Stocklake — AI Stock Intelligence

Get Insider Activity

get_insider_activity

Read-onlyIdempotent

Get AI-synthesized insider + institutional activity for a stock. Returns combined signal (POSITIVE/NEGATIVE/NEUTRAL etc.), signal_score (0-100, higher = stronger/more notable — the exact same field, formula, scale and bands as get_signals()'s signal_score, no separate name) with signal_score_band, per-source breakdown, and a human-readable summary. Data covers insider transactions (SEC Form 4/BaFin/AFM/CNMV, with a gap-fill from Yahoo where the regulatory source has nothing) and institutional holdings — insider_buys/insider_sells, transactions (the individual rows behind those counts, newest first, up to 50, each with date/name/role/type/shares/price/value), top_holders (a union of multiple data sources, up to 15, each with name/shares/pct_held/sources/ share_counts_by_source — the last two show which source(s) contributed to a merged row and each one's own reported share count, useful for spotting a false merge: two genuinely different holders should never collide, but if they did, their per-source counts would diverge wildly), holder_count_divergence (null unless every matched holder disagrees by the same systemic multiplier across sources — e.g. an unrecorded reverse split — a signal top_holders.shares may be unreliable for this symbol; distinct from an ordinary two-holder mismatch, which is normal data lag and stays unflagged), inst_ownership (0-100%), and total_holders. Note: summary is a separately-generated narrative on its own refresh cadence and may not always match the live insider_buys/insider_sells/transactions counts — trust the counts/transactions over the prose if they disagree. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes	Stock ticker symbol to fetch insider (SEC Form 4) and institutional-holdings activity for.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description builds on this with rich behavioral detail: AI-synthesized data with gap-fill from Yahoo, a separately-generated summary on a different refresh cadence that may disagree with live counts, the holder_count_divergence caveat about systemic multipliers, and false-merge detection guidance. This goes well beyond what annotations provide.

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 every sentence adds operational value: signal_score equivalence, per-source breakdown, data-source caveats, summary lag warning, and Pro-tier cost. It is front-loaded with the core purpose and uses structured clauses to separate return fields from caveats. Slightly dense but appropriate for the tool's complexity.

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?

With an output schema present, the description still thoroughly explains return semantics: signal_score band, per-source breakdown, insider_buys/sells, transactions, top_holders merge behavior, holder_count_divergence, inst_ownership, total_holders, and caveats. It also covers access constraints (Pro tier) and data-source behavior. Nothing an agent needs to correctly call and interpret this tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter symbol has full schema coverage (100%), describing it as a stock ticker symbol for insider and institutional activity. The description adds no new meaning beyond the schema, which is the baseline case for complete coverage. The description's mention of SEC Form 4 is already in the schema description.

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: 'Get AI-synthesized insider + institutional activity for a stock.' It clearly distinguishes this tool from siblings by naming get_signals and emphasizing the combined insider/institutional scope, so an agent can confidently select it over get_stock, get_stock_research, or get_signals.

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?

The description gives clear context: it is for insider and institutional activity, is Pro-tier only, and is informational. It does not explicitly say 'use X instead when...', but it references get_signals's signal_score equivalence and implies the tool is the source for insider/institutional data. A small gap remains in not stating when to prefer get_stock_research or get_signals for a related use case.

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 targets—single stock, batch, news, history, insider activity, screener, market pulse, and so on. The closest pairs are get_earnings_calendar vs get_earnings_intelligence and get_market_assessment vs get_market_pulse, which overlap in raw inputs but differ in AI interpretation; the descriptions provide enough clarification to avoid serious misselection.

Naming Consistency5/5

All 17 tools follow a consistent get_<resource> snake_case pattern, with hierarchical names like get_stock, get_stocks, get_stock_news, get_stock_history, and get_stock_research making related resources obvious. There are no mixed naming conventions or vague verb choices.

Tool Count4/5

At 17 tools, the set is slightly above the ideal range, but the scope is broad and each tool has a plausible role in an AI stock intelligence workflow. The convenience aggregations like get_stocks and get_stock_research add some redundancy but remain purposeful rather than bloated.

Completeness5/5

The surface is comprehensive for a read-only AI stock intelligence domain: single-stock fundamentals and technicals, batch lookups, price history, per-stock and market-wide news, insider activity, screening, market pulse, macro assessment, sector intelligence, earnings, signals, and watchlist enrichment. No major operational dead ends are apparent.

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