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calvinlee326

h1b-sponsor-mcp

by calvinlee326
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches by company name, the other ranks sponsors by state. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun snake_case pattern (search_sponsors, get_top_sponsors_by_state), which is predictable and clear.

    Tool Count3/5

    With only 2 tools, the server feels thin for the stated purpose of H-1B sponsor data. While the tools are focused, a more complete service would typically have 5-10 tools.

    Completeness2/5

    The surface lacks essential operations such as fetching detailed sponsor records, filtering by industry or year, or listing all sponsors. Only basic search and state ranking are provided, leaving significant gaps.

  • Average 4.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

    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 discloses key behaviors: case/punctuation insensitivity, fiscal year coverage, aggregation across offices, and ranking by total approvals. This is sufficient for understanding the tool's operation.

    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 concise and well-structured, with two paragraphs. Each sentence adds value, and no extraneous information is present.

    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?

    The tool has a moderate complexity and an output schema (not shown), so return values are covered. The description explains matching behavior, aggregation, ranking, and parameters, making it complete for a search tool.

    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?

    Schema description coverage is 0%, so the description must compensate. It adds meaning by describing 'company_name' as a full or partial name with examples, and 'limit' with a default (10) and range (1-50), which are not in the schema.

    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 finds H-1B sponsoring employers by name, specifying the data range (2024-2026) and aggregation across offices. It distinguishes from the sibling tool 'get_top_sponsors_by_state' by focusing on name-based search.

    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 the tool (searching by company name) with examples. It does not explicitly state when not to use or list alternative tools, but the sibling tool name hints at a different use case.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavioral traits: it counts only petitions from offices in the given state (local figures, not company-wide), and covers fiscal years 2024-2026. This adds critical context beyond what the schema provides.

    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 front-loaded with the core purpose, followed by behavioral nuances, then clear arg explanations. Every sentence is informative and no fluff, making it efficient and easy to parse.

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

    Completeness5/5

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

    Given the tool's simplicity (2 parameters, 0% schema coverage, output schema present), the description is complete: it explains what the tool does, its behavioral scope, and parameter details. The agent has sufficient information to use the tool correctly.

    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?

    The schema has 0% description coverage, so the description must compensate. It thoroughly explains the state parameter with examples of USPS codes and territories, and the limit parameter with the allowed range (1-50). This adds substantial meaning beyond the bare schema types.

    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 ranks the largest H-1B sponsors in a US state by approved petitions, with specific verb (rank) and resource (sponsors by state), distinguishing it from the sibling tool search_sponsors which likely searches sponsors without state-specific ranking.

    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 that the tool is for state-specific rankings counting local petitions, implicitly differentiating from search_sponsors which would cover broader searches. However, it lacks explicit guidance on when to use this tool versus the sibling, and does not state any prerequisites or when not to use it.

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

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