Server Quality Checklist

Tool Scores

• adapt_cv_to_latest_jobC
  2.9/5.0
  Behavior2/5

  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 mentions the tool adapts a CV and returns job details with an adapted CV, but lacks critical behavioral details: it doesn't specify if this is a read-only or mutation operation (though 'adapts' suggests generation, not modification), what permissions or authentication are needed, rate limits, or how the adaptation process works (e.g., AI-based, template-based). This leaves significant gaps for an agent to understand the tool's behavior.

  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: it starts with a clear purpose statement, followed by separate 'Args' and 'Returns' sections. Each sentence adds value without redundancy. However, it could be slightly more front-loaded by integrating parameter roles into the initial statement for faster comprehension.

  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 (CV adaptation tool with 3 parameters), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It covers the basic purpose and parameters but misses behavioral context (e.g., how adaptation works, side effects), detailed parameter guidance, and output specifics beyond a string return. For a tool that likely involves data processing and generation, this leaves too many unknowns for effective agent use.

  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 description includes an 'Args' section that lists the three parameters (position, location, job_id) and a 'Returns' section stating the output is a string with job details and adapted CV. However, schema description coverage is 0%, meaning the input schema provides no descriptions for parameters. The description adds basic semantics by naming the parameters and their roles, but doesn't elaborate on formats (e.g., what 'job_id' refers to), constraints, or examples, which is insufficient to fully compensate for the lack of schema documentation.

  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: adapting a specific person's CV to a job description retrieved from LinkedIn based on position, location, and job ID. It specifies the verb ('Adapts'), resource ('Francisco Perez-Sorrosal's CV'), and target ('job description retrieved from linkedin'). However, it doesn't explicitly differentiate from sibling tools like get_jobs_raw_metadata or get_new_job_ids, which appear to be related to job data retrieval rather than CV adaptation.

  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 alternatives. It doesn't mention prerequisites (e.g., needing job data from siblings), exclusions, or comparisons to other tools. The context implies it might follow job retrieval tools, but this is not stated explicitly, leaving usage unclear.

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

• get_jobs_raw_metadataB
  3.1/5.0
  Behavior2/5

  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 states the tool 'Gets' data, implying a read operation, but doesn't specify if it requires authentication, has rate limits, or what happens with invalid job IDs. This leaves significant behavioral gaps for a tool that fetches metadata.

  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, with the core purpose stated first followed by parameter and return details. It avoids unnecessary fluff, though the formatting with 'Args:' and 'Returns:' sections is slightly verbose but still efficient.

  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?

  Given the tool's complexity (1 parameter, no annotations, no output schema), the description is adequate but incomplete. It covers the basic purpose and parameter semantics but lacks behavioral details like error handling or return format specifics, making it minimally viable but with clear gaps.

  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?

  The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'job_ids' is a 'List of job IDs to get the job raw metadata for', clarifying the parameter's purpose and expected format, which compensates well for the schema's lack of descriptions.

  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 with a specific verb ('Gets') and resource ('job raw metadata'), making it easy to understand what the tool does. However, it doesn't differentiate this tool from its sibling tools like 'get_new_job_ids' or 'get_url_for_jobs_search', which prevents a perfect score.

  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 alternatives. It doesn't mention any prerequisites, context for usage, or comparisons with sibling tools like 'get_new_job_ids' or 'adapt_cv_to_latest_job', leaving the agent without clear usage direction.

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

• get_new_job_idsA
  3.8/5.0
  Behavior2/5

  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. While it describes the core operation (retrieving job IDs from LinkedIn pages), it lacks important behavioral details such as authentication requirements, rate limits, error handling, pagination mechanics, or what constitutes 'new' job IDs. The description is functional but incomplete for a tool that interacts with an external service.

  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 with three clear sections (purpose, args, returns) and front-loaded with the main functionality. The Args and Returns sections are helpful but slightly redundant with the purpose statement. Every sentence contributes value, though minor tightening is possible.

  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?

  Given the tool's complexity (interacting with LinkedIn, pagination, no output schema, and no annotations), the description is minimally adequate but has significant gaps. It explains what the tool does and its parameters but lacks details about authentication, rate limits, error conditions, return format specifics, or what distinguishes 'new' job IDs. The description meets basic requirements but doesn't fully address the operational context.

  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, the description compensates well by explaining both parameters: 'url' is described as 'The URL to search for jobs in LinkedIn' and 'num_pages' as 'The number of pages to retrieve ids from'. It adds meaningful context beyond the bare schema, though it doesn't specify URL format requirements or page number 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 tool's purpose with specific verbs ('gets', 'retrieved from', 'exploring') and resources ('new job ids', 'LinkedIn url', 'number of pages'). It distinguishes from siblings by focusing on job ID retrieval rather than CV adaptation, raw metadata fetching, or URL generation for job searches.

  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 (to get job IDs from LinkedIn URLs with pagination), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. The context is sufficient for basic usage decisions.

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

• get_url_for_jobs_searchA
  4.3/5.0
  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 adequately describes the core behavior (URL generation for LinkedIn job search) and mentions compatibility with LinkedIn's API, but lacks details about rate limits, authentication requirements, error handling, or what makes the URL 'properly encoded' beyond basic encoding. It doesn't contradict any 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 appropriately sized and well-structured with three focused sentences: purpose statement, parameter explanation, and return value. Each sentence earns its place by providing distinct information without redundancy. The Args/Returns formatting enhances clarity without unnecessary verbosity.

  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 moderate complexity (single parameter, no annotations, no output schema), the description is mostly complete. It covers purpose, parameter meaning, and return type adequately. However, it could benefit from more behavioral context (e.g., encoding specifics, error cases) since there are no annotations or output schema to fill those gaps.

  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 and only one parameter, the description adds significant value beyond the input schema. It clearly explains the 'query' parameter as 'The search query string for jobs in LinkedIn,' providing essential semantic context that the schema's title ('Query') and default value alone don't convey. This fully compensates for the schema's lack of 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 clearly states the tool's purpose with specific verbs ('Generates a properly encoded URL') and resource ('to search for jobs on LinkedIn'), distinguishing it from sibling tools like get_jobs_raw_metadata or get_new_job_ids which handle different aspects of job data. It explicitly mentions compatibility with LinkedIn's job search API, providing clear differentiation.

  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 ('to search for jobs on LinkedIn'), but does not explicitly state when not to use it or name alternatives. It implies usage for URL generation rather than direct job fetching, but lacks explicit exclusions or comparisons to sibling tools like get_jobs_raw_metadata.

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