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Signal8 MCP Server

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Get Policy Events

get_policy_events
Read-only

Filter and retrieve executive orders from the Federal Register, including sector-matched trade counts to identify potential co-occurrences for research.

Instructions

List mirrored executive orders (policy events) from the Federal Register feed. Filter by signing-date range, affected sector, or free-text title query. Each event includes its Federal Register document number (externalId), title, signing date (eventDate), normalized affected sectors, full-text URL, and flaggedTradeCount — the number of official trades that occurred in an affected sector near the signing date. 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
qNoFree-text search over event titles
toNoLatest signing date inclusive (YYYY-MM-DD)
fromNoEarliest signing date inclusive (YYYY-MM-DD)
limitNoMaximum results to return (default: 25, max: 100)
offsetNoPagination offset (default: 0)
sectorNoFilter by canonical affected sector (one of the 11 canonical sector strings, e.g. "Healthcare", "Financial Services", "Energy")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
Behavior5/5

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

Annotations indicate readOnlyHint=true, and the description adds behavioral context: it is a read operation, warns about match interpretation, and describes response fields. No contradictions.

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 about 5 sentences, front-loads the main purpose, and includes an important interpretation note. It is appropriately sized but could be slightly more concise.

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 6 well-documented parameters and an existing output schema, the description provides sufficient context including filtering options and result interpretation, making it complete for an agent.

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?

Input schema has 100% description coverage for all 6 parameters. The description adds meaning by explaining filtering by date range, sector, and title, and lists response fields (externalId, title, etc.), which adds value beyond schema.

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 explicitly states it lists mirrored executive orders (policy events) from the Federal Register feed, with filtering capabilities. It clearly differentiates from sibling tools, none of which cover policy events.

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 explains how to filter and importantly warns about interpretation of matches being broad co-occurrence, not evidence of foreknowledge. It does not provide explicit when-to-use vs alternatives, but no alternatives exist among siblings.

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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