MCP server for HR and compensation data including salary benchmarks, job listings, company reviews, and labor market insights for AI agents.
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Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.2/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one focuses on H1B visa salary data, the other on general salary data. Descriptions make the difference explicit, so an agent can easily select the correct tool.
Both tools follow a consistent 'search_' prefix pattern with clear, descriptive nouns (h1b_salaries, salaries). The naming convention is uniform and predictable.
With only 2 tools for a HR & compensation domain, the set feels thin. While each tool serves a specific purpose, additional tools (e.g., for benefits, cost-of-living, or industry breakdowns) would be expected for comprehensive coverage.
The tools cover general and visa salary data, but lack other common compensation-related queries such as experience-level filtering, benefits information, or historical trends. This creates notable gaps for thorough analysis.
search_h1b_salariesASearch the U.S. H1B visa salary database for sponsored employment data. Returns employer name, job title, approved salary, visa year, work location (city/state), and visa status. Use for understanding visa compensation trends, benchmarking tech salaries, or researching employer sponsorship patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| company | No | Company name or partial name (e.g. 'Google', 'Meta', 'Apple') | |
| location | No | Work location as city or state (e.g. 'San Francisco, CA', 'Seattle, WA', 'New York') | |
| job_title | No | Job title to search (e.g. 'Software Engineer', 'Data Scientist', 'Product Manager') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and open-world behavior; description adds detail on specific returned fields and intended use, complementing annotations without contradicting them.
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 sentences: first explains action and output, second provides use cases; front-loaded and concise, no wasted words.
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?
Covers purpose, return fields, and usage; but lacks details on result size, ordering, pagination, or error handling, which would improve completeness.
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 covers all three parameters with descriptions; description does not add extra semantics beyond reinforcing the general purpose of the tool.
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, lists returned fields (employer name, job title, approved salary, etc.), and implies differentiation from sibling 'search_salaries' by specifying H1B context.
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?
Provides explicit use cases (understanding visa compensation trends, benchmarking tech salaries, researching sponsorship patterns) but does not directly compare with sibling tool or mention when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_salariesAQuery general salary data by job title and geographic location. Returns average salary, salary range, number of data points, and median compensation. Use for career planning, negotiation benchmarking, or compensation analysis across roles and regions.
| Name | Required | Description | Default |
|---|---|---|---|
| location | No | Geographic location for salary lookup (e.g. 'San Francisco, CA', 'remote', 'United States') | |
| job_title | Yes | Job position or role (e.g. 'Senior Software Engineer', 'UX Designer', 'DevOps Engineer') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint and openWorldHint; description adds detail on return data (average, range, count, median). No mention of rate limits or data freshness, but overall good transparency.
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 concise sentences front-loaded with purpose, followed by return details and use cases. No wasted words.
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?
Given simplicity (2 params, no output schema, good annotations), description is adequately complete. Covers purpose, output, and use cases. Missing data source info but not critical for a query tool.
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 100% (both parameters have descriptions). Description does not add new parameter semantics beyond schema, so 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?
Clearly states it queries salary data by job title and location, distinguishes from sibling search_h1b_salaries by specifying 'general salary data', and lists specific return fields.
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?
Explicitly mentions use cases (career planning, negotiation benchmarking, compensation analysis) and implies when to use vs sibling via 'general' vs H1B-specific, but lacks explicit exclusion or alternative naming.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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