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 4.3/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one for H1B visa salary data and one for general salary data. An agent can easily choose the correct tool based on the context without confusion.
Both tools follow a consistent `search_` prefix and snake_case naming convention, making them predictable and easy to understand.
With only 2 tools, the server is very focused but adequately covers its stated purpose of salary data. It is slightly sparse but not incomplete.
The pair covers the primary salary domains (H1B and general). Minor gaps exist, such as missing tools for filtering by industry or time trends, but these are not essential for basic use.
Available Tools
2 toolssearch_h1b_salariesARead-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?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description's behavioral burden is reduced. The description adds return field details but does not elaborate on limitations such as data recency or completeness. It is adequate but not enhanced beyond 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 three sentences: one for action and resource, one for returned data, one for use cases. Every sentence adds value without redundancy or unnecessary detail. Highly concise and well-structured.
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 the tool has 3 optional parameters, no output schema, and no nested objects, the description covers all essential information: what the tool does, what it returns, and when to use it. No gaps are evident for an AI agent to make informed decisions.
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% with each parameter described (company, location, job_title). The description lists return fields but adds no additional parameter-level details or constraints beyond what the schema provides. 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 clearly states the action (Search), the resource (U.S. H1B visa salary database), and the returned data items (employer name, job title, etc.). It also distinguishes from sibling 'search_salaries' by focusing on H1B visa-specific data.
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 provides explicit usage scenarios (understanding visa compensation trends, benchmarking tech salaries, researching employer sponsorship patterns), giving clear context for when to use. However, it does not explicitly state when not to use or compare with the sibling tool 'search_salaries', which slightly limits guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_salariesARead-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?
Description details return fields (average, range, count, median) beyond the readOnlyHint and openWorldHint annotations. No contradictions; fully transparent about read-only nature and data scope.
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 describes functionality, second provides use cases. No redundant words, front-loaded, highly efficient.
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 simple read-only query tool with 2 parameters and no output schema, the description covers purpose, return fields, and usage context completely. No gaps.
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 descriptions cover both parameters (100% coverage), so baseline is 3. The description adds little extra meaning beyond restating that the query is by job title and location, which is already implied.
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, with specific verb 'query' and resource 'salary data'. Distinguishes from sibling 'search_h1b_salaries' by indicating it returns 'general' salary data, implying non-H1B specific.
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: career planning, negotiation benchmarking, compensation analysis. Does not explicitly mention when not to use or name alternatives, but the context is clear enough for an agent to infer general-purpose use.
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
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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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