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TONresistor

Telegram MCP Server

by TONresistor

sendMessageDraft

Stream partial message drafts to a chat in real time while content is being generated, showing progress before the final message is ready.

Instructions

Stream partial messages to a user while being generated. This is useful for AI-powered bots that want to show responses as they're being created. Allows progressive message delivery during content generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chat_idYesUnique identifier for the target chat or username of the target channel (in the format @channelusername).
draft_messageYesThe draft message content to stream to the user. Contains text and optional formatting.
reply_parametersNoDescription of the message to reply to.
message_thread_idNoUnique identifier for the target message thread (topic) of the forum; for forum supergroups only.
business_connection_idNoUnique identifier of the business connection on behalf of which the message will be sent.
direct_messages_topic_idNoUnique identifier for the target direct messages topic; for bots only.

Schema Changelog

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

  1. First observedv1.0.0

TDQS

A3.6/5.0
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 behavioral disclosure. It does disclose the core progressive-streaming behavior, but it omits lifecycle details: whether repeated calls update a single draft, how the draft is finalized or replaced, and what side effects occur after generation ends.

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?

The description is short and front-loaded with the key action. There is minor redundancy between 'stream partial messages while being generated' and 'progressive message delivery during content generation,' but every sentence still contributes either behavior or use-case context.

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?

Given 6 parameters, nested objects, no output schema, and no annotations, the description provides enough to understand the tool's purpose and select it, but it is not fully complete for end-to-end invocation. It does not describe the return value, draft replacement semantics, or how this tool relates to finally sending a completed message.

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?

The input schema already provides full descriptions for all 6 parameters, including the nested draft_message object, so schema coverage is 100%. The description adds no parameter-specific meaning beyond the schema, so the baseline score of 3 applies.

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?

The description clearly states the tool's action ('stream partial messages') and target ('a user'), and frames it around progressive delivery during generation. It distinguishes itself from the normal sendMessage sibling through the 'partial' and 'stream' language, though it does not explicitly name or contrast any sibling.

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 provides a concrete when-to-use context: AI-powered bots that want to show responses as they are generated. It does not, however, state when not to use it, mention final-message handling, or point to sendMessage as the alternative for complete messages.

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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