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TelegramLens

by Johnhyeon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

telegram_set_tier

Manually define a Telegram channel's tier to adjust its weight in buzz aggregation, overriding auto-assigned values with analyst, research, info, or gossip categories.

Instructions

채널 tier(성격)를 수동 지정합니다. 자동시드를 덮어쓰며 이후 재시드가 보존합니다.

tier 는 버즈 집계의 채널 가중치 근거입니다.

Args: channel: 채널 username(@ 제외) 또는 6자리가 아닌 숫자 channel_id. tier: analyst(애널리스트) | research(독립리서치) | info(종합·속보) | gossip(찌라시). weight: 가중 계수(생략 시 기본 analyst1.0/research0.8/info0.5/gossip0.3). note: 메모(선택).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
tierYes
weightNo
channelYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.7.0
    • removedInput schema / properties / note / default
      Removed value: -""
    • removedInput schema / properties / weight / anyOf
      Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "null"
-  }
-]
    • removedInput schema / properties / weight / default
      Removed value: -null
    • addedInput schema / properties / weight / type
      Added value: +"number"
  2. First observedv0.4.3

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate a mutating, non-idempotent operation, and the description adds meaningful behavioral detail: it overwrites the auto-seeded tier, the manual tier survives future re-seeds, and tier is the basis for buzz aggregation weights. This is useful context beyond the structured flags.

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 compact and well-structured: purpose, persistence behavior, and weighting impact come first, followed by a clean Args list. Every sentence earns its place and nothing is redundant.

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 definition covers purpose, side effects, and all parameter semantics, and an output schema exists so return values do not need explanation. The only meaningful gap is explicit when-to-use guidance relative to the large sibling set, which makes it very strong but not fully 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 description fully compensates by explaining each parameter: channel format (username without @ or non-6-digit numeric ID), allowed tier values with labels, default weight per tier, and optional note. An agent could construct correct arguments from this alone.

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 and resource ('채널 tier(성격)를 수동 지정합니다') and explains that it overrides the automatic seed. This distinguishes it from automatic-classification siblings such as telegram_classify_channels, even though no sibling is named.

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 manual-override context is clear ('수동 지정', '자동시드를 덮어쓰며'), so an agent can infer when it is relevant. However, it never explicitly states when to prefer this tool over automatic classification or other siblings, and no direct alternatives or exclusions are given.

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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