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TONresistor

Telegram MCP Server

by TONresistor

answerInlineQuery

Answer inline queries by sending up to 50 results that appear directly in the user's chat. Returns success when delivered.

Instructions

Send answers to an inline query. Results are displayed in the user's chat. Maximum 50 results per query. Returns True on success.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
buttonNoAn InlineQueryResultsButton object to be shown above inline query results.
resultsYesA JSON-serialized array of InlineQueryResult objects representing results for the inline query.
cache_timeNoMaximum time in seconds that the result may be cached on Telegram servers. Defaults to 300.
is_personalNoPass True if results may be cached on the server side only for the user that sent the query.
next_offsetNoOffset that a client should send in the next query to receive more results. Max 64 bytes.
inline_query_idYesUnique identifier for the answered query.

Schema Changelog

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

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations present, the description carries the full burden and does disclose behavior beyond the schema: the 50-result cap, that results render in the user's chat, and that it returns True on success. It omits error behavior and timing sensitivity (inline queries expire), but it offers three concrete operational facts useful to an agent.

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 short sentences, each earning its place: purpose, display effect, hard limit, and return value. The core purpose is front-loaded before constraints, with no filler and no repetition of schema content.

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?

For a tool with nested result objects, no output schema, and no annotations, the description covers the essentials — purpose, cap, and return value. It leaves gaps an agent would want: the need to respond promptly before the query expires, failure behavior for an invalid inline_query_id, and the valid InlineQueryResult shapes for the nested results array.

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%, so the schema already documents all six parameters; the baseline of 3 applies. The description adds one useful constraint above the schema — 'Maximum 50 results per query' — but does not otherwise enrich the meaning of individual parameters.

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+resource pair — 'Send answers to an inline query' — which cleanly distinguishes this from siblings like answerCallbackQuery, answerWebAppQuery, and sendMessage. The clause 'Results are displayed in the user's chat' reinforces the exact scenario without ambiguity.

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 wording implies the tool is used in response to an inline query update, but there is no explicit when/when-not guidance or alternative routing. An agent must infer the distinction from the sibling set (e.g., answerCallbackQuery exists for callback queries) rather than being told.

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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