hr-compensation-mcp-server

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

Annotations already establish readOnlyHint=true, and the description adds a valuable list of return fields (employer name, job title, approved salary, visa year, location, visa status) which is especially helpful because no output schema exists. It does not disclose details like pagination or result limits, but the added context goes beyond the annotations.

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 only two sentences, front-loaded with the action and resource, and every sentence contributes value. It avoids redundant details already present in the schema or annotations.

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 compensates by enumerating the return fields. It also suggests use cases, which adds context for the agent. However, it omits details about query combination logic, result ordering, or potential limits, which could be important for a search tool, keeping it just below 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?

All three parameters (company, location, job_title) are fully described in the input schema with examples, resulting in 100% schema coverage. The description does not add extra parameter semantics, such as how filters combine or whether partial matches are allowed, so the baseline score of 3 is appropriate.

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 uses a specific verb 'Search' and identifies a clear resource ('U.S. H1B visa salary database'). It distinguishes itself from the sibling tool 'search_salaries' by explicitly focusing on H1B sponsored employment data, making the purpose unambiguous.

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?

It provides clear use cases such as 'understanding visa compensation trends, benchmarking tech salaries, or researching employer sponsorship patterns', which tell the agent when to use it. However, it does not explicitly mention the alternative tool or provide exclusion criteria, so it falls short of a 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. The description adds behavioral context by specifying the exact return values (average salary, salary range, number of data points, median compensation), which is valuable given the absence of an output schema. No contradictions.

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 two sentences: the first states the function and key outputs; the second lists use cases. It is front-loaded, concise, and contains no redundant information.

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?

For a read-only query tool with only two parameters and no output schema, the description covers the core purpose, return values, and applicable contexts. The sibling tool context and annotations fill any remaining gaps, making the description 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 100% for both parameters, so the schema fully documents job_title and location. The description echoes these dimensions but does not add new semantic details beyond the schema, warranting the baseline 3.

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: 'Query general salary data by job title and geographic location' and lists specific return fields. The word 'general' distinguishes it from the sibling tool 'search_h1b_salaries', making the purpose unambiguous.

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 explicitly provides use cases: 'Use for career planning, negotiation benchmarking, or compensation analysis across roles and regions.' It does not explicitly name the sibling tool as an alternative, but the 'general' qualifier implies when not to use it (i.e., for H1B-specific salary data).

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