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autario-mcp

by io.github.Autario
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find_drivers

Read-onlyIdempotent

Rank candidate drivers of a KPI one at a time: given a target indicator + multiple candidates, order them by correlation strength. Perfect for "what moves my KPI?" questions. Returns ranked list with r, p-value, R² for each candidate. Maximum 30 candidates per call. Use decompose_drivers instead when the candidates may overlap. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

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": {
      "entity": "<entity>",
      "target_indicator": "<target_indicator>",
      "candidates": "<candidates>"
    },
    "name": "find_drivers"
  }
}

Input Schema

NameRequiredDescriptionDefault
timeNoOptional time window. "2010-2023" keeps 2010 through 2023, "2019-01-2024-06" works by month, and a single "2015" keeps 2015 onward. Omitted or unparseable = the whole series.
entityYesEntity code (e.g. DEU)
candidatesYesCandidate indicator IDs to test (max 30)
target_indicatorYesThe KPI you want to explain

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and closed-world, so the safety profile is covered. The description goes further by disclosing the return contents (r, p-value, R² per candidate), the 30-candidate cap, and the data sources it operates on. It does not discuss cost, latency, or how ties/missing data are handled, but that is a minor gap against strong annotation coverage.

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?

Front-loads the core verb and mechanism in the first clause, then layers purpose, return format, limits, alternative, and coverage. Every sentence carries distinct information with no filler.

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, so the description correctly fills that gap by naming the returned values (ranked list with r, p-value, R²). Combined with the candidate cap, the alternative tool, and the source coverage, an agent has everything needed to invoke it correctly.

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 all four parameters are already documented in the schema, and the description largely reiterates them (target indicator, candidates, 30 max). It adds only the framing that candidates are tested 'one at a time' and ranked, which is marginal over the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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: rank candidate drivers of a KPI by correlation strength, given a target indicator plus multiple candidates. It explicitly distinguishes itself from the sibling decompose_drivers, so an agent can route correctly without opening either schema.

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?

Provides the triggering scenario ('what moves my KPI?' questions), a hard constraint (maximum 30 candidates per call), and an explicit alternative with the selecting condition: use decompose_drivers instead when candidates may overlap. Nothing about when-not-to-use is left to inference.

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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