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.
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Average 4/5 across 4 of 4 tools scored.
The tools are mostly distinct: list_job_sources and list_pricing_plans are clearly separate, and search_jobs is general while search_upwork_jobs is specific to Upwork. There is slight overlap because search_jobs may already include Upwork, but the dedicated Upwork tool adds unique signals like budget and client details, so an agent can choose based on need without much confusion.
All tool names follow the consistent verb_noun pattern: list_job_sources, list_pricing_plans, search_jobs, search_upwork_jobs. The verbs 'list' and 'search' are used appropriately, and all names are in lowercase snake_case, making the set predictable and easy to navigate.
The server has 4 tools, which is within the typical scope of 3-15. For a job search data provider, this is a reasonable number covering key actions: listing sources, viewing pricing, and searching jobs. It is slightly on the smaller side, but not underdeveloped enough to feel inadequate.
The core search and listing functionality is present, but there are notable gaps such as a dedicated tool to fetch detailed job information by ID, or tools for managing API authentication or rate limits. The general search tool covers the main domain, but the lack of job detail retrieval and other lifecycle operations leaves the surface incomplete for a full job-search API.
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. | |
| region_or | No | US states and Canadian provinces as ISO 3166-2 codes, e.g. US-NY, CA-ON. Matches every spelling a job board publishes, so CA-ON finds both ON and Ontario. More precise than job_location_or for a state or province. | |
| 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. | |
| work_arrangement_or | No | Match any of remote, hybrid or onsite. Finer than remote, which answers false for hybrid and onsite alike. Jobs whose arrangement is unknown never match, and coverage is still backfilling, so this returns far fewer results than expected today; remote is the reliable filter for now. | |
| 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?
Annotations already declare readOnlyHint=true, so the read-only nature is known. The description adds valuable behavioral context: the tool returns 'guidance for calling the authenticated REST API' rather than raw data, and mentions 'live, normalized' data quality. This goes beyond annotations, though the exact output behavior could be clearer.
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 action and resource, and each sentence earns its place. The second sentence discloses the output behavior, which is essential. No wasted words.
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's complexity (14 parameters, no output schema), the description provides enough context: a clear overview, the output behavior, and key data characteristics. The absence of explicit usage alternatives is a minor gap, but the annotations and schema fill most details.
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 fully documents all 14 parameters. The description only provides a high-level summary of filter categories (title, skill, country, etc.) without adding per-parameter semantics, which is acceptable but not value-added 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 verb 'Search' and the resource 'live, normalized job postings across 30+ sources', listing major filter dimensions. It distinguishes itself from siblings like search_upwork_jobs (broad vs Upwork-specific) and list_job_sources (search vs list).
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 clear context that this is a broad search tool across 30+ sources, implying when to use it over the Upwork-specific sibling. It does not explicitly exclude alternatives or state when-not-to-use, so it lacks the explicit alternative guidance of a 5.
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.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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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
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