get_insider
SEC Form 4 insider transactions as filed (80 symbols, since 2016): officer/director trades with shares, price, post-trade holdings.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| start | No | ||
| symbol | Yes |
market-data
by ai.iturri
TDQS
B3.3/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the only source of behavioral context. It discloses that data is 'as filed' and limited to 80 symbols since 2016, but does not explain how start/end date parameters affect results, response format, or update frequency. This is a moderate level of disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence that front-loads the core purpose and concisely includes scope, time range, and data fields. Every word adds value without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no annotations or output schema, so the description must provide comprehensive context. It gives a high-level overview but omits parameter semantics, usage conditions, and return structure. This leaves a significant gap for an agent to correctly invoke and interpret the tool's output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the three parameters (symbol, start, end). Symbol is required but its format is not mentioned; start/end are present but completely unexplained. The description fails to compensate for the lack of schema documentation.
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 returns SEC Form 4 insider transactions, specifying the data scope (80 symbols, since 2016) and content (officer/director trades, shares, price, post-trade holdings). This distinguishes it from sibling tools like get_institutional and get_fundamentals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for insider transaction data but provides no explicit guidance on when to use this tool over siblings or when not to use it. Alternatives like get_institutional are not mentioned, leaving the agent to infer from the tool name and data type.
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
B3/5.0
Disambiguation4/5
Most tools clearly target a distinct data resource: bars, events, fundamentals, funding, open interest, order flow, and so on. A few adjacent tools like audit_my_data and validate_backtest_data, or get_market_pulse and get_regime_label, are somewhat similar, but their descriptions provide enough separation for an agent to choose correctly.
Naming Consistency4/5
The dominant pattern is get_<data_type>, used consistently across most tools and all in lowercase snake_case. The non-get tools are mostly still readable verb-noun names like build_bundle and validate_backtest_data, though lookahead_check and survivorship_check are minor deviations.
Tool Count3/5
With 22 tools, this is on the heavier side for a single MCP server, especially since many tools have fairly specialized data sources. Each tool is individually justifiable, but the overall surface is large and may push agents to spend extra work choosing among near-adjacent data options.
Completeness4/5
The server covers far more than plain OHLCV: it includes fundamentals, insider and institutional ownership, funding rates, open interest, order flow, events, context, regime labels, and backtest-quality validation. Minor missing areas like trade-by-trade quotes or a broader symbol catalog mechanism exist, but the common market-data workflows are very well supported.