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rivradev

recite-mcp

by rivradev

scan_receipt

Scan receipts to extract financial data. Provide an image file, URL, base64 data, or raw text to get structured transaction details.

Instructions

Scan a receipt using the Recite API to extract financial data.

Provide exactly one input: file_path, image_url, image_base64, or raw_text.

Args: file_path: Local path to an image. image_url: Publicly accessible URL (must use https). image_base64: Base64-encoded image data. raw_text: Pre-extracted text. auto_save: Auto-create a transaction if successful. Requires project_id. save_threshold: Confidence threshold for auto-saving. project_id: Project UUID. Required if auto_save is True. status: Target status of the transaction. image_type: MIME type hint for the image. idempotency_key: Key to prevent duplicate processing. metadata: Custom key-value data. ephemeral: Process without saving scan records server-side. Cannot be True if auto_save is True.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathNo
image_urlNo
image_base64No
raw_textNo
auto_saveNo
save_thresholdNo
project_idNo
statusNo
image_typeNo
idempotency_keyNo
metadataNo
ephemeralNo
Behavior3/5

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

With no annotations, the description carries full burden. It discloses key behaviors like the ability to auto-create transactions and the incompatibility of ephemeral and auto_save. However, it lacks information on error handling, permission requirements, idempotency effects, rate limits, or what the tool does with the extracted data beyond auto-saving.

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 structured as a docstring with clear sections, but it is somewhat lengthy. Every sentence adds value, but it could be more concise by reducing redundancy (e.g., 'Provide exactly one input' is clear from the list).

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?

Given 12 parameters and no output schema, the description explains inputs well but completely omits the return value structure. It only mentions 'extract financial data', leaving the agent to guess the output format. This is a notable gap for a tool of this complexity.

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 description coverage is 0%, so the description must compensate. It adds meaning by listing each parameter and its purpose (e.g., 'file_path: Local path to an image'). This provides clarity beyond the schema, though some explanations are brief (e.g., 'metadata: Custom key-value data').

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 clearly states the tool scans a receipt using the Recite API to extract financial data. However, it does not differentiate from sibling tools like process_receipt or process_receipts_batch, which may have overlapping functionality.

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 provides guidance on the input mode ('Provide exactly one input') and explains conditions for auto_save and ephemeral. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention when not to use it.

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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