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aeoess

agent-passport-system-mcp

by aeoess

aps_aggregate_settlement

Aggregate attribution primitives over a settlement period into a signed SettlementRecord with Merkle commitments and residual buckets, ready for third-party verification.

Instructions

Aggregate a batch of Attribution Primitives over a half-open settlement period [t0, t1) into a signed SettlementRecord. Each axis (D, P, G, C) produces a per-contributor total with a balanced-Merkle commitment. Residual buckets pool sub-threshold contributors per Build A §4.1. Output is a fully signed record ready for third-party verification. Spec: BUILD-C-SETTLEMENT-PIPELINE.md.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodYes
receiptsYesArray of AttributionPrimitives to aggregate
issued_atNoOverride issued_at (canonical ISO-8601 UTC ms + Z); defaults to now
gateway_didYesGateway DID that signs the record
gateway_private_keyYesEd25519 gateway private key (hex)
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It explains internal steps (per-axis totals, balanced-Merkle commitment, residual buckets) and the output format (fully signed record), giving agents a good understanding of what the tool does without side effects being hidden. It stops short of explicitly stating safety or side-effect status.

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 concise and front-loaded with the primary purpose. Four sentences cover the main action, internal processes, and output. Each sentence adds useful information, though it could be slightly tighter without losing essential detail.

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?

The tool is complex with nested objects and no output schema, but the description explains the output type and high-level structure (axes, commitments, residual buckets). It also references a spec file. It does not elaborate on the receipts array elements or prerequisites, but overall it gives sufficient context for a capable agent.

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?

The input schema already has detailed descriptions for all parameters (80% coverage), including period semantics and gateway fields. The description adds context about the period being half-open and the signed output, but it does not deepen parameter-level meaning beyond the schema. Baseline 3 is appropriate.

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 clearly identifies the tool's function with a specific verb ('Aggregate') and resource ('Attribution Primitives' into a 'SettlementRecord'). It also details the half-open period, axis outputs, and residual buckets, making it distinct from siblings like aps_verify_settlement or generate_settlement.

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?

The description provides clear context by describing the input, process, and output, implying when to use it (for batch aggregation of primitives into a signed settlement). It does not explicitly mention alternatives or exclusions, but the context is strong enough to guide selection among siblings.

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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