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telegram-mcp

telegram-mcp

by draiqw
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

tg_translate

Translate any text or Telegram messages into a target language with Telegram's native translator. Set to_lang, then optionally provide text or select chat and up to 20 message IDs.

Instructions

Translate messages (or any text) with Telegram's own translator.

Args: to_lang: target language code, e.g. "ru", "en", "de". chat: chat the messages live in. message_ids: which messages to translate (max 20). text: translate this text instead of messages.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "to_lang": "<to_lang>"
    },
    "name": "tg_translate"
  }
}

Input Schema

NameRequiredDescriptionDefault
chatNo
textNo
to_langYes
message_idsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of transparency. It mentions using Telegram's own translator and a max of 20 message IDs, but does not disclose whether this operation modifies anything, the return format, or edge cases like what happens when both text and message_ids are provided.

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?

The description is succinct and well-structured: an action-first first sentence followed by a clean args list. Every line adds value without redundancy.

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?

The description covers the core function and parameter semantics adequately for a translation tool. It lacks explicit guidance on parameter interactions (e.g., precedence if both text and message_ids are provided), but the presence of an output schema reduces the need to describe return values. Overall, it is sufficiently complete for the tool's complexity.

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%, but the description compensates by explaining all four parameters: to_lang with examples, chat as the location of messages, message_ids with a max limit, and text as an alternative to message translation. This is a clear upgrade over the 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?

The description clearly states a specific verb and resource: 'Translate messages (or any text) with Telegram's own translator.' This distinguishes it from sibling tools like tg_transcribe (audio transcription) and tg_summarize (summarization).

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?

The description provides clear context by stating it translates messages or arbitrary text. While it does not explicitly mention alternatives or exclusions, the absence of any other translation tool among siblings makes the usage context sufficiently clear.

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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