Onça Setorial

Regulatory base rates (Outcome Ledger), labelled estimativa

base_rates

Read-onlyIdempotent

How long regulatory processes take and how they end, measured over each regulator's own records (e.g. ANEEL fine → appeal → decision; ANVISA public consultation → published rule): N, median, quartiles and range per figure, with method and limits. Figures are estimativa; a figure below the minimum N is withheld and has no number. Use for 'how long until ANVISA turns a consultation into a rule'. Deterministic: computed from official datasets, no model in the loop.

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": "base_rates"
  }
}

Input Schema

Name	Required	Description	Default
`study`	No	Optional. A study id (e.g. 'aneel_enforcement'), a regulator ('ANVISA') or a sector. Default: every study for your sectors.

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 cover read-only/idempotent/non-destructive, but the description adds substantive behavior the schema cannot: figures are labelled estimativa, any figure below the minimum N is withheld and has no number, and the method and limits are included per figure. The determinism claim (computed from official datasets, no model in the loop) tells the agent how much to trust the output.

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?

Front-loads the core claim before examples, usage and trust caveats, and every sentence carries information (examples, suppression rule, determinism). The opening is a long, colon-and-parenthesis-heavy sentence, so it reads denser than necessary but wastes no content.

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?

With only one optional parameter and no output schema, the description supplies the missing return shape (N, median, quartiles, range per figure, plus method and limits) and the suppression rule for small samples. Nothing an agent needs to interpret or trust the response 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 coverage is 100% and the single `study` parameter is already documented as accepting a study id, a regulator or a sector with a default of all studies for the caller's sectors. The description reinforces the per-regulator framing but adds no new syntax, resolution order, or fallback behavior beyond the 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?

States a specific measurement subject (duration and termination of regulatory processes) plus the unit of analysis (each regulator's own records), with two concrete pipeline examples (ANEEL fine → appeal → decision; ANVISA consultation → rule). The mention of N, median, quartiles and range makes clear this is an aggregate statistics tool, not an event listing, which separates it from siblings like regulatory_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?

Explicitly gives a use case in the quoted form "how long until ANVISA turns a consultation into a rule", which is a strong positive signal for selection. It stops short of naming alternatives or stating when not to use it (e.g. when raw timelines rather than distributions are wanted).

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

All Onça Setorial tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?