Skip to main content
Glama

Announced vs executed sales

planned_vs_executed
Read-onlyIdempotent

Cross-feed signal: pair each Form 144 notice (an insider's ANNOUNCED sale) with the seller's actual Form 4 sale executions - same person, exact CIK identity, matched inside the notice's factual Rule 144 validity window (90 days from filing). Surfaces execution ratios and, most notably, announced-but-never-executed sales: insiders whose notice expired with no sale. Raw EDGAR cannot answer this in one query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows per page, default 50, cap 500.
sinceNoEarliest approximate sale date, YYYY-MM-DD. Default: 90 days back - pass an explicit date to reach deeper history.
untilNoLatest approximate sale date, YYYY-MM-DD.
cursorNoOpaque pagination token from the previous response's page.next_cursor; pass it back verbatim to fetch the next page.
sellerNoSubstring match on the seller's name.
statusNoFilter: executed, pending (window still open), not_executed (window elapsed, no matching sale - the leading-indicator residue), unverifiable (the seller entity never files Form 4 - trusts and foundations report via the beneficiary), or all (default).
tickersNoTicker symbols to include. Empty = all issuers.
min_planned_usdNoMinimum planned sale value in USD.

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent behavior. The description goes further by disclosing the exact matching criteria (same CIK, 90-day window), the meaning of the status categories, and the key insight about expired notices with no sale. This is rich behavioral context beyond what annotations provide, and there is 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.

Conciseness5/5

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

Two dense, information-packed sentences. The opening phrase 'Cross-feed signal' immediately sets the tool apart. Every clause earns its place, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex cross-feed tool with no output schema, the description gives a solid conceptual overview, including what it surfaces (execution ratios, never-executed sales) and the matching logic. However, it does not describe the response structure or pagination details beyond the schema, and given the tool's complexity, a bit more about return format would complete the picture.

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?

Schema description coverage is 100%, so the baseline is 3. The description reinforces concepts like 'not_executed' and 'unverifiable', but it does not add syntax or format details beyond what the schema already provides. It adds narrative context but not parameter-specific semantics.

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 uses a specific verb ('pair'), names the exact resources (Form 144 notices, Form 4 executions), and clearly states the output (execution ratios, announced-but-never-executed sales). It distinguishes itself from sibling tools by emphasizing the 'cross-feed' nature and that raw EDGAR cannot answer this in one query.

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 clearly implies when to use this tool (when comparing planned vs executed insider sales) and provides context about the matching logic. However, it does not explicitly name alternative tools such as get_planned_sales or get_insider_trades, nor does it state when-not-to-use it, so it falls short of a 5.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: per-form search tools (get_insider_trades, get_material_events, get_planned_sales, get_stakes), cross-feed signals (cluster_buys, planned_vs_executed, trades_before_events), and support utilities (company_profile, describe_coverage, find_company, list_recent_filings). The only slight overlap is company_profile vs. get_insider_trades, but company_profile is explicitly an aggregator that routes to detailed tools, eliminating ambiguity.

Naming Consistency4/5

Most tools follow a verb_noun pattern, especially the get_* family (get_insider_trades, get_material_events, etc.). However, three tools deviate: company_profile (noun_noun), planned_vs_executed (phrase with 'vs'), and trades_before_events (noun_phrase). These are still readable and clearly named, but they break the otherwise consistent convention.

Tool Count5/5

11 tools is well-scoped for the domain of insider trading analytics. It provides a complete set of per-filing-type search tools, cross-feed signals, and support utilities without redundancy or bloat. The count is within the ideal 5-15 range and feels justified.

Completeness5/5

The tool set covers the full lifecycle of insider data: entity resolution (find_company), orientation (describe_coverage), all major SEC forms (3/4/5, 8-K, 144, 13D/13G) via dedicated search tools, and advanced cross-feed analytics (cluster_buys, planned_vs_executed, trades_before_events). No obvious gaps are apparent for the stated purpose.

Resources