WorkforceGPT

generate_role

Start generating a structured role profile — description, responsibilities, skills with proficiency levels. Returns immediately with a role_id; generation continues in the background, so poll check_role_status. Spends one of this account's role-generation credits. Only one generation runs at a time.

Input Schema

Name	Required	Description	Default
`industry`	No	The employer's industry in plain words, e.g. "Banking", "Healthcare" or "Retail". Skills are chosen to fit it. Optional: left out, it is inferred from company_name, which suits a well-known company. Pass it for a small, private or ambiguously named one, or whenever you already know it.
`role_title`	Yes	A single job title, e.g. "Staff Data Engineer". Not a list.
`company_name`	Yes	The organization the role is for.
`job_description`	No	Full text of a related job description, used as source context. Strongly recommended for titles labor-market data may not recognize.
`learning_resources_source`	No	Which catalog the suggested courses come from. Ignored when include_learning_resources is false.	Skillsoft
`include_learning_resources`	No	Attach suggested courses to each skill, readable later at result[].skills[].learning_resources. On by default. Turn it off for a shorter profile when nobody is going to act on the training suggestions.
`use_job_description_if_title_not_found`	No	Build the role from the job description when the title cannot be matched to labor-market data, instead of failing. Defaults to true when job_description is supplied, and has no effect without one.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 22, 2026

addedInput schema / properties / industry

Added value: +{
+  "description": "The employer's industry in plain words, e.g. \"Banking\", \"Healthcare\" or \"Retail\". Skills are chosen to fit it. Optional: left out, it is inferred from company_name, which suits a well-known company. Pass it for a small, private or ambiguously named one, or whenever you already know it.",
+  "maxLength": 100,
+  "type": "string"
+}

First observedSep 23, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does well: it discloses immediate return with role_id, background continuation, polling requirement, credit consumption, and single-generation concurrency. It does not cover failure behavior, credit exhaustion handling, or cancellation, which keeps it below a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core action and then adds only high-value operational details: async behavior, polling, credits, and concurrency. Every sentence contributes useful information with no repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an async generation tool with no annotations or output schema, the description covers the essential workflow and cost/concurrency constraints. It is not fully complete because it omits failure modes and credit-exhaustion behavior, but the rich schema descriptions compensate for much of the rest.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already explains all seven parameters, including optional industry inference and conditional defaults. The description adds no parameter-specific meaning beyond what the schema provides, which fits the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource: start generating a structured role profile with description, responsibilities, and skills. It also distinguishes the async start from the follow-up check_role_status sibling, so an agent can identify the tool without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly says to use this tool to start a generation and then poll check_role_status, and it notes the credit and concurrency constraints. It does not explicitly compare against siblings like assess_job_description or get_role, so it stops short of full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All WorkforceGPT tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?