Live job postings for AI agents: search normalized postings from 30+ ATS feeds and job boards by title, skill, country, remote, seniority and recency. Also lists sources and pricing plans.
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.
Average 3.8/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: listing job sources, listing pricing plans, searching all jobs, and searching Upwork jobs specifically. No overlap in functionality.
All tools follow a consistent verb_noun pattern in snake_case (list_job_sources, list_pricing_plans, search_jobs, search_upwork_jobs).
4 tools is appropriate for a server focused on job search and metadata. The scope is narrow, and each tool serves a necessary function without bloat.
The tool set covers listing sources, pricing, and searching, but lacks tools for retrieving individual job details or managing queries. The search_jobs tool only returns API guidance, not results, which is a notable gap.
list_job_sourcesAList the ATS and job-board sources JobsPipe normalizes into a single JSON schema, with coverage and freshness notes.
| Name | Required | Description | Default |
|---|---|---|---|
| name_contains | No | Optional case-insensitive substring to filter sources by name (e.g. "workday"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds context about normalizing into a single JSON schema and including coverage/freshness notes, which goes beyond the 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?
The description is a single sentence that is front-loaded and contains no waste. Every word adds value.
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 listing tool with no required parameters, no output schema, and good annotations, the description fully covers its behavior and return values. It mentions the key output aspects (sources, coverage, freshness), making it complete.
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 the single optional parameter already well-documented in the schema. The tool description does not repeat or add to parameter semantics, which is acceptable given high schema 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?
The description states a specific verb ('List') and resource ('ATS and job-board sources JobsPipe normalizes'), including details about output ('coverage and freshness notes'). It clearly distinguishes from sibling tools like search_jobs and search_upwork_jobs.
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 implies the tool is for listing sources, but does not explicitly state when to use it versus alternatives like search_jobs or list_pricing_plans. No when-not or explicit usage context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_pricing_plansAList JobsPipe pricing plans with monthly price in USD, monthly job quota and included features.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | No | Optional plan name to return just one plan (e.g. "free", "builder", "scale"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is disclosed. The description adds output field details but does not disclose other behavioral traits such as ordering, error handling, or case sensitivity of the plan parameter. This is adequate given the 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 a single, front-loaded sentence that states the tool's purpose and what it returns. It contains no filler, redundancy, or unnecessary detail, making it highly efficient and easy to parse.
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 listing tool with one optional parameter and read-only annotations, the description is complete. It states what is listed (plans, price, quota, features), and the schema covers the parameter. No output schema exists, so detailing return format is not necessary.
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 schema has 100% coverage for the single optional 'plan' parameter, including examples ('free', 'builder', 'scale'). The description does not add any additional parameter semantics beyond what the schema provides, so the baseline score 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 a specific verb ('List') with a clear resource ('JobsPipe pricing plans') and specifies the output attributes (monthly price in USD, monthly job quota, included features). This clearly distinguishes it from siblings like list_job_sources and search_jobs.
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 is a pure purpose statement with no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It does not say 'use this when you need pricing plans' or provide context for choosing it over other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_jobsASearch live, normalized job postings across 30+ sources by title, skill/tech, country, remote, seniority, employment type and recency. Returns guidance for calling the authenticated REST API that runs the query.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return. | |
| remote | No | Only remote roles when true. | |
| skills_or | No | Match jobs tagged with any of these skill slugs, e.g. python, kubernetes. | |
| job_title_or | Yes | Match any of these job titles. | |
| description_or | No | Match any of these skills or technologies in the posting. | |
| job_location_or | No | Match any of these cities or regions, e.g. Seattle, WA. | |
| isic_division_or | No | ISIC Rev.4 employer industry divisions (2-digit, e.g. 62). | |
| job_seniority_or | No | Match any of these seniority levels. | |
| employment_type_or | No | Match any of these employment types. | |
| occupation_code_or | No | ISCO-08 occupation codes; 4-digit exact, 1-3 digit as hierarchy prefix. | |
| job_country_code_or | No | Match any of these ISO 3166-1 alpha-2 country codes. | |
| posted_at_max_age_days | No | Only postings published within this many days. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint), the description discloses that the tool returns guidance for an API call rather than direct job results. This is a key behavioral trait not covered by 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 with no wasted words. The purpose is front-loaded, and the key behavioral note (returns guidance) is provided succinctly.
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 12 parameters and no output schema, the description covers what filters are available but does not explain the 'guidance' output format or pagination. More detail on the result structure 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?
All parameters have schema descriptions (100% coverage), so the description adds limited value by summarizing filter dimensions. It groups parameters but does not provide additional syntax or constraints 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 searches live job postings across multiple sources and lists filter dimensions. However, the phrase 'Returns guidance for calling the authenticated REST API' introduces ambiguity about whether it returns results or instructions, lowering clarity slightly.
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 implies usage for broad job searches across 30+ sources, but does not explicitly differentiate from sibling search_upwork_jobs or state when not to use. No exclusions or alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_upwork_jobsASearch live Upwork job postings ingested by JobsPipe. Returns normalized rows with budget, skills and client signals.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | ||
| limit | No | Maximum rows to return (1-200, default 25). | |
| order | No | ||
| offset | No | Pagination offset. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, non-destructive, and open-world. The description adds value by specifying inputs (parameter mentions in schema but not description), outputs (normalized rows with budget, skills, client signals), and data freshness (live, ingested by JobsPipe). The description does not contradict 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 consists of two front-loaded sentences with no wasted words. It efficiently conveys the core purpose and key output characteristics.
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 no output schema and four parameters (all optional), the description gives a general idea of the output but lacks details about default behaviors, sorting/ordering mechanics, and full response structure. Adequate but not comprehensive.
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 50% (only 'limit' and 'offset' have descriptions). The tool's description does not mention any parameters or provide additional meaning beyond the schema. Since half of parameters lack documentation and the description offers no compensation, the semantic value is low.
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 verb 'search', the resource 'live Upwork job postings', and gives specifics about the data source and returned fields (budget, skills, client signals). This distinguishes it from siblings like 'search_jobs' and 'list_job_sources'.
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 implies usage for Upwork-specific job searches with certain data, but does not explicitly state when to use this tool versus alternatives or provide any exclusions. The context signals mention sibling tools, but they are not referenced in the description.
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
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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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