get_job
Get detailed information about a specific job by its ID, including employer, agent, payout, status, validation state, and metadata URIs
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | The job ID to look up |
Get detailed information about a specific job by its ID, including employer, agent, payout, status, validation state, and metadata URIs
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | The job ID to look up |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the content of the response (employer, agent, payout, status, etc.) which is useful. However, it does not mention error handling (e.g., 404 for unknown ID), authentication requirements, or confirm that this is a read-only operation. It provides adequate but not rich behavioral context.
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 with no filler. It states the verb, resource, and key return fields efficiently, earning every word.
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 lookup tool with one parameter and no output schema, the description adequately covers input (jobId) and output (the listed fields). It does not explain return formatting or error cases, but these are less critical for a read-only get operation. Overall, it is sufficiently complete for an agent to use 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 coverage is 100%: the only parameter, jobId, has a clear description ('The job ID to look up'). The tool description adds no additional meaning beyond restating 'by its ID', so it does not exceed the baseline for fully covered 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 uses a specific verb ('Get') and resource ('job') with clear scoping ('by its ID'), and enumerates the exact information returned (employer, agent, payout, status, etc.). This distinguishes it from sibling tools like list_jobs (which lists multiple jobs) and fetch_job_metadata (which likely fetches only metadata).
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 clearly implies when to use the tool: when you have a specific job ID and need detailed information about that job. However, it does not explicitly name alternative tools (e.g., list_jobs for browsing) or state when not to use it, so it falls short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose with clear boundaries: job lifecycle (create_job, apply_for_job, cancel_job, request_job_completion, finalize_job, expire_job), validation (approve_job, disapprove_job, dispute_job), metadata handling (fetch_job_metadata, upload_to_ipfs), and queries (list_jobs, get_job, get_agent_reputation, check_agent_identity, get_protocol_info, register_agent). No overlapping functionality exists.
All 17 tools follow a consistent snake_case verb_noun pattern (e.g., create_job, list_jobs, fetch_job_metadata). The naming is highly predictable, with verbs like create, get, list, apply, approve, cancel, check, upload, and register consistently paired with relevant nouns.
17 tools is well-scoped for a job management platform covering the full lifecycle from creation to completion, including validation, disputes, metadata handling, and queries. Each tool earns its place, with no redundancy, and the count aligns with the complexity of the domain.
The toolset provides complete coverage for the AGI Alpha job platform: full CRUD/lifecycle (create, list, get, apply, cancel, complete, finalize, expire), validation workflows (approve, disapprove, dispute), identity and reputation management (register_agent, check_agent_identity, get_agent_reputation), metadata handling (upload_to_ipfs, fetch_job_metadata), and protocol info. No gaps are apparent.