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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

preview_compose_alignment_by_agent

Read-onlyIdempotent

Preview composed alignment (dry run) — Composes the cascade against a hypothetical body at the agent layer and returns conflicts + the composed view. No DB writes. Used by the dashboard editor for live conflict markers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
auditYesHow long this agent's own decision log is kept, and whether it can be queried. Required. (This is the agent's audit policy — it is NOT Mnemom's retention policy for the card itself; see the tool's data-handling disclosure for that.)
valuesYesThe values this agent declares it is bound by. Required.
agent_idYesThe agent this card belongs to (e.g. `smolt-abc123`). Identifier only — never place an API key, a secret, an email address, or any other personal data in this field.
autonomyYesWhat the agent may do on its own authority. Required.
principalYesWhose authority this agent acts under. Required.
card_versionYesCard schema version. REQUIRED by the server-side validator. Current canonical value: `unified/2026-04-26`.
autonomy_modeYesMaster switch for the action-policing pipeline. Required. `off` disables it; `observe` records only; `nudge` warns; `enforce` blocks.
integrity_modeYesMaster switch for the values pipeline. Required. Same four states as `autonomy_mode`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesTrue when composition succeeded (no blocking conflicts).
summaryYesOne-line human-readable summary of composition status.
full_reportNoOptional pointer to the full /v1 conflict report (method + path).
conflicts_countYesTotal number of conflicts detected (0 = none).
composition_validYesTrue when the composed card is coherence-valid.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description reinforces this with 'No DB writes' while adding that it composes against a hypothetical body and returns conflicts and a composed view. This adds useful context beyond annotations, though the meaning of 'cascade' and 'agent layer' could be clearer.

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?

The description is two sentences, front-loaded with the core purpose and dry-run nature, with no wasted words. It efficiently conveys the primary function, side effects, and intended usage.

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?

Given the tool's complexity (8 nested params, output schema exists), the description provides sufficient orientation: what it does, side effects, and typical use case. It does not elaborate on input semantics, but the schema covers that, and the output schema eliminates the need to describe return structure.

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 schema fully documents all eight parameters. The description does not add parameter-specific meaning, but the rich schema already handles that, so 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 states the tool performs a dry-run preview of composed alignment at the agent layer, returning conflicts and the composed view. It differentiates from sibling tools like put_alignment_by_agent (write) and preview_compose_protection_by_agent (protection vs alignment).

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 specifies it is a dry run with no DB writes and mentions its use by the dashboard editor for live conflict markers, implying a preview-before-commit context. It does not explicitly contrast against put_alignment_by_agent, but the use case is clear enough.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct operation: identity claiming, lookup, reputation retrieval/badge, scanning, verification, alignment/protection management, and feedback. No significant overlap exists.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern using snake_case (e.g., claim_agent, get_reputation, verify_scan). Even complex names like preview_compose_alignment_by_agent adhere to the pattern.

Tool Count4/5

With 16 tools, the set is slightly heavy but still well-scoped for the domain of AI agent trust ratings. Each tool serves a clear purpose, and no tool feels redundant.

Completeness4/5

The surface covers core workflows: agent identity, reputation, alignment/protection, scanning, verification, and feedback. Minor gaps like agent updates or deletion might exist, but the core lifecycle is complete.

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