Search ~10,000 remote/hybrid jobs for Germany and the EU; aggregate market stats. Free, no auth.
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Usage analytics
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Average 4.4/5 across 2 of 2 tools scored.
The two tools serve clearly distinct purposes: one for aggregate statistics and one for searching jobs. There is no functional overlap or ambiguity in their use.
Both tool names follow the same verb_noun pattern (get_job_statistics, search_jobs), making the naming predictable and internally consistent.
With only two tools, the server feels thin for a job board. While the two tools cover basic search and stats, the count is on the borderline of being too minimal.
The server provides search and aggregate statistics, but lacks direct job retrieval by ID or listing of all jobs without a search query. Search likely returns results, but the surface is not fully complete for typical job board interactions.
get_job_statisticsAInspectGet aggregate statistics (active jobs, new-this-week/month, top sources, salary ranges) for nomado24's remote-jobs board. No input required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool performs a read-only operation (getting statistics) and lists the specific metrics it returns. It also states 'No input required,' setting clear expectations. There are no hidden side effects or prerequisites mentioned, but given the simplicity of the tool, this is adequate.
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 a single, concise sentence that front-loads the core purpose and includes only necessary detail. There is no waste or 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?
For a tool with zero parameters and no output schema, the description is fully complete. It specifies the data returned, the domain (nomado24's remote-jobs board), and invocation requirements (no input). No additional context is needed for an agent to use it correctly.
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?
The input schema is empty, and the description explicitly states 'No input required,' leaving no ambiguity about invocation. Given zero parameters, the description provides complete clarity beyond the schema.
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's purpose with a specific verb ('Get') and resource ('aggregate statistics ... for nomado24's remote-jobs board'). It enumerates the types of statistics (active jobs, new-this-week/month, top sources, salary ranges), which distinguishes it from the sibling tool search_jobs, presumably a job search 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?
The description indicates the tool's usage context (aggregate statistics for a remote-jobs board) and explicitly states 'No input required,' clarifying that no parameters are needed. However, it does not explicitly mention when to use this tool instead of search_jobs or provide exclusions, though the purpose makes the distinction clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_jobsAInspectSearch nomado24's remote/hybrid job board. Read-only, free, attribution required (see structuredContent.attribution).
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text search over job title/company/tags | |
| page | No | 1-based page number, default 1 | |
| language | No | Filter by job content language | |
| per_page | No | Results per page, 1-25, default 20 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly states 'Read-only' (safety), 'free' (cost), and 'attribution required' (licensing), while also pointing to structuredContent.attribution for specific details. This adds significant behavioral context beyond the schema, though it omits other traits like rate limits or result structure. Given the safety and licensing disclosures, a 4 is appropriate.
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 two short sentences with no fluff. The first sentence states the core purpose, the second adds key behavioral traits. Every word earns its place, making it exemplary in conciseness and front-loading.
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 4 parameters, 0 required, and no output schema, the description covers the essential purpose and key behavioral flags (read-only, attribution). It does not detail the return structure beyond mentioning structuredContent.attribution, but for a simple search tool with a self-explanatory name, it is reasonably complete. A 4 reflects minor gaps in return-value expectations.
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?
The input schema describes all 4 parameters with clear descriptions (e.g., 'Free-text search over job title/company/tags', '1-based page number'). The description itself adds no parameter-specific detail, so with 100% schema coverage, the baseline of 3 applies.
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 uses the specific verb 'Search' with the resource 'nomado24's remote/hybrid job board', clearly distinguishing it from the sibling tool get_job_statistics. It names the exact board and action, leaving no ambiguity about its function.
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 clearly implies usage for searching jobs on the specified board, but it does not explicitly mention alternatives or when-not-to-use scenarios. Since a sibling tool exists (get_job_statistics), a brief 'use this for jobs, that for stats' would push it to 5, but the context is clear enough for a 4.
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
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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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