US H-1B Visa Sponsors MCP

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

No annotations, so the description carries the full behavioral burden. 'PRO' hints at a paid/subscriber requirement and the access_token schema description corroborates that, but there is no mention of rate limits, cost, result size, or pagination for what is described as 'all rows' for an employer.

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?

Extremely short with zero filler, and the output granularity is front-loaded. However, the 'PRO.' token is unexplained jargon that consumes attention without conveying behavior, and the brevity comes at the cost of specification.

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 two-parameter lookup with no annotations and no output schema, the agent still cannot tell the data source, whether results are paginated or capped, or what 'approval rate' means. More disclosure is required before an agent can invoke this with confidence.

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 coverage is 50%: access_token is documented in the schema, while 'employer' has no description. The description implies the employer argument drives the lookup but never says whether it is a name, ticker, or ID, or whether matching is exact or fuzzy — the same coverage gap remains.

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?

It identifies the resource (an employer's records) and the output granularity (each location/tax ID) plus the enrichment (approval rate), which is more than a restatement of the name. But 'rows' is never defined — the agent must guess the data domain (H-1B/LCA filings implied only by siblings) and there is no explicit differentiation from h1b_check or sponsor_search.

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?

No statement of when to use this instead of h1b_check or sponsor_search, and no guidance on prerequisites beyond the opaque 'PRO' prefix. The agent must infer from context that this is the detailed, subscription-gated alternative.

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?

No annotations exist, so the description carries the full burden. It usefully discloses cost ('FREE'), data provenance (USCIS Employer Data Hub), and that results are capped at the top 3 matches, but says nothing about authentication needs, matching behavior (exact vs fuzzy), or what is returned when no match is found.

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?

Two compact sentences with the core question front-loaded, followed by provenance and result shape. 'FREE.' is short but does consume the lead position with a billing caveat rather than the capability statement.

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 annotations, no output schema, and an undocumented parameter, the description does enough to convey what comes back (approvals/denials for top 3 matches). It is still thin on input expectations and on the relationship to the two sibling tools, leaving an agent to guess which tool fits a given request.

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 coverage is 0% for the single 'employer' parameter, so the description must compensate. It implies the value is a US employer name and that matching is approximate ('top 3 matches'), but it does not clarify expected formatting, partial-name tolerance, or whether multiple employers can be supplied.

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?

States a specific question the tool answers (does this US employer sponsor H-1B visas) plus the data source (USCIS H-1B Employer Data Hub) and the result shape (approvals/denials, top 3 matches). It does not explicitly differentiate itself from siblings employer_detail or sponsor_search, so it stops 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.

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

Usage is implied (quick check of a named employer) but there is no statement of when to use this versus sponsor_search or employer_detail, and no guidance on what happens when the employer name is ambiguous or absent from the data. Only the 'top 3 matches' hint suggests a fuzzy lookup use case.

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?

With no annotations, the description carries the full burden. It usefully discloses the result ordering ('ranked by approvals') and that this is a PRO/subscriber-gated feature, hinting at the access_token requirement. It says nothing about read-only safety, result volume, pagination, or what happens with zero filters despite all parameters being optional.

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?

A single tight sentence with no waste, and the core capability is front-loaded before the audience note. The telegraphic 'PRO.' fragment is slightly cryptic but costs little space.

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 no-output-schema, no-annotation, all-optional search tool, the description covers what it returns conceptually (ranked sponsors) and who it's for. It stops short of describing response fields or behavior with no filters, but the essentials for correct invocation are largely present.

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 coverage is only 17%, so the description must compensate. It clarifies that 'naics' is an industry *prefix* and that state/city are filters, adding real meaning for three of six params. But limit, min_approvals, and the relationship between min_approvals and 'ranked by approvals' are left unexplained.

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?

States the resource (H-1B sponsors), the filter axes (state, city, NAICS prefix), and the ranking (by approvals), so an agent knows this is a ranked sponsor lookup. It doesn't explicitly distinguish itself from employer_detail or h1b_check, but 'lead lists' implies a broad discovery search versus a single-record lookup.

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 audience framing ('lead lists for recruiters, immigration lawyers and job-seekers') implies an exploratory/browse use case, which tacitly points away from employer_detail. However, there is no explicit when-to-use or when-not statement naming the sibling tools.

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