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Boulevard MCP by usefulapi

List orders

boulevard_list_orders
Read-only

List checkout orders at one location with totals (cents: subtotal, discount, tax, gratuity, total, refunds), optionally filtered e.g. by closedAt range or clientId. GraphQL: orders.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterNoCursor: pageInfo.endCursor from the previous page. Omit for the first page.
firstNoPage size, 1-100 (default 25).
queryNoBoulevard filter expression (QueryString) over: id, clientId, closedAt, updatedAt, createdAt. Comparisons joined with AND/OR, strings in single quotes, ISO 8601 datetimes, e.g. "startAt >= '2026-10-01T00:00:00Z' AND cancelled = false". Field names are case sensitive.
location_idYesLocation id, e.g. urn:blvd:Location:<uuid> (from boulevard_list_locations).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so safety is covered. The description still adds real context — amounts are in cents and results are scoped to one location — but it never says what an unfiltered call returns (open vs closed orders), whether results are paged, or any rate/limit behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense sentence front-loads the verb, scope and returned fields, followed by a short GraphQL pointer. The parenthetical totals list is long but earns its place; the trailing 'GraphQL: orders.' fragment is terse but harmless.

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

Completeness4/5

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

With no output schema, the description does the necessary work of naming the returned monetary fields and their unit, and pagination is covered by the schema's after/first definitions. It falls short only on default result scope and pagination behavior, which an agent may need when forming the first call.

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%, and the schema's query parameter is documented far more thoroughly (field list, syntax, examples) than the description's brief 'closedAt range or clientId' mention. Baseline 3 is appropriate since the schema already carries the semantics.

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?

States a specific verb (list) and resource (checkout orders) plus the scope constraint 'at one location', which cleanly separates it from the sibling boulevard_get_order for a single order. It also enumerates the monetary fields returned, so an agent knows what it gets without opening a schema.

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

Usage Guidelines3/5

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

Only 'optionally filtered e.g. by closedAt range or clientId' implies when the query parameter is useful; there is no when-to-use versus boulevard_list_appointments/boulevard_get_order guidance and no note on what unfiltered results contain. Usage is inferable but not stated.

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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