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HumanMirror Outcome

humanmirror_outcome

Execute one deterministic, independently verified outcome. Free dry-run quotes. 5 Nexus credits are kept only on verified success and refunded on execution or verification failure. Supports JSON cleanup, deduplication, entity normalization, data-quality thresholds, anomaly scans, secret scans, output-quality gates, schema compatibility, safe preflight and verifiable execution receipts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesJSON-serializable input for the requested outcome.
dry_runNoWhen true, return a free quote and do not reserve or consume credits.
optionsNoOptional operation-specific settings.
criteriaNoOptional measurable success thresholds.
objectiveNoNatural-language deterministic objective.
contract_idNoOptional Outcome contract ID returned by a dry run.
outcome_typeNoExplicit deterministic verification recipe.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses important behaviors: outcomes are deterministic and independently verified, dry runs are free, and 5 Nexus credits are only kept on verified success and refunded on failure. It does not explain side effects or return/receipt structure, but the disclosed credit and verification behavior goes well beyond a generic description.

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 description is compact and front-loaded with the core behavior, followed by credit mechanics and a feature list. Each sentence contributes useful information, and there is no filler. The final 'Supports...' list is somewhat dense, but overall the structure is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a complex 7-parameter tool with a 10-value enum, nested objects, and no output schema, the description provides a good high-level overview and credit policy but lacks guidance on how to construct criteria, objective, options, or how to interpret the execution receipt. It is sufficient for basic selection but not fully complete for correct invocation in all cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 adds value by mapping outcome_type enum values to plain-language operations (e.g., 'deduplication' → deduplicate_records, 'secret scans' → no_secrets, 'schema compatibility' → schema_compatible) and by clarifying dry_run through 'Free dry-run quotes.' It does not elaborate on input, options, criteria, or objective, but the schema already covers those.

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?

The description begins with a specific action ('Execute one deterministic, independently verified outcome') and then enumerates concrete supported operations such as JSON cleanup, deduplication, anomaly scans, and schema compatibility. The term 'outcome' is abstract, but the feature list and the outcome_type enum make the tool's purpose clear, especially given there are no sibling tools to distinguish from.

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?

Usage context is implied through the listed supported operations and the credit/dry-run policy, but there is no explicit statement of when to use this tool versus alternatives or how to choose among the ten outcome_type recipes. The description does not provide exclusions or decision guidance, so the agent must infer appropriateness from the feature list.

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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