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TONresistor

Telegram MCP Server

by TONresistor

answerShippingQuery

Reply to Telegram shipping queries by confirming delivery availability and providing shipping options, or decline with a clear error message.

Instructions

Reply to shipping queries. If you sent an invoice requesting a shipping address and the parameter is_flexible was specified, the Bot API will send an Update with a shipping_query field to the bot. Use this method to reply to shipping queries. On success, True is returned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesPass True if delivery to the specified address is possible and False if there are any problems.
error_messageNoRequired if ok is False. Error message in human readable form that explains why it is impossible to complete the order.
shipping_optionsNoRequired if ok is True. A JSON-serialized array of available shipping options.
shipping_query_idYesUnique identifier for the query to be answered

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the disclosure burden. It states the trigger context and that True is returned on success, but it does not explain failure behavior, whether a query can be answered only once, or any side effects beyond the reply.

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

Conciseness3/5

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

The description is short and front-loaded with the core purpose, but it repeats 'reply to shipping queries' in the first and third sentences. This redundancy slightly undermines conciseness without adding information.

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 straightforward look-and-answer method, the description provides the necessary trigger context and return behavior, while the schema covers parameter details. It is complete enough for an agent to understand when and how to invoke the tool.

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%, so the schema documents all four parameters. The description adds no extra meaning about the parameters themselves; it only references is_flexible from the invoice context, not any direct parameter.

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 clearly identifies the action as 'Reply to shipping queries' and references the shipping_query Update field. This distinguishes it well from sibling methods like answerPreCheckoutQuery or answerCallbackQuery.

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?

It specifies the exact triggering condition: after sending an invoice with is_flexible requesting a shipping address, the Bot API sends a shipping_query Update. This gives clear context for when to use the tool, though it does not explicitly name alternatives.

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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