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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

get_agent

Read-onlyIdempotent

Look up an agent's public identity and trust state by ID — the accountable record other agents and humans can rely on.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesAgent identifier (e.g. smolt-abc123).
nameNoAgent name (2-32 chars, alphanumeric + hyphens).
callerNoWhich projection THIS response is. `org_member` receives the owner field set; `anonymous`/`authenticated` receive the reduced public set (id, name, claimed, created_at, last_seen, status, avatar_url, caller). Read this instead of inferring why a field is absent.
groupsNoActive groups this agent belongs to, name-ordered; `[]` when none. Present on org-fleet rows.
org_idNoThe agent's organization binding. Required as an input by the org-scoped tools (fleet listing, posture assignment). Identifies an organization, not a person.
publicNoWhether the agent's identity record is publicly discoverable. Distinct from Trust Rating visibility, which is always public.
statusNoDerived from last_seen (active = seen within the last hour).
claimedNoWhether a human or organization has claimed accountability for this agent. On the owner projection this is derived from the ownership column; the owning user's identifier itself is not returned.
last_seenNo
agent_hashNoThe canonical public identity hash (first 16 hex chars) used as the gateway lookup key and as the input to verify_agent_binding. Owner projection only. Not a credential and not reversible to one.
avatar_urlNo
claimed_atNo
created_atNo
containment_statusNoContainment state of the agent.
aip_enforcement_modeNo

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only nature is covered. The description adds that the lookup targets 'public identity and trust state,' providing some scope beyond annotations, but it does not disclose behavior for missing IDs, error cases, or data freshness, so the added transparency is modest.

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 a single, front-loaded sentence with no wasted words. It names the action, the resource, and the lookup key, while the final clause adds meaningful context about the tool's reliability as an 'accountable record.'

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?

For a one-parameter lookup with an output schema and strong annotations, the description is largely complete: it identifies what is looked up and why it matters. The only missing piece is explicit guidance about when to prefer sibling tools, but that is more a usage-guideline concern and does not undermine overall completeness for a simple read-only lookup.

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 has 100% coverage: agent_id is described with an example ('e.g. smolt-abc123'). The description only repeats 'by ID' without adding new parameter semantics, 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 states a specific verb and resource: 'Look up an agent's public identity and trust state by ID.' This clearly distinguishes it from siblings like list_agents (which lists) and get_reputation (which focuses on reputation), and it explicitly identifies the lookup key (ID).

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 use when one needs the accountable agent record, but it provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools like get_reputation or verify_agent_binding. It relies on context rather than explicit exclusions.

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.

Resources