HR & Compensation Data MCP Server
Server Details
MCP server for HR and compensation data including salary benchmarks, job listings, company reviews, and labor market insights for AI agents.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
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.
Tool Definition Quality
Average 3/5 across 2 of 2 tools scored.
The two tools target distinct datasets (H1B visa vs. general market salaries) with clear boundaries; an agent would not confuse their purposes.
Both tools follow a consistent snake_case pattern 'search_[qualifier]_salaries', using identical verbs and nouns throughout.
With only 2 tools, the set feels thin for an 'HR & Compensation Data' server, though it covers basic salary lookup functionality.
Provides search capability for two salary sources but lacks read operations (e.g., get by ID), filtering options, or other HR functions implied by the server name.
Available Tools
2 toolssearch_h1b_salariesCRead-onlyInspect
Search 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') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return fields (employer, job title, salary, location) which is valuable given the lack of output schema, but omits other behavioral traits like rate limits or data freshness.
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 concise and front-loaded with purpose first, though verging on too minimal for adequate guidance.
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?
Adequate for a simple search tool by covering the return structure, but incomplete due to missing parameter semantics and usage differentiation.
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 has 0% description coverage and the description fails to compensate by explaining any of the three parameters (company, location, job_title) or their relationships.
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 searches H1B visa salary data, distinguishing it from the generic 'search_salaries' sibling, though it lacks detail on the data scope.
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 no guidance on when to use this tool versus the 'search_salaries' alternative or when to combine them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_salariesBRead-onlyInspect
Query 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') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fails to disclose behavioral traits like data sources, return format, or rate limits.
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?
Single sentence is appropriately sized for the tool's simplicity and front-loads the core functionality 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?
Covers basic purpose but lacks critical context about output format, data granularity, and scope given the absence of an output schema.
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?
Mentions both parameters (job_title, location) in context, providing minimal semantic meaning to compensate for 0% schema description coverage.
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 the tool searches general salary data by job title and location, though it does not explicitly differentiate from the H1B-specific sibling tool.
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 no guidance on when to use this tool versus search_h1b_salaries or other alternatives.
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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
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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