Skip to main content
Glama
signal8ai

Signal8 MCP Server

Official

Get Policy-Trade Leaderboard

get_policy_trade_leaderboard
Read-only

Rank politicians by trading activity around executive order signings, filtered by sector impact. Identify which officials trade most near policy events.

Instructions

Rank politicians (Congress + executive branch) by trades that occurred near executive-order signings in sectors the orders affect. Each row includes the politician, flaggedTradeCount, totalEstimatedUsd, topSector, and an exampleEvent. Use for "who trades most around policy activity" style questions. Defaults to the same "traded 1-14 days before signing" lens as get_policy_trade_overlap; same-day trades are always excluded. IMPORTANT: matches are sector-level co-occurrence — the official traded a stock in a sector the executive order affects, within a window of its signing date. Sector matches are broad and many trades will coincide with policy activity by chance; a match is a starting point for research, not evidence of foreknowledge. The matchBasis field describes match strength only ('sector' = broad sector match), never culpability, and matchCount shows how many EOs matched in the window (a noise indicator).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoRanking order: 'usd' (default — estimated USD value) or 'count' (flagged-trade count)
limitNoMaximum results to return (default: 50, max: 100)
offsetNoPagination offset (default: 0)
windowNoMatch window in days around the EO signing date (default: 14, max: 30)
directionNoWhich side of the signing date to include: 'before' (default), 'after', or 'both'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, but the description goes beyond by detailing sector-level matching, window defaults, direction options, and crucial caveats about matches being co-occurrence, not evidence. This adds significant behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is somewhat verbose (over 100 words) and includes detailed caveats. While front-loaded with the main purpose, it could be more concise without losing essential warnings.

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 5 optional parameters and an existing output schema, the description covers return fields (flaggedTradeCount, totalEstimatedUsd, etc.), match logic, and important limitations. It is complete for an AI agent to understand tool behavior.

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 coverage is 100%, so the baseline is 3. The description adds minor context (defaults, exclusion of same-day trades) but does not significantly enhance parameter understanding beyond the schema descriptions.

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 ranks politicians by trades near executive-order signings, using a specific verb ('rank') and resource ('politicians'). It distinguishes from the sibling get_policy_trade_overlap by noting the default lens and same-day exclusion.

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 explicitly says 'Use for <U+2018>who trades most around policy activity<U+2019> style questions.' It references a sibling tool but does not provide explicit when-not-to-use or alternatives. The context is clear but lacks exclusions.

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

Deploy Server

Other Tools