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stornoro

Storno CLI

by stornoro

declarations_agent_result

Submit ANAF's response back to the Storno server to trigger response parsing, extract the upload ID, set status to PROCESSING, and initiate status checking for the declaration.

Instructions

Submit the ANAF response received via the local agent back to the server. The server parses the response, extracts the upload ID, sets status to PROCESSING, and dispatches status checking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesDeclaration UUID
bodyYesResponse body from ANAF
headersNoResponse headers from ANAF
companyIdNoCompany UUID (overrides STORNO_COMPANY_ID env var)
statusCodeYesHTTP status code from ANAF response
Behavior3/5

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

No annotations are provided, so the description carries the full burden. The description discloses a meaningful behavior: the server sets status to PROCESSING and dispatches status checking, which is a state transition the user should know about. However, it doesn't disclose failure behavior, validation errors, or what happens if the upload ID extraction fails. It also doesn't mention auth requirements.

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?

A single concise sentence covers purpose and effects without redundancy. Efficient and front-loaded, though it could benefit from a second sentence on failure/error behavior.

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

Completeness3/5

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

No output schema exists, so the description must explain return values and outcomes. It describes the server-side state transition but omits the response shape, polling flow, or error conditions. For a submission tool with complex ANAF response handling and status dispatch, this is a meaningful gap.

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 structured field descriptions already document each parameter (id, statusCode, body, headers, companyId). The description doesn't add meaning beyond the schema — it mentions the upload ID extraction from the response but doesn't clarify which parameter yields it or any format requirements for statusCode/body. Baseline 3 is appropriate given full schema coverage.

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

Purpose4/5

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

The description uses a specific verb ('Submit') with a clear resource ('the ANAF response') and explains the server-side behavior (parses, extracts upload ID, sets status to PROCESSING, dispatches status checking). It distinguishes this as the 'agent_result' submission path, though it doesn't explicitly contrast with sibling tools like declarations_submit or declarations_prepare.

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

Usage Guidelines3/5

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

The description implies when to use this tool — when a local agent has received an ANAF response that must be submitted back. However, it doesn't explicitly state when NOT to use it, nor does it contrast with alternatives like declarations_submit, declarations_sync, or declarations_prepare. The 'via the local agent' phrase gives some contextual framing but lacks explicit exclusions.

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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