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LexRegPulse: Banking Regulation Brief

get_upcoming_deadlines

Upcoming regulatory deadlines from LexRegPulse's deterministic tracker: comment windows closing and rules taking effect, pulled from the Federal Register's structured fields rather than prose. Returns date, kind, title and agency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoWindow in days from today (default 21, max 90).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context by noting the tracker is deterministic, that data comes from structured Federal Register fields rather than prose, and that the result includes date, kind, title, and agency. However, it does not mention ordering, read-only nature, or any request constraints beyond the schema-covered 'days' parameter.

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 tight sentences, each with a distinct job: the first defines the resource and its source, the second lists the return shape. There is no filler, redundant wording, or restatement of the tool name.

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?

Given the single optional parameter and lack of output schema, the description sufficiently covers what the tool returns and where the data comes from. Minor details like sorting or empty-result behavior are not mentioned, but this is a simple deterministic query and the description is otherwise complete.

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?

The only parameter, 'days', is fully documented in the input schema with default and maximum values, so schema coverage is 100%. The description adds no additional parameter semantics, but the baseline for high schema coverage is 3.

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 names a specific resource ('upcoming regulatory deadlines'), enumerates the two kinds of deadlines (comment windows, rules taking effect), and identifies the source (Federal Register structured fields). It explicitly states the return fields, making it clearly distinguishable from sibling content tools like get_blog_posts and get_daily_briefing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool or when to prefer an alternative. The description does not reference sibling tools or state selection criteria; the only signal is the topic itself, forcing the agent to infer applicability.

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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