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draiqw

telegram-mcp

by draiqw

tg_export

Export entire Telegram chats to local files, including attachments and links, for analysis or archival.

Instructions

Dump whole conversations to files (max 5000 messages per chat).

With media=true every attachment is downloaded next to the transcript and each message carries the local file path, its links, and a t.me link to the message itself where one exists. That is the tool for "give me the full conversation with everything in it", and it pairs with tg_activity: take the chat ids from there, feed them in as chats.

Args: chat: one chat to export. chats: several chats at once, up to 25. One failure does not stop the rest. limit: how many recent messages per chat, written oldest first. format: json for analysis, markdown or text for reading. dest: target directory. Defaults to tg-agent/data/downloads. since: only messages from this point — "today" or an ISO datetime. until: upper bound, ISO datetime. media: also download every attachment. media_max_mb: skip attachments larger than this (they are listed as skipped).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chatNo
destNo
chatsNo
limitNo
mediaNo
sinceNo
untilNo
formatNojson
media_max_mbNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries full burden and delivers: discloses the 5000-message limit, media download behavior (local paths, links, t.me link), per-chat failure isolation, oldest-first writing, and default destination. This goes well beyond minimal disclosure.

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 well-structured with a concise summary up front followed by a terse Args list. Every line carries unique information; no filler or redundancy is present. Length is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 9 parameters, no annotations, and 0% schema coverage, this description covers all aspects: purpose, parameter semantics, limitations, defaults, and side effects (downloading files). The output schema handles return values, so the description is appropriately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the Args section explains every parameter in plain language: chat vs chats, up to 25 chats, limit, format choices, dest default, since/until formats, media download flag, and media_max_mb skip behavior. This fully compensates for the schema's lack of descriptions.

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 opens with 'Dump whole conversations to files', a specific verb and resource. It clearly distinguishes this tool from siblings by positioning it as the full-conversation export option, even naming its companion tool tg_activity.

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 explicitly states the use case ('That is the tool for "give me the full conversation with everything in it"') and gives a concrete integration example with tg_activity. However, it doesn't explicitly say when not to use alternatives, so it misses the when-not clause for a perfect score.

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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