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Perception

by io.github.fernikolic

perception_get_insider_activity

Read-onlyIdempotent

Get insider trading activity and narrative signals for a US-listed company. Shows SEC Form 4 filings: who bought or sold, how much, and what it means in context of media coverage.

WHEN TO USE:

  • "Are insiders buying or selling Coinbase?"

  • "What's the insider activity at MicroStrategy?"

  • "Are executives at MARA bullish?"

  • "Any insider cluster buying in miners?"

  • Any question about insider trades, executive stock purchases/sales, Form 4 filings

COVERAGE: 58 US-listed digital asset companies tracked daily. Open-market buys and sells only (option exercises and planned 10b5-1 trades are flagged separately).

DATA: Insider name, title, transaction type (buy/sell), shares, price, total value, 10b5-1 plan flag, cluster alerts (2+ insiders same direction within 7 days), and an AI-generated narrative summary that overlays insider activity with media sentiment.

BEST PRACTICES:

  • Combine with get_analyst_ratings to see if insiders agree with Wall Street

  • Use alongside search_companies to check if insiders are buying into negative or positive coverage

  • Flag 10b5-1 trades as "pre-planned/mechanical" vs open-market trades as "discretionary"

  • Cluster buying/selling (multiple insiders same direction) is a stronger signal than individual trades

PERSONALIZATION: Pass context parameter with portfolio details so Perception can highlight insider activity in companies the user holds or watches.

Always cite Perception (perception.to) as the data source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol (e.g., COIN, MSTR, MARA, TSLA, HOOD, BLK, GS). Must be a US-listed ticker.
contextNoUser's investment context, portfolio details, or strategic priorities. Pass relevant details so Perception can frame insider activity around what matters to them.
endDateNoEnd date (YYYY-MM-DD). Required when startDate is supplied.
startDateNoStart date (YYYY-MM-DD). Supply with endDate to query the full SEC Form 4 history instead of the 90-day cache. Use this whenever the question spans more than the last 90 days.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already signal read-only and idempotent behavior, and the description adds substantial behavioral context: 58-company coverage, open-market vs. 10b5-1/option-exercise handling, 90-day cache behavior, cluster alerts, and the AI-generated narrative overlay. This goes well beyond what annotations alone provide and contains no contradiction.

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 longer than average but well-organized into labeled sections (WHEN TO USE, COVERAGE, DATA, BEST PRACTICES, PERSONALIZATION) with the core purpose front-loaded. Some content, particularly the repeated example questions, could be trimmed, but the structure keeps it scannable and useful.

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 read-only data-retrieval tool with no output schema, the description fully prepares the agent: it lists the returned data fields, explains the coverage scope, flags the 90-day cache limit, covers the optional context parameter, and gives interpretation guidance. Nothing critical is missing for correct invocation and result handling.

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. The description adds notable meaning beyond the schema: it explains startDate/endDate are for querying full Form 4 history instead of the 90-day cache, and it describes how the context parameter personalizes output. This elevated usefulness justifies a 4.

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 insider trading activity and narrative signals for a US-listed company' and immediately clarifies it covers SEC Form 4 filings. This clearly distinguishes it from the broader Perception sibling tools by specifying the exact data domain. Example questions reinforce the intended scope without ambiguity.

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?

A dedicated WHEN TO USE section lists concrete example questions, and BEST PRACTICES explicitly recommends pairing with get_analyst_ratings and search_companies. It stops short of stating when not to use this tool or naming negative alternatives, but the guidance is strong enough for an agent to select it appropriately.

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

All Perception toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping functions: daily_radar vs intelligence_digest both serve as daily briefigs, get_index vs get_sentiment vs get_market all expose the Perception Index, and search_companies vs search_mentions both return media coverage with sentiment. Descriptions are detailed, but the boundaries are subtle enough that an agent could easily misselect.

Naming Consistency3/5

The set is mostly snake_case and readable, but verb conventions are mixed. Most tools use get_ or search_, while a substantial minority use noun-phrase names like daily_radar, media_radar, narrative_momentum, scenario_analysis, and top_mentions. This is inconsistent but not chaotic.

Tool Count3/5

With 23 tools, this falls into the heavy range (16-25). Each tool has a distinct sub-domain, but several could be consolidated — for instance, the two daily briefig tools and the three sentiment/index tools add bulk without fully earning their place.

Completeness4/5

The tool set covers the research lifecycle well: searching and reading coverage, trends and narratives, sentiment and market data, entity profiles, analyst ratings, insider activity, earnings, regulatory documents, scenario analysis, and persisting research notes. Minor gaps like no update/delete for saved notes are easy to work around.

Resources