Stocklake — AI Stock Intelligence

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 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 — 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

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Max results to return, 1-25 (default 20). Each returned stock counts as one call toward your daily limit.
`preset`	No	"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.
`sector`	No	Filter by sector, e.g. "Technology", "Healthcare", "Financial Services".
`country`	No	Filter by country, e.g. "United States", "Germany".
`max_rsi`	No	Maximum RSI bound (e.g. max_rsi=30 = oversold).
`min_rsi`	No	Minimum RSI bound (e.g. min_rsi=70 = overbought).
`sort_by`	No	"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_dir`	No	Sort direction: "asc" \| "desc" (default "desc").	desc
`sma_trend`	No	"above_200" (price above 200-day MA) \| "below_200".
`min_volume`	No	Minimum daily volume, e.g. 1000000.
`macd_signal`	No	"positive" (MACD line above signal) \| "negative".
`max_perf_1d`	No	Maximum 1-day performance %.
`min_perf_1d`	No	Minimum 1-day performance %, e.g. min_perf_1d=2.0 = up 2%+ today.
`min_ai_score`	No	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.
`analyst_rating`	No	Filter by analyst consensus rating: "strong_buy" \| "buy" \| "hold" \| "sell" \| "strong_sell".
`max_pe_forward`	No	Maximum forward P/E, e.g. 20 for a value screen.
`max_market_cap_b`	No	Maximum market cap in billions of US dollars (compared against market_cap_usd).
`min_market_cap_b`	No	Minimum market cap in billions of US dollars (compared against market_cap_usd, so non-USD listings are converted first).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 21, 2026

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)."

Changed18 schema fields changedAug 25, 2026

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\")."

Changed2 schema fields changedAug 24, 2026

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
-}

Changed3 schema fields changedAug 12, 2026
- 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"
```

Changed3 schema fields changedJul 25, 2026

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
+}

AddedJun 5, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already state readOnlyHint, destructiveHint, and idempotentHint, but the description adds substantial behavioral nuance: pro-tier filters are silently ignored on free accounts, sort_by falls back to market_cap for non-pro, market cap filters exclude stocks without FX rates, and each returned stock counts toward the daily limit. These are exactly the kinds of edge cases that affect invocation decisions.

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 long but well-structured: an opening one-liner, a bulleted parameter list, a Returns section, and a Pro-tier note. It's front-loaded with purpose. Some redundancy exists with schema descriptions (e.g., every parameter is re-explained), but the added context (like 'silently ignored for free') justifies the length. It could be tightened, but the organization helps scanability.

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 the tool's complexity (18 parameters, pro-tier gating, output schema), the description covers everything an agent needs to call it correctly: parameter semantics, defaults, tier limitations, output shape, and routing to get_stock_research. The output schema exists and the description still outlines the return fields, adding clarity on market_cap vs market_cap_usd. It's fully complete for this sophisticated tool.

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 baseline is 3. The description adds meaningful extra detail beyond the schema: e.g., for min_ai_score it explains the distinction from signals.signal_score and the rename history; for market_cap filters it notes the FX exclusion; for sort_by it clarifies the high_conviction default. These are not present in the schema, so the description adds value beyond it.

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's function: 'Filter and rank stocks from the Stocklake universe — fundamentals, technicals, and AI signals in one tool.' It names a specific verb (filter/rank) and resource (stocks), and the extensive parameter list distinguishes it from sibling tools like get_stocks or get_stock. It conveys a comprehensive screening capability.

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?

While it doesn't explicitly contrast with siblings, it provides clear guidance on when to use this tool (for screening/ranking) and points to get_stock_research for deeper research on a specific result. It also clarifies pro-tier vs free-tier behavior, which informs when to call it with certain filters. No explicit 'when not to use' is given, but the context is strong.

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 tools Inspect

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