Skip to main content
Glama
mutonby

Aikount MCP

api_request

Make custom API calls to any Aikount endpoint not covered by specialized tools. Use GET, POST, PATCH, or DELETE to interact with accounting data.

Instructions

Call any Aikount API endpoint not covered by a dedicated tool.

The full surface is documented at https://api.aikount.com/openapi.json — fetch it (e.g. api_request('GET', '/../openapi.json')) when you need an endpoint this server doesn't wrap.

Args: method: GET / POST / PATCH / DELETE. path: API path relative to the base, e.g. '/invoices' or '/contacts/'. Leading slash optional. params: query parameters. body: JSON body (for POST/PATCH).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNo
pathYes
methodYes
paramsNo
Behavior3/5

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

Describes HTTP method, path, params, body but does not disclose return value format, error behavior, or authentication details. With no annotations, more transparency on response would be beneficial.

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?

Reasonably concise with a clear 'Args' section. The example of fetching openapi.json adds extra but valuable information. Could trim slightly but overall well-structured.

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?

Provides enough to use the tool: method, path, params, body, and a link to full docs. However, no output schema or description of return value is a gap for completeness. Adequate but not exemplary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the docstring clearly explains each parameter: method (GET/POST/PATCH/DELETE), path (relative to base), params (query parameters), body (JSON for POST/PATCH). Adds useful context beyond bare schema.

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?

Description explicitly states it calls any Aikount API endpoint not covered by a dedicated tool, clearly distinguishing it from sibling tools.

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

Usage Guidelines4/5

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

Provides clear guidance to use when an endpoint is not wrapped by a dedicated tool, and suggests fetching the OpenAPI spec for discovery. No explicit when-not-to-use, but context is sufficient.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mutonby/aikount-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server