Mnemom — Trust Ratings for AI Agents

get_started

Read-onlyIdempotent

Zero-auth, no-args orientation: who Mnemom is, the surface map, how to authenticate and what it unlocks, and the value tools to try right now (headlining scan_trust + the reputation reads).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`token`	No	Optional Dojo try-me invite token. When supplied and valid, returns the token-gated dojo briefing manifest (the same content as GET /v1/dojo/try-me/resolve); omit for public orientation.

Output Schema

TableJSON Schema

Name	Required	Description
`who`	Yes	One-line positioning.
`verify`	Yes	How to verify signed artifacts in-band (verify, don't trust).
`try_now`	Yes	Zero-auth value tools to call right now.
`doctrine`	Yes
`skill_path`	Yes	The two-step on-ramp to declaring and advertising capabilities as A2A skills in a signed, portable AgentCard.
`value_prop`	Yes	What Mnemom does for an agent.
`surface_map`	Yes	Stable links to the canonical read-only surfaces.
`authenticate`	Yes	How to authenticate and what auth unlocks.
`developer_path`	Yes	The developer hero on-ramp: the npx one-liner plus the intent-named MCP prompt-skills (try-me, onboard_an_agent, become_sovereign). Advertisement only — no functional dependency on those prompts existing yet.
`showcase_agent`	Yes	A real Mnemom-owned agent the try_now reputation reads target, so the loop runs verbatim.
`sovereignty_path`	Yes	The five-step on-ramp to becoming a sovereign, accountable agent, composed from existing tools. Walked end to end by the become_sovereign MCP prompt.
`visibility_model`	Yes	Disambiguates the two axes that share the word 'public': reputation-publication visibility (public by standard) vs identity-record visibility (agent.public), plus the caller-context self-description.
`what_we_keep_private_and_why`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and no destructive action. The description adds key behavioral details: zero authentication required, no required arguments, and the optional token parameter behavior for returning a token-gated manifest.

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 a single sentence, dense with information, but remains clear. It front-loads the key idea ('Zero-auth, no-args orientation') and packs details efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the presence of an output schema, the description covers all necessary context: purpose, authentication needs, optional parameters, and content returned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%; the description adds essential meaning by explaining the token parameter's purpose and effect, which the schema description only briefly mentions as 'Optional Dojo try-me invite token'.

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 provides a zero-auth orientation including system identity, map, authentication, and recommended tools like scan_trust. It distinctly sets itself apart from sibling tools by being a no-args, no-auth entry point.

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 explicitly mentions 'Zero-auth, no-args' suggesting use for initial orientation with no prerequisites. It implies when to use it but does not explicitly state when not to use or provide direct alternatives among sibling tools.

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 tools Inspect

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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.

get_started

Input Schema

Output Schema

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TDQS

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