Skip to main content
Glama
pyalgobot

angelone-mcp

by pyalgobot

estimate_charges

Calculate brokerage and other charges for a basket of prospective orders to plan trading costs before placing them.

Instructions

Estimate brokerage and other charges for a basket of prospective orders.

Each order dict needs: product_type, transaction_type, quantity, price, exchange, symbol_name, token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ordersYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of indicating side effects. 'Estimate' and 'prospective' strongly suggest no order is actually placed, but the description does not explicitly disclose this safety-relevant trait or any other behavioral details.

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?

Two concise sentences: the purpose is stated first, then the essential parameter requirement follows immediately. No filler or redundant content.

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?

For a single-parameter tool with an output schema present, the description gives enough to call it correctly by listing the required sub-fields. It lacks some operational context such as whether orders can be empty or whether estimates are per-order or aggregate, but those are minor against the output schema and the clear required fields.

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?

Schema description coverage is 0% and the orders parameter is undocumented in the schema, but the description compensates by enumerating the required key fields for each order dict: product_type, transaction_type, quantity, price, exchange, symbol_name, token. It stops short of giving types or formats, so it is strong but not complete.

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?

The description uses a specific verb ('Estimate') and clarif resource ('brokerage and other charges for a basket of prospective orders'). This clearly distinguishes the tool from siblings like place_order or get_margin, none of which are about estimating charges.

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?

The term 'prospective orders' implies use before placing real orders, but the description does not explicitly state when to use this tool versus alternatives, nor when not to use it. Usage context is implied rather than spelled out.

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

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/pyalgobot/angelone-mcp'

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