nü people Job Server
Server Details
Public MCP server for discovering open jobs. Search, filter, and get application links.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Tool Definition Quality
Average 4.4/5 across 5 of 5 tools scored.
Each tool has a clearly distinct role: search_jobs finds jobs, get_job_details returns details for one job, get_application_link provides the apply URL, list_contract_types supports filtering, and get_skill_clusters gives an overview of in-demand skills. There is no overlapping functionality or risk of selecting the wrong tool.
All tool names follow a consistent verb_noun snake_case pattern: get_*, list_*, search_*. This makes the tool set predictable and easy to navigate.
Five tools is a compact, well-scoped set for a read-only job search server. Each tool earns its place and directly supports the job browsing and application workflow.
The core workflow is fully covered: search_jobs → get_job_details → get_application_link. Supporting tools for contract types and skill clusters round out the domain and enable effective filtering and discovery, with no obvious missing operations.
Available Tools
5 toolsget_application_linkGet Application LinkARead-onlyIdempotentInspect
Returns the direct application link for a public open job. Use this tool when the user wants to apply for a specific job after identifying it via search_jobs or get_job_details.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | No | Job ID from a previous search_jobs result. Accepts an integer or an integer represented as a string. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful scoping by stating the link is for a 'public open job' and clarifying the return value is a 'direct application link,' though it does not discuss edge cases such as expired links or non-open jobs.
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 with no filler: the first front-loads the tool's output and scope, the second gives actionable usage context. Every sentence earns its place.
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 one-parameter read-only tool with comprehensive schema documentation and safety annotations, the description fully covers what the tool does, when to use it, and what it returns. No output schema is present, but saying it returns the 'direct application link' is sufficient for this tool's simplicity.
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 description coverage is 100% and the jobId parameter is already well-documented as coming from a previous search_jobs result and accepting an integer or integer-string. The description adds only a minor connection to get_job_details as another source, so the schema carries most of the parameter burden.
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 states a specific verb and resource: 'Returns the direct application link for a public open job.' It also distinguishes the tool from siblings by framing it as the application step after identification via search_jobs or get_job_details, making its role in the workflow clear.
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 explicitly says to use this tool 'when the user wants to apply for a specific job after identifying it via search_jobs or get_job_details.' This gives a clear trigger condition and names the preceding tools, effectively telling the agent when not to use it (before a job is identified).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_job_detailsGet Job DetailsARead-onlyIdempotentInspect
Returns public details for a single open job by ID. Use this tool after search_jobs when the user wants more information about one specific job.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Job ID from a previous search_jobs result. Accepts an integer or an integer represented as a string. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint and idempotentHint annotations already establish the safety profile. The description adds useful context by noting the details are 'public' and the job must be 'open,' which is extra behavior information. However, it does not describe error behavior for invalid or non-open IDs, so there is a small transparency gap.
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 with no filler. The core behavior is front-loaded, and the usage context is given immediately after. Every word earns its place.
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 single-ID lookup tool with rich annotations and a well-documented parameter, the description covers what the tool does, when to use it, and what type of job it applies to. An agent has enough context to select and invoke 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?
Schema description coverage is 100%, so the schema already documents the 'id' parameter well, including its source and accepted types. The description only says 'by ID,' adding no meaning beyond what the schema provides.
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 a specific verb ('Returns') and identifies the exact resource ('public details for a single open job by ID'). It clearly distinguishes itself from sibling tools like search_jobs, which lists jobs, and get_application_link, which serves a different purpose.
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?
Explicity states when to use: 'Use this tool after search_jobs when the user wants more information about one specific job.' This gives the agent clear routing guidance and implies it should not be used for browsing or searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_skill_clustersGet Skill ClustersARead-onlyIdempotentInspect
Returns the current skill cluster data for public jobs on the nü people website. Use this tool when the user wants an overview of which skills or technologies are currently in demand.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true. The description adds context about currency ('current') and scope ('public jobs'), enhancing understanding 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?
Two sentences front-loaded with purpose and usage guideline. No wasted words, efficient and clear.
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 no parameters and rich annotations, the description fully explains the tool's output and use case. No gaps remain.
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?
Tool has no parameters, so schema coverage is complete. Description does not need to add parameter details, meeting the baseline for zero-param tools.
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 returns current skill cluster data for public jobs on the nü people website. It distinguishes from sibling tools like search_jobs and get_job_details, which focus on different aspects.
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?
Explicit guidance: 'Use this tool when the user wants an overview of which skills or technologies are currently in demand.' No ambiguity about when to invoke.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_contract_typesList Contract TypesARead-onlyIdempotentInspect
Lists the available contract types, including IDs and names. Use this tool when the user wants to filter search_jobs by contract type.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. The description adds that it returns IDs and names, but no other behavioral traits (e.g., ordering, caching) are disclosed, which is adequate for a simple list.
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, front-loaded with purpose and usage. Every sentence is valuable and concise.
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 zero-parameter list tool, the description fully covers what it returns and when to use it. No output schema is needed.
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?
No parameters exist, so schema coverage is 100%. Baseline is 4 per guidelines; the description adds no parameter info because none is needed.
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 it lists contract types with IDs and names. However, it does not explicitly distinguish from sibling tools, though the sibling tools are different in nature.
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 states when to use the tool: 'when the user wants to filter search_jobs by contract type'. No exclusion or alternatives are mentioned, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_jobsSearch JobsARead-onlyIdempotentInspect
Searches currently open jobs on the nü people website. Use this tool when the user wants to find matching jobs by keyword, role, skill, location, or contract type. Returns a list of public job summaries.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query, such as a role, skill, technology, or job title, for example 'Senior C# Developer' or 'Kubernetes'. | |
| location | No | Optional city filter, for example 'Munich'. | |
| maxResults | No | Maximum number of results to return. Allowed range: 1 to 25. Default: 10. | |
| contractTypeId | No | Optional contract type ID. Use list_contract_types first if the user wants to filter by contract type. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. The description adds that results are 'currently open' and 'public job summaries,' which is useful context beyond annotations. No contradictions.
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 sentences, front-loaded with the core purpose, and every word adds value. No redundant or filler content.
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 is a search with 4 parameters, annotations covering safety and idempotency, and no output schema, the description sufficiently covers purpose, usage, and return type ('list of public job summaries'). It omits pagination details but the schema handles maxResults.
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?
With 100% schema description coverage, the schema already explains all parameters. The description mirrors this without adding extra meaning or format details beyond what the schema provides. Baseline 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 'Searches currently open jobs on the nü people website,' specifying the verb (searches) and resource (open jobs). It distinguishes from siblings like get_job_details (single job) and list_contract_types (list types).
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 explicitly says when to use the tool ('when the user wants to find matching jobs by keyword, role, skill, location, or contract type') and advises using list_contract_types first for contract type filtering, providing clear guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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