Reg Monitoring

Get update detail

regmonitoring_get_update

Read-onlyIdempotent

Full detail for one regulatory update, by id or slug: the AI analysis (executive summary, what changed, who is affected, actions required, key dates, compliance impact, citations), any deadline, any enforcement penalty data, and related records. Call this after regmonitoring_search_updates when you need to judge what an update actually requires. Check the analysis depth field: "deep" means real structured analysis, "rationale" means only a short classification note. Set include_source_text to read the original document text.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "regmonitoring_get_update"
  }
}

Input Schema

Name	Required	Description
`id`	No	Record id, as returned by regmonitoring_search_updates.
`slug`	No	URL slug, the last path segment of a regmonitoring.com/updates/... link. Use this or id; id is faster.
`include_source_text`	No	Include the full scraped text of the source document. Off by default because it can run to tens of thousands of characters. Present on ~72% of records; ask for it when the analysis is not enough to judge impact.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 5, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower, yet the description adds substantial value: it explains the analysis `depth` field semantics ('deep' vs 'rationale'), the ~72% presence rate of source text, and that source text can run to tens of thousands of characters. With no output schema, this is exactly the return-shape disclosure an agent needs.

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?

Three sentences, front-loaded with purpose and followed by usage and a key output caveat. The first sentence is dense with a parenthetical list but every element earns its place; no filler or repetition.

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?

For a read-only detail tool with no output schema and complex structured returns, the description fully compensates by enumerating the analysis components, deadline/penalty/related-record presence, the depth field caveat, and the source-text option behavior. Nothing an agent needs to call it correctly or interpret the result is missing.

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 schema already documents all three parameters, including 'id is faster' and the source-text size caveat. The description mostly restates the include_source_text purpose and does not add syntax or format details beyond the schema, making the baseline 3 appropriate.

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?

States a specific verb and resource ('Full detail for one regulatory update, by id or slug') and enumerates the returned contents (AI analysis, deadline, penalty data, related records). It clearly distinguishes itself from regmonitoring_search_updates, which it names.

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

Usage Guidelines5/5

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

Explicitly routes the agent: 'Call this after regmonitoring_search_updates when you need to judge what an update actually requires.' It also gives a condition for the include_source_text option ('when the analysis is not enough to judge impact'), leaving little inference needed.

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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