job-market-mcp-server

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful return-content details (ratings, pros, cons, salary data) and the max_results default, but does not disclose potential rate limits, pagination behavior, or dependency on live external data. Thus, it adds some value beyond annotations but falls short of rich behavioral disclosure.

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

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

The description is concise and well-structured: a single-sentence overview followed by an Args block with clear parameter explanations. Every sentence adds value without redundancy.

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

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

With no output schema, the description must convey what to expect from the call. It does list the key return fields (ratings, pros, cons, salary data) and parameter behavior. It does not mention edge cases like unrecognized company names or empty results, but for a simple read-only tool with two parameters, this is reasonably complete.

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

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

Schema description coverage is 0%, so the description carries the full burden of explaining parameters. It does so effectively: 'company' is explained with an example, and 'max_results' states its default and meaning ('Max reviews'). This fully compensates for the lack of schema descriptions.

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

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

The description clearly states the tool's function: 'Get Glassdoor reviews for a company.' It specifies the resource (Glassdoor reviews) and lists the return types (ratings, pros, cons, salary data). This distinguishes it from sibling tools like salary_search and search_jobs, which focus on different data types.

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

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

The description gives clear context for when to use the tool (whenever Glassdoor reviews are needed) but does not explicitly mention alternatives or exclusions. While the sibling tools are visible in the environment, the description itself does not address when not to use this tool, leaving this dimension slightly below 5.

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

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

The annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the return fields (employer, salary, location) and the arg list, but does not disclose deeper behavioral traits like data source limitations, pagination, or precision. This is consistent with annotations, so no contradiction.

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

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

The description is extremely concise: a one-sentence purpose, a one-line return summary, and a compact Args list. Every sentence earns its place, and the most important information is front-loaded.

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

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

The tool is simple, has no output schema, and the description explains what it returns (employer, salary, location). It also specifies parameter optionality. Some minor gaps remain (e.g., result count, data source specifics), but given the low complexity and good annotations, this is nearly complete.

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

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 does: job_title is explained with an example ('Data Scientist') and location is described as 'City or state (optional)'. This adds meaning beyond the raw schema, though it could give more examples or format constraints.

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

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

The description clearly states the tool searches H1B visa salary data with a specific verb ('Search') and resource ('H1B visa salary data'), and explicitly lists the return fields (employer, salary, location). This distinguishes it from sibling tools like company_reviews and search_jobs, which serve different purposes.

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

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

The description implies when to use the tool (when you need salary data for a job title) and provides examples for job_title and location. However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a full 5.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, reducing the burden. The description adds value by specifying the return fields (titles, companies, salaries, descriptions) and mention of 'Indeed' as an external source. It does not cover pagination or rate limits, but the annotations cover safety, so the additional context justifies a solid score.

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

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

The description is extremely concise, with a clear first sentence stating the purpose and a structured Args block listing parameters. Every sentence adds value, with no filler or redundancy. The format is easy to scan and understand.

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

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

Despite being a simple tool with no output schema, the description covers purpose, parameters, and return values. The context signals show only 3 parameters and 1 required, which the description explains well. For such a tool, this is a complete and self-contained description.

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

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

The schema has no descriptions for parameters (0% coverage), so the description carries the full burden. It provides meaningful examples and defaults: query as 'Job title or keywords (e.g. 'software engineer')', location as optional 'City or state', and max_results with default 20. This fully compensates for the schema's lack of detail.

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

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

The description clearly states the tool 'Search Indeed for job listings' with a specific verb and resource. It distinguishes from sibling tools by focusing on job listings, even though it also returns salaries, which is a secondary feature. The explicit return fields further clarify its purpose.

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

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

The description implies usage for searching jobs but does not explicitly state when to prefer this over the alternative tools (company_reviews, salary_search). The overlap in returning salaries could cause confusion, but the primary focus on job listings gives some implicit guidance. No explicit exclusions or alternatives are mentioned.

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