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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

claim_agent

Claim a verifiable identity — bind an agent to your organization so its trust and accountability record is provably yours. No human in the loop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
org_idNoOptional. The organization to claim the agent into (e.g. `org-...` or `pers-...`). The caller must be a member of this org (role floor: member). If omitted, the agent is claimed into the caller's personal org.
agent_idYesAgent identifier (e.g. smolt-abc123)
hash_proofYesAgent possession proof — either the live birth token (`mnbt_…`) whose row pins this agent's hash, or the full 64-hex SHA-256 digest of `${apiKey}|${agentName}` (or `${apiKey}` for an unnamed singleton agent).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
org_idYesThe organization the agent was claimed into (echoes the resolved org — the supplied `org_id`, or the caller's personal org when omitted).
claimedYes
agent_idYes
claimed_atYes

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate this is not read-only and not idempotent. The description adds valuable context: 'No human in the loop' and 'provably yours' suggest a non-reversible, automatic binding that creates a persistent ownership record. This goes beyond what annotations provide, though it doesn't fully disclose edge cases like conflict resolution.

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 action, and every sentence adds value. There is no redundancy or filler, making it optimally concise.

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 presence of an output schema, 100% parameter coverage, and annotations, the description is fairly complete. It covers the purpose and key behavioral trait ('No human in the loop') but could ideally mention what happens post-claim (e.g., immutability) for full completeness. Still, for the complexity level, it is sufficient.

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%, with all three parameters (org_id, agent_id, hash_proof) already clearly documented. The description does not add any additional parameter-level semantics beyond what the schema provides, so the baseline score of 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 action ('Claim a verifiable identity — bind an agent to your organization') with a specific verb and resource, and distinguishes itself from siblings like verify_agent_binding by emphasizing the binding/claiming action rather than verification.

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?

Provides clear context for use ('bind an agent to your organization') and mentions the automated nature ('No human in the loop'), which implies when this tool is appropriate. However, it does not explicitly mention alternatives or when-not-to-use, so it does not earn a 5.

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.

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