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FinBridge

CAN SLIM Screener (FinBridge DB)

screen_canslim
Read-only

Screen KR and/or US stocks against William O'Neil's CAN SLIM checklist (as taught by David Ryan), joining the nightly valuation_latest (earnings/sales growth, ROE, PER) and indicators_latest (relative strength, distance from the 52-week high) snapshots. Returns fundamentally strong momentum leaders, sorted by RS by default.

Only C, A, N, S, L are coded as filters — I (institutional sponsorship) and M (market direction) require fund-flow and index-level data that cannot be evaluated from a single stock's snapshot, so they are intentionally omitted: C — Current quarterly earnings: latest-quarter diluted-EPS YoY >= c_min (default 25). Quarter codes compare like-for-like a year apart (1=Q1, 2=cumulative half, 3=Q3). A — Annual earnings & quality: latest annual diluted-EPS YoY >= a_min (default 25) AND ROE >= roe_min (default 17) N — New highs: price within near_high_pct% of the 52-week high (default 15) S — Sales: latest annual revenue YoY > 0 when require_sales is true (default true) L — Leader: RS rating (national percentile 1-99) >= rs_min (default 80) A metric that is NULL (e.g. growth base was a loss, so the sign-flipped percentage is dropped) fails its comparison and the stock is excluded.

Args:

  • market: 'kr' (DART/KOSPI+KOSDAQ), 'us' (EDGAR), or 'all' (default)

  • c_min: min latest-quarter EPS YoY %, CAN SLIM C (default 25)

  • a_min: min latest-annual EPS YoY %, CAN SLIM A (default 25)

  • roe_min: min ROE %, quality gate under A (default 17)

  • rs_min: min RS percentile 1-99, CAN SLIM L (default 80)

  • near_high_pct: max % below the 52-week high, CAN SLIM N (default 15; smaller = closer to the high)

  • require_sales: require positive annual revenue growth, CAN SLIM S (default true)

  • min_vol_avg20: optional min 20-day average volume (liquidity filter for illiquid microcaps)

  • min_price: optional min close price (O'Neil avoids low-priced stocks; e.g. 10 for US$, 5000 for KRW)

  • sort_by: rs_pctile|eps_q_yoy|eps_a_yoy|sales_a_yoy|roe|pct_from_52w_hi|close (default rs_pctile)

  • order: 'asc'|'desc' (default 'desc'); limit: 1-50 (default 20); response_format: 'markdown'|'json'

Returns: {count, market, criteria:{c_min, a_min, roe_min, rs_min, near_high_pct, require_sales}, rows:[{name, source, ticker|stock_code, as_of, close, eps_q_yoy, eps_a_yoy, sales_a_yoy, roe, rs_pctile, pct_from_52w_hi, per}]}. Growth/ROE values are percent; pct_from_52w_hi is <= 0.

Examples:

  • US CAN SLIM leaders with liquidity: {market:'us', min_vol_avg20: 500000}

  • Strict KR growth leaders near highs: {market:'kr', c_min: 40, a_min: 30, rs_min: 90, near_high_pct: 10}

Use when: finding CAN SLIM-style growth leaders combining earnings/sales acceleration with strong relative strength. Don't use for a single company's valuation detail (get_valuation), the Minervini price template (screen_minervini), or raw statements (get_dart_financials / get_edgar_financials). Notes: CAN SLIM's I (institutional sponsorship) and M (market direction) cannot be screened from single-stock data — only C, A, N, S, L are applied. Growth uses diluted-EPS/revenue YoY; a company whose prior-period base is non-positive (loss->profit sign flip) has a null metric and is excluded. KR fundamentals follow K-IFRS and US follow US-GAAP, so cross-market growth/ROE comparisons are approximate. KR/US/TW prices are adjusted for corporate actions but not dividends (indicators around dividend events may be slightly distorted); US history starts 2023-03-28 (volume from 2024-07-01) so long-window figures are shallower there. Snapshot from the nightly ingest, not real-time, and not investment advice. Errors: an empty result is not an error (count 0 = nothing passed today); 'database has not been built yet' -> the valuation/indicators ingest has not run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
a_minNoMin latest-annual EPS YoY % — CAN SLIM A (default 25)
c_minNoMin latest-quarter EPS YoY % — CAN SLIM C (default 25)
limitNoMax rows, 1-50 (default 20)
orderNoSort direction (default desc)desc
marketNoMarket: 'kr' (default), 'us', or 'all'. ⚠Taiwan is not offered here — TWSE publishes a cumulative snapshot with no quarterly EPS growth or ROE, so every row would fail C/A silently.kr
rs_minNoMin RS percentile 1-99 — CAN SLIM L (default 80)
roe_minNoMin ROE % — quality gate under CAN SLIM A (default 17)
sort_byNoSort column (default rs_pctile)rs_pctile
min_priceNoMin close price (O'Neil avoids low-priced stocks; e.g. 10 for US$, 5000 for KRW)
min_vol_avg20NoMin 20-day average volume (liquidity filter)
near_high_pctNoMax % below the 52-week high — CAN SLIM N (default 15)
require_salesNoRequire positive annual revenue growth — CAN SLIM S (default true)
response_formatNo'markdown' for a table, 'json' for compact machine-readable outputmarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
countYes
notesNo
orderNo
marketYes
signalsNo
sort_byNo
us_noteNo
criteriaNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only provide readOnlyHint=true and openWorldHint=false, so the description carries the behavioral burden. It discloses nightly snapshot timing, NULL-metric exclusion, accounting-standard differences, dividend/corporate-action caveats, and the empty-result-not-error behavior—all beyond what annotations convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections: overview, criteria, args, returns, examples, usage, notes, and errors. It is long, but the complexity of a 13-parameter screener justifies most of it; the I/M omission is repeated in both the intro and notes, adding minor redundancy.

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?

The description covers the full decision surface: filter semantics, return shape, example calls, market-specific caveats, known error messages, and data freshness limitations. With an output schema present and all parameters documented, nothing needed to call this tool correctly is missing.

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 coverage is 100%, so the baseline is 3, but the description adds substantial value: it maps each parameter to a CAN SLIM criterion, explains quarter-code comparisons, gives examples, and specifies units. However, it incorrectly states that market's default is 'all' while the schema says the default is 'kr', which is a real reliability concern.

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 a specific verb and resource: it "screens KR and/or US stocks" against the CAN SLIM checklist using two named snapshots. It also distinguishes itself from siblings by explicitly saying not to use it for get_valuation, screen_minervini, or raw statement tools.

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?

It has an explicit 'Use when' section naming the exact scenario and an explicit 'Don't use for' section that names four sibling tools. It also explains why CAN SLIM's I and M criteria are intentionally not screened, preventing the agent from expecting them.

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct resource+action targets, and the overlapping screen_* tools are thoroughly cross-referenced with 'use screen_X instead' guidance. Minor ambiguity exists between get_disclosure_feed, get_dart_filings, and get_dart_major_events, which all surface KR filings from different angles but remain distinguishable.

Naming Consistency5/5

Every tool follows a consistent verb_noun snake_case pattern: get_* for retrievers, screen_* for screeners, search_* for lookups, plus action verbs like analyze_, backtest_, compare_, import_, and query_. Subfamilies (dart_*, edgar_*, fred_*, crypto_*) are consistently prefixed, making tool selection predictable.

Tool Count3/5

37 tools is heavy, and the four momentum screeners (canslim/kell/minervini/schwartz) plus three KR disclosure tools could arguably be collapsed into parameterized variants. However, the server's unusually broad scope—KR/US/TW/JP/EU equities, crypto, macro, portfolio, backtesting—means most tools earn their place, so the count is high but not chaotic.

Completeness4/5

The surface covers the core workflow well: search, prices, fundamentals, filings, insider trades, valuation, screeners, backtesting, and portfolio tracking for KR/US, plus crypto and macro. Notable gaps are the lack of single-company financial-statement tools for TW/JP/EU (only available through screen_companies) and no real-time stock quotes, but these are workable for the stated local-database research purpose.

Resources