Dice MCP

Get Job Details

get_job_details

Read-only

Retrieve a job's full description and associated skills by job ID.

Use this tool when users want more detail about a specific job than search_jobs returns (e.g. after finding a job via search_jobs and wanting its full description and skills list). DO NOT use for: searching/discovering jobs by keyword (use search_jobs instead).

LLM USAGE INSTRUCTIONS:

Only call this after the user has picked out a specific job from search_jobs results (or otherwise supplied a job's guid directly) — do not call it speculatively for every search result.
After returning job details, do NOT automatically call get_company. Instead, offer to look up the company (e.g. "Want me to pull up more info on the company?") and only call get_company, passing this same job_id, if the user says yes.

Args: job_id: The unique identifier of the job to retrieve (required). This must be the job's guid field from a search_jobs result, NOT its id field — id is a different, internal identifier that this tool does not accept.

Returns: JobDetails: Contains: - description: The full job description - skills: List of Skill objects associated with the job

Raises: Exception: If the API call fails, the job is not found, or input validation errors occur

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`skills`	No	Skills associated with the job
`description`	No	The job description

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that job_id must be the guid (not id) and that exceptions are raised for API failure, job not found, or input validation errors—adding value 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.

Conciseness4/5

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

The description is well-structured with sections (Args, Returns, Raises) and front-loads the main purpose. However, there is some redundancy (e.g., first two paragraphs both describe when to use), slightly reducing conciseness.

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

Completeness5/5

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

Given the simple single-parameter tool with an output schema, the description covers all necessary context: parameter semantics, return structure, error conditions, and usage guidelines. No gaps remain.

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

Parameters5/5

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

Schema coverage is 0%, but the description thoroughly explains job_id: it is required, must be the guid field from search_jobs, and not the id field. This adds essential semantics beyond the schema.

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 clearly states 'Retrieve a job's full description and associated skills by job ID', which is a specific verb+resource. It also distinguishes from siblings by contrasting with search_jobs and mentioning get_company usage.

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

Usage Guidelines5/5

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

Provides explicit when-to-use ('after the user has picked out a specific job from search_jobs results') and when-not-to-use ('DO NOT use for: searching/discovering jobs by keyword'). Also gives specific LLM instructions about not automatically calling get_company.

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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TDQS

A4.8/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: search_jobs discovers job listings, get_job_details returns the full description and skills for a specific job, and get_company retrieves company details for a job. There is no overlap in purpose, and descriptions explicitly clarify when to use each tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: search_jobs, get_job_details, get_company. The verbs are semantically appropriate (search for discovery, get for retrieval) and the pattern is uniform across the set.

Tool Count5/5

Three tools is a well-scoped, focused set for a read-only job search MCP. Each tool earns its place in the search → details → company flow, and the count falls comfortably within the typical 3-15 range.

Completeness4/5

The core user journey is covered: searching for jobs, viewing full job details, and looking up company information. Minor gaps exist (e.g., no company search or application submission tools), but these are explicitly outside the stated purpose, so the surface is adequate for the domain.

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