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submit_and_pay_job

Submit a job to a provider on the elisym network, automatically pay using Solana, and wait for the result. If the result doesn't arrive in time, receive a notice to poll later.

Instructions

Full customer flow: submit job -> auto-pay -> wait for result. Validates that the payment recipient matches the provider card. If payment succeeded but no result arrives within the wait window, this returns a non-error "still processing" notice with the event ID (NOT a failure) - re-poll get_job_result later (results persist on the relays; for long jobs, poll periodically, e.g. from a subagent). Handles both free and paid providers automatically. If max_price_lamports is not set and the capability is paid, this returns the advertised price for confirmation WITHOUT submitting a job - re-call with max_price_lamports set to approve payments up to that limit (this is a confirmation, not an error). COST: input is sent inline in the tool call, so a large input pays output tokens on the calling LLM. For files or git diffs, prefer submit_and_pay_job_from_file or submit_diff_review respectively.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
capabilityNogeneral
session_idNoConversation control. Omit for automatic session management (providers advertising context support get a conversation auto-started on first contact; an ongoing conversation triggers a continue/new/one-off question before anything is published). Pass "new" to force a fresh conversation, "none" to force a stateless one-off, or a session_id from a previous result to continue that conversation. The provider answers with the conversation context of prior exchanges under the same id.
kind_offsetNo
timeout_secsNo
provider_npubYes
max_price_lamportsNo
Behavior5/5

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

Without annotations, the description fully bears the burden of transparency. It discloses payment validation, timeout behavior (returns 'still processing' notice), confirmation flow, cost implications (input size affects tokens), and that results persist on relays.

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 efficiently structured, front-loading the core purpose and then covering edge cases and guidance. Every sentence adds value without redundancy.

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?

Given the complexity (7 parameters, no output schema, no annotations), the description covers key behavioral aspects and edge cases well. However, it lacks explicit details on return values and individual parameter semantics, preventing a perfect score.

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

Parameters2/5

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

Schema description coverage is only 14%, yet the description only adds meaning for max_price_lamports (confirmation flow). Other parameters like input, capability, kind_offset, timeout_secs, provider_npub are not explained. The description does not compensate sufficiently for the low schema coverage.

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 purpose: 'Full customer flow: submit job -> auto-pay -> wait for result.' It also distinguishes from siblings by explicitly mentioning alternatives for files and git diffs.

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?

The description provides explicit guidance on when to use this tool vs. alternatives ('For files or git diffs, prefer submit_and_pay_job_from_file or submit_diff_review respectively') and explains the confirmation flow for price approval.

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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