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AGI Alpha

by com.agialpha

create_job

Prepare a transaction to create a new job on AGI Alpha. Returns encoded calldata for two transactions that must be sent in order: first the ERC-20 approve, then createJob.

STEP 1 — Build and upload the job spec JSON to IPFS using upload_to_ipfs. The JSON must have this exact structure: { "name": "AGI Job · ", "description": " — ", "image": "https://ipfs.io/ipfs/Qmc13BByj8xKnpgQtwBereGJpEXtosLMLq6BCUjK3TtAd1", "attributes": [ { "trait_type": "Category", "value": "" }, { "trait_type": "Locale", "value": "en-US" } ], "properties": { "schema": "agijobmanager/job-spec/v2", "kind": "job-spec", "version": "1.0.0", "locale": "en-US", "title": "", "category": "<research | development | analysis | creative | other>", "summary": "", "details": "", "tags": ["tag1", "tag2"], "deliverables": ["Concrete thing to deliver"], "acceptanceCriteria": ["Criterion validators will check"], "requirements": ["Any skill or tool requirement"], "payoutAGIALPHA": , "durationSeconds": , "employer": "", "chainId": 1, "contract": "0xB3AAeb69b630f0299791679c063d68d6687481d1", "ensPreview": "—", "ensURI": null, "generatedAt": "<ISO 8601 timestamp>", "createdVia": "" } } Note: "schema" is a plain string tag (not a URL) identifying the format version.

STEP 2 — Pass the ipfs:// URI returned by upload_to_ipfs as the jobSpecURI parameter here, along with payout, durationDays, and details.

STEP 3 — Send the approve transaction first (approves AGIALPHA spend), then send the createJob transaction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payoutYesPayout amount in AGIALPHA tokens (e.g. "1000" for 1000 AGIALPHA)
detailsYesOn-chain description string for the job
jobSpecURIYesIPFS URI pointing to job specification metadata (e.g. ipfs://Qm...). Use upload_to_ipfs first with the job spec JSON.
durationDaysYesJob duration in days

TDQS

A4.4/5.0
Behavior4/5

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

Discloses that the tool only prepares calldata rather than executing the transaction, and that two transactions must be sent in order. It also discloses the exact JSON structure requirement and the dependence on upload_to_ipfs. Since no annotations are present, this description carries the full burden and does so effectively.

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 lengthy but well-structured with clear STEP markers and a necessary JSON template. It front-loads the purpose and every element serves a functional role, though the length prevents a perfect score.

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?

For a complex tool with no output schema, the description is remarkably complete. It covers the full workflow: IPFS upload, parameter usage, transaction ordering, and return value, providing all necessary context for correct invocation.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful context by explaining that jobSpecURI comes from upload_to_ipfs, and by tying payout and durationDays to the job spec fields. This improves understanding beyond the schema's individual parameter descriptions.

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 the tool's function: preparing a transaction to create a new job on AGI Alpha, returning calldata for two transactions. It distinguishes itself from sibling tools by explicitly detailing the creation workflow and the need for prior IPFS upload.

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?

Provides explicit step-by-step instructions: first upload the job spec to IPFS, then call this tool with jobSpecURI, and finally send the approve transaction before createJob. It does not explicitly name alternatives, but the workflow clearly differentiates it from sibling job management tools.

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

A4/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness5/5

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.

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