get_documentGet the extraction result for a document by id: doc_type, extracted fields, user corrections (when present), and deterministic verification warnings (ISO 6346 container check digits, plausibility flags). fields is the best available data (corrections win); original_fields and corrected_fields show both sides.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Document id |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation declares readOnlyHint=true, so the safe-read nature is already known. The description adds valuable behavioral context: the precedence of corrections over original fields, the existence of verification warnings, and the presence of optional fields. This goes beyond the annotation.
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 concise, two sentences long, front-loaded with the core purpose, then elaborates on the return structure. Every sentence adds meaningful detail without redundancy.
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 no output schema, the description effectively explains the expected return fields and their relationships. It mentions optionality and warning details. It could optionally cover error cases, but for a single-id read tool it is largely complete.
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 the only parameter 'id' with a clear description ('Document id'). The tool description reinforces 'by id' but adds no extra format or constraints. With 100% schema coverage, a baseline 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 gets the extraction result for a document by id, listing the specific contents (doc_type, extracted fields, corrections, warnings). This distinguishes it from sibling tools like list_documents or save_corrections.
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?
Usage is implied: use this tool when you need a single document's extraction result by id. However, there are no explicit comparisons to alternatives or exclusions, so it lacks strong 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.
Each tool targets a distinct action and resource: single extraction, batch extraction, document retrieval, listing, export, corrections, reconciliation, and account usage. extract_bol vs extract_batch is the only close pair, but single vs batch is a clear distinction.
All tool names follow a consistent verb_noun pattern with lowercase and underscores: export_documents, extract_batch, extract_bol, get_document, get_related_documents, get_usage, list_documents, save_corrections. No mixed styles or vague verbs.
8 tools is well within the ideal 3-15 range. Each tool earns its place by covering a distinct step in the document processing workflow, with no redundancy or bloat.
The toolkit covers the full lifecycle: extraction (single and batch), retrieval (get/list), update (save_corrections), export, reconciliation, and account management. There are no obvious gaps that would hinder agent workflows.