qb_get_invoiceRetrieve a complete invoice by ID including all line items, amounts, taxes, and payment history.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | QuickBooks Invoice ID |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "oneOf": [
+ {
+ "properties": {
+ "error": {
+ "description": "Error code if connection not configured",
+ "type": "string"
+ },
+ "message": {
+ "description": "Error message with setup instructions",
+ "type": "string"
+ }
+ },
+ "required": [
+ "error",
+ "message"
+ ],
+ "type": "object"
+ },
+ {
+ "properties": {
+ "Invoice": {
+ "description": "Complete invoice with line items, amounts, taxes, and payment history",
+ "properties": {
+ "DocNumber": {
+ "description": "Invoice number",
+ "type": "string"
+ },
+ "Id": {
+ "description": "Invoice ID",
+ "type": "string"
+ },
+ "Line": {
+ "description": "Line items on invoice",
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "TotalAmt": {
+ "description": "Total invoice amount",
+ "type": "number"
+ }
+ },
+ "type": "object"
+ }
+ },
+ "type": "object"
+ }
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "id": "123"
+ },
+ {
+ "id": "456"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds value by specifying what data the response includes (line items, amounts, taxes, payment history), which goes beyond annotations and helps the agent understand the tool's output.
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 sentence that is concise and front-loaded with the key information. There is no wasted text.
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 that there is an output schema (context signal) and the tool is simple (one required parameter), the description provides enough context about what is retrieved. It does not need to explain return values because the output schema covers that.
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?
Schema coverage is 100% and the parameter 'id' is documented in the schema. The tool description does not add additional semantic meaning beyond the schema's description, so baseline 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 retrieves a complete invoice by ID, with a specific verb 'retrieve' and resource 'invoice'. It distinguishes from sibling tools like qb_list_invoices (which lists invoices) and qb_get_customer (which gets a different entity).
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 does not provide explicit guidance on when to use this tool versus alternatives or when not to use it. It implies use for single invoice retrieval, but lacks exclusions or context for comparison with qb_query or qb_list_invoices.
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.
Several tools are near-duplicates (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and there are overlapping clusters among the polymarket_* tools, research tools (deep_research, entity_profile, compare_entities, recent_changes), and AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence). The detailed descriptions help, but an agent selecting among these could easily pick the wrong one.
Snake_case is used consistently, but the naming pattern is otherwise mixed: some tools are verb_noun (validate_claim, suggest_questions), some are bare nouns (entity_profile, polymarket_edges), some are verbs without objects (remember, forget, ask_pipeworx), and only the five QuickBooks tools share a qb_ prefix. This creates multiple naming ecosystems with no unified convention.
At 36 tools, this is well above the 25+ threshold for 'too many'. More importantly, the server is named Quickbooks but only 5 tools are accounting-related; the other 31 are unrelated Pipeworx data, prediction-market, memory, and meta tools, making the count both excessive and off-purpose.
For the QuickBooks domain named by the server, the surface is read-only: get customer, get invoice, list accounts, list invoices, and generic query. There are no create, update, delete, payment, bill, deposit, or report operations, which is a significant gap. For the broader Pipeworx data domain it is fairly complete, but that domain is not what the server name promises.