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aol-integration

accurate-schema-mcp

generic_call

Executes authenticated calls to any Accurate Online endpoint. First run schema_lookup to get correct field names and types, then pass params for GET or DELETE, and body for POST.

Instructions

Call any Accurate Online endpoint. Always run schema_lookup on the endpoint first so you know the correct field names, required fields, and types. For GET (list/detail) endpoints pass query params in params. For POST (save) endpoints pass the payload in body. For DELETE pass params (usually {'id': ...} or {'number': ...}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoJSON body, for POST (save/bulk-save) endpoints
paramsNoQuery params, for GET/DELETE endpoints
endpointYese.g. 'purchase-invoice/save', matches the key from schema_lookup

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the schema_lookup prerequisite and method-to-param mapping, but says nothing about destructive semantics (DELETE endpoints), auth/refresh_host usage, rate limits, error shapes, or whether POST save is upsert vs insert. For a tool that can mutate any resource, these omissions are notable.

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

Conciseness5/5

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

Four sentences, each carrying distinct required information (dependency, GET pattern, POST pattern, DELETE pattern). Front-loads the critical schema_lookup prerequisite. No waste.

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

Completeness3/5

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

No output schema and no annotations, so the description is the sole source of behavioral context. It covers the calling convention well but leaves mutation semantics, auth/refresh flow, and response handling unaddressed for a tool capable of arbitrary resource mutation.

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 coverage is 100% and the schema already documents body as 'JSON body for POST' and params as 'Query params for GET/DELETE'. The description reinforces the mapping and adds the DELETE id/number example, but largely restates schema content. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb+resource ('Call any Accurate Online endpoint') with an explicit dependency, and it is distinguishable from siblings by being the invocation mechanism while schema_lookup is the discovery mechanism. Lacks specificity about which endpoint families exist, but the generic nature is intentional.

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

Usage Guidelines5/5

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

Gives explicit prerequisites ('Always run schema_lookup on the endpoint first') and routes by HTTP method to the correct parameter bucket (GET/DELETE -> params, POST -> body) with concrete examples including id vs number for DELETE. This is exactly the when/when-not guidance needed for a generic dispatcher.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.