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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

get_reputation

Read-onlyIdempotent

Look up an AI agent's published Trust Rating — Mnemom's portable reliability signal for autonomous software, computed from the agent's own verified activity record. Returns the rating plus the technical factors behind it. Free, public, read-only: every registered agent's rating is published by standard (the visibility field is the reputation-publication axis, distinct from identity-record visibility).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent identifier (e.g. smolt-abc123)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNo
gradeYesAAA–D or NR.
scoreYes
claimedNo
agent_idYes
trend_30dNo
agent_nameNo
componentsYes
confidenceYes
visibilityYesReputation-publication axis — whether this agent's Trust Rating is published. Every registered agent's reputation is `public` by accountability standard (the default; that is the whole point of a portable, verifiable rating); `private` is a rare owner opt-out that 403s the read to non-owners. This is DISTINCT from `Agent.public` (the identity-record visibility axis) — they share the word "public" but govern different things.
computed_atNo
is_eligibleYes
next_compute_atNoNext scheduled recompute — the 00/06/12/18 UTC cron slot strictly after `computed_at` (`floor(computed_at/6h)*6h + 6h`). Null when `computed_at` is null.
checkpoint_countYes
a2a_trust_extensionNoA2A trust extension for interop. Only present on `GET /reputation/{agent_id}` (not on batch/compare rows).
checkpoint_accountingNoStructured breakdown of how checkpoints were counted toward the score. `analyzed` is the scoring population; `excluded` buckets are mutually exclusive and `analyzed + synthetic + insufficient_thinking + quarantined = total`. Null for legacy rows computed before this field existed.

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds that it's 'computed from the agent's own verified activity record' and clarifies the visibility field distinction, providing useful behavioral context beyond annotations.

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?

Two concise, front-loaded sentences with no wasted words. Each sentence adds value: main action, result, and key properties. Perfectly sized for quick comprehension.

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?

Tool has output schema, so return values are covered. Description provides enough context about the rating's nature, visibility, and computation. Minor gap: does not mention if the rating is numeric or categorical, but output schema handles this.

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 coverage is 100% with adequate description for agent_id (example format). Description does not add extra parameter meaning beyond the schema, so baseline 3 is appropriate.

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?

Clearly states 'Look up an AI agent's published Trust Rating' with specific verb and resource. Distinguishes from siblings like get_reputation_badge by mentioning 'returns the rating plus the technical factors', but does not explicitly contrast with other similar siblings like scan_trust.

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?

Provides context that it's 'Free, public, read-only', implying when to use (simple lookup). No explicit guidance on when not to use or alternatives like search_reputation_directory or verify_reputation, leaving some ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Mnemom — Trust Ratings for AI Agents toolsInspect
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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.

Resources