Reconcile two or more documents (e.g. purchase register vs GSTR-2B, bank statement vs ledger). Matches line items by GSTIN + invoice number + amount; reports matches, mismatches with exact fields, and unmatched items. Requires Developer tier or above.
| Name | Required | Description | Default |
|---|---|---|---|
| files | Yes | 2-10 documents. Each item: {file_url OR file_base64, filename} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the matching behavior and a requirement (Developer tier), but does not mention if the operation is read-only, any rate limits, or the exact format of the output. The absence of such details leaves behavioral ambiguities.
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 three sentences, concise and to the point. It front-loads the main action and includes necessary context. Minor verbosity in the first sentence with examples does not detract significantly.
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?
Given the lack of annotations, output schema, and simple parameter structure, the description is relatively complete. It explains the matching logic and authorization requirement. It could be improved by specifying the output format or whether the tool is read-only, but it adequately covers the core functionality.
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?
With 100% schema description coverage, the schema already documents the 'files' parameter well. The description adds context about document types and matching logic but does not provide additional semantics for the parameter beyond the schema. Baseline score of 3 is appropriate.
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 reconciles two or more documents with specific examples (purchase register vs GSTR-2B, bank statement vs ledger) and details the matching logic (by GSTIN + invoice number + amount) and output (matches, mismatches, unmatched items). It distinguishes itself from sibling tools like extract_invoice_data or compress_pdf.
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 provides examples of when to use the tool (reconciling specific document pairs) and includes a tier requirement. However, it does not explicitly state when not to use it or mention alternative tools. The context is clear enough for an agent to infer appropriate scenarios.
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.
Each tool targets a distinct document processing task (compression, fraud detection, invoice extraction, statement parsing, conversion, reconciliation, GST validation) with clear descriptions that avoid overlap.
All tool names follow the verb_noun snake_case pattern (e.g., compress_pdf, validate_gstin), providing a predictable and uniform naming convention.
Seven tools cover the core capabilities for a PDF/document magic service focused on financial documents, balancing breadth without being overwhelming or sparse.
The set addresses key workflows (compress, convert, extract, parse, validate, reconcile), but misses general OCR or merge/split features. For the financial niche, it is nearly complete.
