Server Quality Checklist

Tool Scores

• find_matching_linkedin_jobsD
  1.3/5.0
  Behavior1/5

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

  With no annotations provided, the description must disclose behavior. It only vaguely hints at 'local matching' and 'public LinkedIn job data', but does not explain whether this is a read-only operation, whether it makes network calls, what side effects exist, or any required authorization. This is severely insufficient.

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

  Conciseness2/5

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

  The description is a single short sentence, so it is concise in length, but it is under-specified to the point of being unhelpful. It does not front-load any concrete information beyond a vague action and resource.

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

  Completeness1/5

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

  Despite having an output schema and five parameters, the description lacks almost any context about the tool's behavior, configuration requirements, or relationship to siblings. It is completely inadequate for an agent to select and invoke the tool correctly.

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

  Parameters1/5

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

  Schema description coverage is 0%, and the description mentions none of the five parameters. It does not explain how 'location', 'max_jobs', 'date_posted', 'max_queries', or 'limit_per_query' affect the matching behavior. The description adds no meaningful parameter information.

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

  Purpose2/5

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

  The description uses a vague verb 'Run' and an unclear resource 'configured local matching over public LinkedIn job data'. It does not clearly state what the tool actually does (e.g., finds matching jobs, returns a list of matches) nor how it differs from sibling tools like search_linkedin_jobs.

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

  Usage Guidelines1/5

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

  There is no guidance on when to use this tool versus the sibling tools. It does not mention any prerequisites, configuration steps, or situations where this tool is preferred.

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

• search_linkedin_jobsC
  2.4/5.0
  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 does mention 'guest-accessible' and 'bounded', hinting at authentication requirements and limits, but these are vague and unquantified. It doesn't reveal how pagination works, whether filters mutate the search, or any rate-limiting considerations. The description adds some but insufficient transparency.

  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 a single short sentence with no wasted words, which is concise. However, it lacks any structure or elaboration, making it minimally viable at best. It is not front-loaded with the most critical information (e.g., key filters or limitations), so it doesn't earn its place as a standalone guide.

  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?

  For a tool with 11 parameters and a search function, the description is grossly incomplete. It doesn't explain the search behavior, available filters (job_type, experience_level, etc.), pagination controls, or the meaning of 'bounded'. Although an output schema exists, the description still needs to clarify scope and usage context, which it mostly fails to do.

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

  Parameters1/5

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

  The schema has 11 parameters with 0% description coverage; the description provides no explanation of any parameter. While the schema includes enums and defaults, it lacks human-readable semantics (e.g., what 'distance' means in this context, how 'exclude_job_ids' is used). The description completely fails to compensate for the low coverage, leaving the agent without guidance on parameter intent or relationships.

  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 specifies the action ('Search') and resource ('public LinkedIn job data'), making the basic purpose clear. However, it doesn't distinguish this from sibling tools like 'find_matching_linkedin_jobs', which likely performs a similar search function. The qualifiers 'bounded guest-accessible' add some context but don't clarify the unique scope.

  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?

  No guidance is provided on when to use this tool versus alternatives. The description merely states what it does, with no mention of exclusions, prerequisites, or scenarios where other sibling tools would be preferred. This is a clear gap given the existence of overlapping tools like 'find_matching_linkedin_jobs'.

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

• get_linkedin_job_raw_payloadC
  2.6/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'bounded in-memory public', hinting at caching or limits, but does not clarify the behavior, failure modes, authentication requirements, or output structure. The term 'bounded' is ambiguous.

  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 a single, short sentence, making it concise, but the ambiguous phrasing 'bounded in-memory public' reduces clarity. It is not overly verbose, yet it under-specifies important details, leaving the description less effective than it could be.

  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?

  For a tool with a single parameter and an output schema, the description is too sparse. It offers no context on when to use this over alternatives, what 'raw payload' entails, or any limitations. The presence of sibling tools and output schema does not mitigate the lack of clear purpose and usage guidance.

  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?

  The only parameter, job_id_or_url, is somewhat self-explanatory from its name, but the description does not elaborate on accepted formats (e.g., numeric ID vs full URL) or usage. With 0% schema description coverage, the description should compensate, but it remains silent on parameter semantics.

  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 uses the verb 'Get' and identifies the resource as a 'raw payload', clearly indicating its purpose. It distinguishes from sibling tools like get_linkedin_job_details by emphasizing 'raw payload' and 'in-memory', but the term 'bounded' is vague and not fully explained.

  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?

  No guidance is provided on when to use this tool versus alternatives such as search_linkedin_jobs, get_linkedin_job_details, or find_matching_linkedin_jobs. The description only states what it does, with no context or exclusions.

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

• get_linkedin_job_detailsB
  3.1/5.0
  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. It only says 'normalized details' but does not disclose what normalization entails, potential error conditions, authentication requirements, or rate limits. This leaves significant behavioral ambiguity.

  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 a single, concise sentence that directly states the tool's purpose. There is no unnecessary verbiage, and it is appropriately front-loaded.

  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?

  For a simple tool with one parameter and an output schema, the description covers the basics, but it lacks clarity on what constitutes a 'public' job fails or whether there are any restrictions. The output schema likely covers return values, but the absence of any behavioral or usage context makes it only minimally complete.

  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?

  The single parameter 'job_id_or_url' is self-explanatory to some degree, but schema description coverage is 0% and the description adds nothing about accepted formats, examples, or the difference between an ID and a URL. The agent must infer these details from the parameter name alone.

  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 verb ('Get') and resource ('normalized details for one public LinkedIn job'), which distinguishes it from search and raw-payload tools. However, it does not explicitly contrast with sibling tools within the description itself, so it falls just short of a 5.

  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?

  Usage context is implied: use this tool to fetch details for a single job. But there is no explicit guidance on when to use this instead of search_linkedin_jobs or get_linkedin_job_raw_payload, nor any exclusions or alternatives mentioned.

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