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telegram-mcp

telegram-mcp

by draiqw
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

tg_history_batch

Batch-fetch up to 25 Telegram chats at once for comparing or summarizing conversations, with optional search and per-chat message limits.

Instructions

Read several chats in one call (up to 25). Use this instead of calling tg_history repeatedly when comparing or summarising multiple conversations.

Args: chats: chat ids, @usernames or exact titles. limit: messages per chat. search: only messages containing this text, applied to every chat.

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": {
      "chats": "<chats>"
    },
    "name": "tg_history_batch"
  }
}

Input Schema

NameRequiredDescriptionDefault
chatsYes
limitNo
searchNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior3/5

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

With no annotations provided, the description must carry the burden of behavioral disclosure. It does disclose a batch limit of 'up to 25' chats and that search is 'applied to every chat,' which is useful context. However, it does not mention error handling, behavior on invalid chat IDs, or whether the operation is purely read-only until assumed from the verb 'read.' This lacks depth for full transparency.

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 highly concise, using two short sentences to convey purpose and usage, followed by a compact bulleted list of parameters. Every sentence earns its place and there is no redundant or verbose language.

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 purpose, usage, alternatives, and all parameter semantics, which is strong for a batch read tool. The output schema likely documents return structure, so its absence in the description is acceptable. Minor gaps remain around failure modes and edge cases, but the overall context is sufficiently 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?

Despite the schema having 0% description coverage, the description fully explains all three parameters: chats can be 'chat ids, @usernames or exact titles,' limit is 'messages per chat,' and search filters for 'only messages containing this text, applied to every chat.' This adds essential meaning beyond the raw schema types.

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 the tool reads multiple chats in one call, specifying 'Read several chats in one call (up to 25).' It also distinguishes from the sibling tool tg_history by saying 'Use this instead of calling tg_history repeatedly,' making the tool's unique value explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: 'Use this instead of calling tg_history repeatedly when comparing or summarising multiple conversations.' This names the alternative tool and defines the exact condition for use, leaving no ambiguity about when to choose this tool.

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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