scan_receiptExtract precious metals purchase data from a receipt image using AI vision
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | TroyStack API key (required) | |
| image_base64 | Yes | Base64-encoded receipt image (JPEG) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must carry the full burden. The descriptor 'extract' indicates a read-like operation, but it does not disclose whether the tool only returns data or also saves it to the portfolio, nor does it mention any side effects or limitations. This is adequate but not deeply transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence of 10 words. It is front-loaded with the core action ('Extract precious metals purchase data') and wastes no words. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (2 parameters, no output schema). The schema is thorough, and the description explains the core function. However, the lack of an output schema means the description should provide more detail on what the returned 'purchase data' looks like or any behavior caveats. The description is adequate but leaves some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of the parameters with descriptions for api_key and image_base64. The description adds no extra parameter-level meaning beyond mentioning 'receipt image' and 'AI vision,' so it meets the baseline for high schema coverage without enriching the semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Extract precious metals purchase data from a receipt image using AI vision.' It uses a specific verb ('extract'), identifies the resource (receipt image), and specifies the method (AI vision). This clearly distinguishes it from sibling tools like add_holding or get_portfolio.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when you have a receipt image containing precious metals purchase data. However, it does not explicitly mention alternatives or exclusions, such as manual entry via add_holding. This gives clear context but lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct resources and actions (portfolio, spot prices, history, COMEX vault, coin melt, receipts). However, get_portfolio and get_analytics both report cost basis and P/L, and get_daily_brief and get_stack_signal both provide market commentary, creating mild ambiguity.
All tools follow a clear verb_noun snake_case pattern, with nine get_* prefixed tools and three distinct action verbs (add_, chat_, scan_). Naming is consistent and predictable.
12 tools is well-scoped for a precious metals platform covering portfolio management, market data, and specialized calculations. Each tool serves a distinct function within that domain.
The server supports adding and reading holdings but lacks update/delete operations for holdings, creating a dead end for correcting or removing entries. Market data coverage is thorough, but portfolio lifecycle management is incomplete.