Onça Setorial

One enforcement process's timeline (Outcome Ledger)

enforcement_process

Read-onlyIdempotent

The dated steps of ONE sanctioning process from the regulator's own records: an ANEEL auto de infração (issued, received, appeal, decisions, closure), an ANS penalty case (1st and 2nd instance, payment, archiving) or an ANATEL PADO process (phases, dated when first observed). Returns the regulator, the source url, the company only when the record binds it by CNPJ to a tracked company, and each span's days next to that regulator's published base rate (median, quartiles, N; estimativa). Use for 'where is auto 0001/2019-AGEPAN-SFE and is it slow'. Needs the regulator's sector licensed (ANEEL energy, ANS health, ANATEL telecom). Deterministic: computed from official datasets, no model in the loop.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "process": "<process>"
    },
    "name": "enforcement_process"
  }
}

Input Schema

Name	Required	Description	Default
`process`	Yes	The process id: ANEEL auto number ('0001/2019-AGEPAN-SFE') or SEI process ('48500.026432/2025-01'), ANS demanda or processo ('33902.499206/2016-46'), or ANATEL PADO process number.
`regulator`	No	Optional. Restrict the lookup to one regulator.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

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, idempotent, non-destructive behavior, and the description goes well beyond that: it states data provenance (regulator's own records, official datasets), determinism ('no model in the loop'), the CNPJ-binding rule that decides whether a company appears, and that each span is shown against published base-rate statistics with N. These are material traits an agent could not infer from the annotations.

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 purpose is front-loaded in the opening clause and every subsequent sentence carries information (span list, return fields, CNPJ rule, rate comparison, prerequisite, determinism). It is dense but the trailing determinism sentence is somewhat promotional and could be tightened.

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?

There is no output schema, and the description compensates by enumerating the returned fields (regulator, source url, conditional company, days plus median/quartiles/N base rate) as well as the licensing precondition. For a single-lookup read tool, nothing an agent needs to call it correctly 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 both the process-id formats and the regulator enum are already fully documented in the schema. The description only reinforces the same formats and maps each regulator to a sector (energy/health/telecom), which is a marginal addition rather than new parameter meaning.

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 verb and resource ('dated steps of ONE sanctioning process') and enumerates exactly what that covers for each of three regulators (ANEEL auto de infração, ANS penalty case, ANATEL PADO), plus what is returned. An agent can distinguish this from consultation_outcome or rule_enforcement without opening any schema.

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?

It gives a concrete trigger ('Use for "where is auto 0001/2019-AGEPAN-SFE and is it slow"') and an explicit prerequisite ('Needs the regulator's sector licensed'). It stops short of naming a sibling alternative or a when-not-to-use condition, but the usage context is unambiguous.

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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