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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

get_reputation_badge

Read-onlyIdempotent

Get an embeddable Trust Rating badge for an agent — returns the badge image URL plus ready-to-paste Markdown and HTML snippets for a README or agent card.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesThe agent the badge is for (echoed from the request).
badge_urlYesCanonical SVG Trust Rating badge image URL (always on api.mnemom.ai).
html_embedYesPaste-ready HTML badge snippet.
profile_urlYesHuman-readable reputation profile page (on www.mnemom.ai).
verified_urlYesPublic cryptographic verification URL for the rating.
markdown_embedYesPaste-ready Markdown badge snippet.

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds output format details (URL, snippets) but does not disclose additional behavioral traits like authentication requirements or rate limits beyond what annotations imply.

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?

A single, front-loaded sentence that conveys all necessary information without extraneous words. Every part contributes to understanding the tool's purpose and output.

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 (one parameter, clear output schema), the description fully covers what the tool does and returns. No additional context is needed for an agent to correctly invoke it.

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 a clear description for the single parameter 'agent_id'. The tool description does not add meaning beyond the schema, meeting the baseline of 3.

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 returns an embeddable Trust Rating badge, specifying the output includes badge URL and Markdown/HTML snippets. The verb 'Get' and resource 'badge' are specific, and it distinguishes from sibling tools like 'get_reputation' which likely return raw data.

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 usage when an embeddable badge is needed but does not explicitly state when to use this tool versus alternatives (e.g., 'get_reputation' for raw data). No guidance on when not to use it is provided.

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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