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TelegramLens

by Johnhyeon
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Python
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telegram_fp_candidates

Read-onlyIdempotent

Surfaces stock mention candidates that lack confirmation markers, helping you pinpoint and block false positives from name or spelling collisions.

Instructions

오탐(잘못 잡힌 종목) 후보를 반환합니다.

'확인 표기' 없이 이름·철자만으로 자주 잡힌 것 → 일반명사·약어 충돌 의심. 확인 표기는 국내는 본문의 6자리 코드, 미국은 cashtag($NVDA) 입니다. 글쓴이가 종목임을 명시한 흔적이라는 점에서 같은 역할을 합니다. 검토 후 telegram_block_name 으로 차단 목록에 추가하세요.

Args: days: 분석 기간(일). 기본 7. max_name_len: 검사할 최대 길이. 0이면 자동 — 국내 3(짧은 이름이 충돌), 미국 5(티커 전체). min_count: 최소 '확인 표기 없는' 매칭 수. 기본 3. top: 상위 N개. 기본 40. market: "KR"(국내 종목명)/"US"(미국 티커)/"all"(합쳐서 의심도 순). 기본 all. 미국 오탐은 AI(C3.ai)·IR(Ingersoll Rand)·HBM(Hudbay Minerals)처럼 한국 증권 글에서 단어로 쓰이는 철자가 티커와 겹쳐 생깁니다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNo
daysNo
marketNo
min_countNo
max_name_lenNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.7.0
    • removedInput schema / properties / days / default
      Removed value: -7
    • removedInput schema / properties / market / default
      Removed value: -"all"
    • removedInput schema / properties / max_name_len / default
      Removed value: -0
    • removedInput schema / properties / min_count / default
      Removed value: -3
    • removedInput schema / properties / top / default
      Removed value: -40
  2. Changed2 schema fields changedv0.6.1
    • addedInput schema / properties / market
      Added value: +{
+  "default": "all",
+  "title": "Market",
+  "type": "string"
+}
    • changedInput schema / properties / max_name_len / default
      Previous value: -3New value: +0
  3. First observedv0.4.3

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context: it explains the heuristic (matches without confirmation markers), the role of confirmation markers, and market-specific behavior (e.g., US false positives from common words like AI, IR, HBM). No contradiction with annotations.

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: purpose, criteria, confirmation marker definition, workflow, then parameter details. Each section earns its place and provides necessary context without redundancy. The front-loaded purpose and clear parameter list make it easy to scan.

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 an output schema exists, the description does not need to explain return values. It provides enough context to understand the algorithm, parameter behavior, and downstream action (blocking via telegram_block_name). The description is complete for a read-only analysis tool with annotations already covering safety.

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%, so the description must compensate, and it does thoroughly. Every parameter (days, max_name_len, min_count, top, market) is described with defaults and behavioral meaning, including the automatic length logic and market-specific explanations. This adds significant value beyond 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 states a clear purpose: returns false-positive candidates for stocks incorrectly detected by name/character alone. It explains what counts as a false positive (no confirmation marker, common noun/abbreviation conflicts) and even defines the confirmation markers for KR (6-digit code) and US (cashtag). This is specific and distinguishes it from downstream tools like telegram_block_name.

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 description implies usage: use this tool to identify false positives, then review and add to block list via telegram_block_name. It gives a clear workflow but does not explicitly state when to use this tool over sibling alternatives like telegram_alias_candidates or when not to use it. No exclusions or alternative selection criteria are provided.

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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