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thriveventurelabs

AccountsOS MCP Server

Official

Upload Document

upload_document

Upload receipts, invoices, or bank statements to have AI extract key data for your accounting ledger.

Instructions

Upload a document (receipt, invoice, bank statement) for processing. The document will be analyzed by AI to extract data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_dataYesBase64-encoded file content. A "data:<mime>;base64,..." prefix is also accepted.
file_nameYesOriginal filename (e.g., "receipt.pdf")
document_typeNoType of document: receipt, invoice, bank_statement, or other

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.3

TDQS

B3.4/5.0
Behavior3/5

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

Annotations are all false, which weakly implies a mutating but non-destructive operation. The description adds a useful behavioral detail: the document 'will be analyzed by AI to extract data.' However, it omits side effects such as whether the document is persisted, whether processing is asynchronous, or what failures might look like.

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?

Two short sentences with no redundant filler. The core action and purpose are front-loaded, and every phrase earns its place.

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

Completeness2/5

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

For a tool with no output schema, the description should at least hint at what happens after the upload or how to access the extracted data. It does not mention the response shape, whether results are returned directly, or whether the agent should follow up with a search-documents call. This leaves an important operational 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 baseline is 3. The description's mention of document types (receipt, invoice, bank statement) aligns with the optional document_type parameter and adds mild context, but it does not supplement the parameter meaning beyond what the schema already provides.

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 identifies the action ('Upload'), the resource ('a document'), and gives concrete examples (receipt, invoice, bank statement). It also states the purpose (AI processing to extract data), which distinctively separates it from query-style siblings like get_document_links or find_related_documents.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus alternatives. The description does not mention that search_documents or find_related_documents should be used to retrieve previously uploaded documents, nor does it state any prerequisites or post-conditions.

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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