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Decision Anchor

by com.decision-anchor
OverviewDiscussionsAdmin

get_environment_anomaly

Observe environment-level anomaly distribution: within_band/outlier counts per dimension across the population. De-identified, k-anonymity k>=10. Costs DAC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dimensionNoOptional dimension filter (decision_scale, decision_class, target_class, time_zone, ee_resolution)
auth_tokenNoYour DA agent auth token. Optional when this connection already carries one (Authorization: Bearer header on the remote server, or DA_AUTH_TOKEN for a local stdio server); an explicit value takes precedence.
period_daysNoWindow in days
payment_signatureNoOptional x402 payment payload (base64), required only for paid calls. Omit it on the first call: the tool returns the payment challenge. Sign that challenge with your own wallet, then call this tool again with identical arguments plus this field. Decision Anchor never holds your key and never signs on your behalf.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / auth_token / description
      Previous value: -"Your DA agent auth token"New value: +"Your DA agent auth token. Optional when this connection already carries one (Authorization: Bearer header on the remote server, or DA_AUTH_TOKEN for a local stdio server); an explicit value takes precedence."
    • removedInput schema / required
      Removed value: -[
-  "auth_token"
-]
  2. Changed1 schema field changed
    • addedInput schema / properties / payment_signature
      Added value: +{
+  "description": "Optional x402 payment payload (base64), required only for paid calls. Omit it on the first call: the tool returns the payment challenge. Sign that challenge with your own wallet, then call this tool again with identical arguments plus this field. Decision Anchor never holds your key and never signs on your behalf.",
+  "type": "string"
+}
  3. Added

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are present, so the description carries the behavioral disclosure burden. It meaningfully discloses de-identification, k-anonymity (k>=10), and DAC cost. It doesn't fully describe return shape or authorization behavior, but the privacy and cost traits are valuable context.

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?

Three short sentences, front-loaded with the core output and followed by essential privacy and cost caveats. Every sentence earns its place, with no filler or redundant schema repetition.

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?

With no output schema or annotations, the description provides the output concept and key constraints (de-identified, k-anonymous, cost), while the schema fully documents parameters including the payment challenge flow. It is adequate, though it could slightly strengthen agent confidence by naming when the payment challenge is triggered.

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 baseline is 3. The description's 'per dimension' phrase connects the dimension parameter to the output, but it adds no new parameter semantics beyond what the schema already documents for auth_token, period_days, and payment_signature.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb ('Observe') and resource ('environment-level anomaly distribution'), and specifies the output ('within_band/outlier counts per dimension') plus a privacy constraint. It doesn't explicitly differentiate from sibling tools such as observe_environment or compare_anomaly, so it stops short of a 5.

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

Usage Guidelines3/5

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

The description implies this tool is for examining population-level anomaly distribution and warns that it costs DAC, but it gives no explicit when-to-use guidance, exclusions, or alternatives. An agent must infer when to choose this over related observation/comparison tools.

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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