Skip to main content
Glama
Sign In

Stocklake — AI Stock Intelligence

by dev.stocklake
OverviewDiscussionsAdmin

Screen Stocks

get_screener
Read-onlyIdempotent

Filter and rank stocks from the Stocklake universe - fundamentals, technicals, and AI signals in one tool.

Parameters:

  • sector: e.g. "Technology", "Healthcare", "Financial Services"

  • country: e.g. "United States", "Germany"

  • min_rsi / max_rsi: exact RSI bounds (e.g. max_rsi=30 = oversold, min_rsi=70 = overbought)

  • sma_trend: "above_200" (price above 200-day MA) | "below_200"

  • macd_signal: "positive" (MACD line above signal) | "negative"

  • min_perf_1d / max_perf_1d: 1-day performance % (e.g. min_perf_1d=2.0 = up 2%+ today)

  • min_volume: minimum daily volume (e.g. 1000000)

  • min_market_cap_b / max_market_cap_b: market cap in billions of US dollars, compared against market_cap_usd (stocks with no FX rate for their currency are excluded when either bound is set)

  • max_pe_forward: maximum forward P/E (e.g. 20 = value screen)

  • analyst_rating: "strong_buy" | "buy" | "hold" | "sell" | "strong_sell"

  • min_ai_score: minimum AI score 0-100 (pro tier only - silently ignored for free). Gates on the AI research composite ai_score - same 0-100 scale and bands as signal_score, but a separate score (per-stock AI summary strength, not a directional signal). Renamed 2026-08-24 from the retired 0-10 min_flag_score - the raw flag_score field it used to gate on is no longer part of this fleet's public vocabulary at all (see ai_score below).

  • preset: "oversold" | "overbought" | "momentum" | "high_conviction" (pro only) oversold = RSI≤35 + above SMA200 · overbought = RSI≥65 momentum = RSI 50-70, above SMA200, up 0.5%+ today · high_conviction = ai_score≥70

  • sort_by: "market_cap" | "rsi" | "perf_1d" | "volume" | "analyst_rating" | "rating" | "ai_score" (pro). Defaults to "market_cap", except the "high_conviction" preset defaults to "ai_score" (the dimension it's filtering by) unless you explicitly pass a different sort_by.

  • sort_dir: "asc" | "desc" (default "desc")

  • limit: 1-25 (default 20). Each returned stock counts as one call toward your daily limit.

Returns: { count, preset, filters, results[] } - each result includes symbol, name, sector, industry, country, price, change_pct, volume, market_cap, market_cap_usd, currency, pe_forward, rsi, macd_signal, sma200_trend, analyst_rating. market_cap is in the listing's own currency (the currency field); market_cap_usd is converted to US dollars and is what the market_cap sort and cap filters use. Pro tier adds rating (0-10 composite score) + ai_verdict + ai_headline + ai_score (0-100) + ai_score_band to every result row (the headline is the "why" - a bare verdict alone isn't enough to act on), enables min_ai_score filter, high_conviction preset, and sorting by rating/ai_score (sort_by silently falls back to market_cap on free/guest for both). All other filters available to all tiers. For the full research bundle on any one result, call get_stock_research(symbol).

Input Schema

NameRequiredDescriptionDefault
limitNoMax results to return, 1-25 (default 20). Each returned stock counts as one call toward your daily limit.
presetNo"oversold" | "overbought" | "momentum" | "high_conviction" (pro only). oversold = RSI<=35 + above SMA200; overbought = RSI>=65; momentum = RSI 50-70, above SMA200, up 0.5%+ today; high_conviction = ai_score>=70.
sectorNoFilter by sector, e.g. "Technology", "Healthcare", "Financial Services".
countryNoFilter by country, e.g. "United States", "Germany".
max_rsiNoMaximum RSI bound (e.g. max_rsi=30 = oversold).
min_rsiNoMinimum RSI bound (e.g. min_rsi=70 = overbought).
sort_byNo"market_cap" | "rsi" | "perf_1d" | "volume" | "analyst_rating" | "rating" | "ai_score" (pro). Defaults to "market_cap", except the "high_conviction" preset defaults to "ai_score" (the dimension it's filtering by) unless you explicitly pass a different sort_by. Sorting by rating/ai_score silently falls back to market_cap on free/guest.
sort_dirNoSort direction: "asc" | "desc" (default "desc").desc
sma_trendNo"above_200" (price above 200-day MA) | "below_200".
min_volumeNoMinimum daily volume, e.g. 1000000.
macd_signalNo"positive" (MACD line above signal) | "negative".
max_perf_1dNoMaximum 1-day performance %.
min_perf_1dNoMinimum 1-day performance %, e.g. min_perf_1d=2.0 = up 2%+ today.
min_ai_scoreNoMinimum AI score, 0-100 - pro tier only (silently ignored for free). Gates on the AI research composite ai_score, same 0-100 scale and bands as signal_score but a separate score (per-stock AI summary strength, not a directional signal).
analyst_ratingNoFilter by analyst consensus rating: "strong_buy" | "buy" | "hold" | "sell" | "strong_sell".
max_pe_forwardNoMaximum forward P/E, e.g. 20 for a value screen.
max_market_cap_bNoMaximum market cap in billions of US dollars (compared against market_cap_usd).
min_market_cap_bNoMinimum market cap in billions of US dollars (compared against market_cap_usd, so non-USD listings are converted first).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / min_ai_score / description
      Previous value: -"Minimum AI score, 0-100 — pro tier only (silently ignored for free). Gates on stock_ai_summary.py's own composite ai_score, same 0-100 scale/band convention as signals.signal_score but a distinct field/pipeline (per-stock AI summary confidence, not a directional trade idea). Renamed 2026-08-24 from the retired 0-10 min_flag_score."New value: +"Minimum AI score, 0-100 - pro tier only (silently ignored for free). Gates on the AI research composite ai_score, same 0-100 scale and bands as signal_score but a separate score (per-stock AI summary strength, not a directional signal)."
  2. Changed2 schema fields changed
    • changedInput schema / properties / max_market_cap_b / description
      Previous value: -"Maximum market cap in billions of dollars."New value: +"Maximum market cap in billions of US dollars (compared against market_cap_usd)."
    • changedInput schema / properties / min_market_cap_b / description
      Previous value: -"Minimum market cap in billions of dollars."New value: +"Minimum market cap in billions of US dollars (compared against market_cap_usd, so non-USD listings are converted first)."
  3. Changed18 schema fields changed
    • addedInput schema / properties / analyst_rating / description
      Added value: +"Filter by analyst consensus rating: \"strong_buy\" | \"buy\" | \"hold\" | \"sell\" | \"strong_sell\"."
    • addedInput schema / properties / country / description
      Added value: +"Filter by country, e.g. \"United States\", \"Germany\"."
    • addedInput schema / properties / limit / description
      Added value: +"Max results to return, 1-25 (default 20). Each returned stock counts as one call toward your daily limit."
    • addedInput schema / properties / macd_signal / description
      Added value: +"\"positive\" (MACD line above signal) | \"negative\"."
    • addedInput schema / properties / max_market_cap_b / description
      Added value: +"Maximum market cap in billions of dollars."
    • addedInput schema / properties / max_pe_forward / description
      Added value: +"Maximum forward P/E, e.g. 20 for a value screen."
    • addedInput schema / properties / max_perf_1d / description
      Added value: +"Maximum 1-day performance %."
    • addedInput schema / properties / max_rsi / description
      Added value: +"Maximum RSI bound (e.g. max_rsi=30 = oversold)."
    • addedInput schema / properties / min_ai_score / description
      Added value: +"Minimum AI score, 0-100 — pro tier only (silently ignored for free). Gates on stock_ai_summary.py's own composite ai_score, same 0-100 scale/band convention as signals.signal_score but a distinct field/pipeline (per-stock AI summary confidence, not a directional trade idea). Renamed 2026-08-24 from the retired 0-10 min_flag_score."
    • addedInput schema / properties / min_market_cap_b / description
      Added value: +"Minimum market cap in billions of dollars."
    • addedInput schema / properties / min_perf_1d / description
      Added value: +"Minimum 1-day performance %, e.g. min_perf_1d=2.0 = up 2%+ today."
    • addedInput schema / properties / min_rsi / description
      Added value: +"Minimum RSI bound (e.g. min_rsi=70 = overbought)."
    • addedInput schema / properties / min_volume / description
      Added value: +"Minimum daily volume, e.g. 1000000."
    • addedInput schema / properties / preset / description
      Added value: +"\"oversold\" | \"overbought\" | \"momentum\" | \"high_conviction\" (pro only). oversold = RSI<=35 + above SMA200; overbought = RSI>=65; momentum = RSI 50-70, above SMA200, up 0.5%+ today; high_conviction = ai_score>=70."
    • addedInput schema / properties / sector / description
      Added value: +"Filter by sector, e.g. \"Technology\", \"Healthcare\", \"Financial Services\"."
    • addedInput schema / properties / sma_trend / description
      Added value: +"\"above_200\" (price above 200-day MA) | \"below_200\"."
    • addedInput schema / properties / sort_by / description
      Added value: +"\"market_cap\" | \"rsi\" | \"perf_1d\" | \"volume\" | \"analyst_rating\" | \"rating\" | \"ai_score\" (pro). Defaults to \"market_cap\", except the \"high_conviction\" preset defaults to \"ai_score\" (the dimension it's filtering by) unless you explicitly pass a different sort_by. Sorting by rating/ai_score silently falls back to market_cap on free/guest."
    • addedInput schema / properties / sort_dir / description
      Added value: +"Sort direction: \"asc\" | \"desc\" (default \"desc\")."
  4. Changed2 schema fields changed
    • addedInput schema / properties / min_ai_score
      Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
    • removedInput schema / properties / min_flag_score
      Removed value: -{
-  "anyOf": [
-    {
-      "type": "integer"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null
-}
  5. Changed3 schema fields changed
    • addedInput schema / properties / sort_by / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / sort_by / default
      Previous value: -"market_cap"New value: +null
    • removedInput schema / properties / sort_by / type
      Removed value: -"string"
  6. Changed3 schema fields changed
    • addedInput schema / properties / analyst_rating
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
    • addedInput schema / properties / country
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
    • addedInput schema / properties / min_volume
      Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  7. Added

TDQS

A4.2/5.0
Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), and the description adds substantial non-obvious behavior: min_ai_score is silently ignored on free tier, sort_by silently falls back to market_cap for free/guest on rating/ai_score, limit counts each returned stock against a daily quota, and non-USD listings without an FX rate are excluded when cap bounds are set. It also covers sort_by default overrides per preset and the naming history of min_ai_score/flag_score.

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

Conciseness3/5

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

Well front-loaded and sectioned (purpose, parameters, returns, tier notes), but it restates essentially every one of the 18 parameter descriptions nearly verbatim from a schema that already has 100% coverage, which bloats the definition considerably. The genuinely additive material (tier gating, preset/sort interaction, FX caveat) is buried among redundant restatement.

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?

For a 18-parameter, tier-gated tool with many interacting options, the description covers filter semantics, defaults, preset compositions, tier limitations, the return shape and its per-tier fields, and the follow-up tool for single-symbol depth. Despite an output schema existing, the tier-conditioned return differences are helpful to state. Nothing needed to call it 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 goes beyond the schema in meaningful ways: the market_cap vs market_cap_usd distinction and the FX-exclusion side effect of cap bounds, the preset-driven sort_by default, and the ai_score vs signal_score distinction with the retired flag_score rename. Duplicated filter examples add less, but the extra semantics are real.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource+scope: "Filter and rank stocks from the Stocklake universe - fundamentals, technicals, and AI signals in one tool." An agent immediately understands this is a screening/ranking tool rather than a lookup. It never explicitly contrasts itself with the closest siblings (get_stocks, get_signals), so it stops short of a 5.

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?

Gives clear operational context: preset definitions and what each preset filters on, tier availability, and an explicit hand-off ("For the full research bundle on any one result, call get_stock_research(symbol)"). It does not state when to prefer this tool over get_stocks or get_signals, so the routing guidance is incomplete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Stocklake — AI Stock Intelligence toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources