, it lacks details on error handling, rate limits, or authentication needs, which would be beneficial for a tool with no annotations.

  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, starting with the core purpose. Each sentence adds value: the first explains the tool's function, the second details usage scenarios, the third lists technical constraints, and the fourth describes the output. There is no redundant or wasted information.

  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 (AI-based schema generation), no annotations, no output schema, and 0% schema coverage, the description does a good job by covering purpose, usage, constraints, and output. However, it could be more complete by including error cases, example outputs, or more details on the schema structure, which would help an agent use it effectively in varied contexts.

  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?

  With 0% schema description coverage and 1 parameter (file_path), the description must compensate. It adds significant meaning by specifying supported file formats and constraints (max file size: 50MB, max pages: 100), which clarifies the expected input beyond the basic schema. However, it doesn't detail the file_path format or examples, leaving some ambiguity.

  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: 'Generate an extraction schema for a document using Upstage AI's schema generation API.' It specifies the verb ('generate'), resource ('extraction schema'), and distinguishes from siblings by mentioning its output is used with 'extract_information' when 'auto_generate_schema' is false, unlike classify_document or parse_document.

  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?

  The description explicitly states when to use this tool: 'This is useful when you want to: - Create a reusable schema for multiple similar documents - Have more control over the extraction fields - Ensure consistent field naming and structure across extractions.' It also mentions the alternative: using the generated schema with 'extract_information' when 'auto_generate_schema' is false, providing clear context for tool selection.

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