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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

scan_trust

Read-onlyIdempotent

Scan a website's agent-trust-readiness and return a signed scorecard (Trust, plus an Access axis on newer rubrics). Zero-auth. Results are CACHED for up to 24h — check cached and scannedAt on the result; pass fresh: true to force a re-scan (rate-limited). Proxies to the SSRF-locked isittrustready scanner; the Ed25519 signature + permalink are preserved verbatim. Rubric + docs: https://www.isittrustready.ai/rubric and https://docs.mnemom.ai/.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesDomain or URL to scan, e.g. "example.com" or "https://example.com".
freshNoForce a fresh re-scan instead of the cached result (results are cached up to 24h; the engine rate-limits re-scans). Equivalent to the scanner's rescan flag.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
gradeYesTrust letter grade (A+…F).
scoreYes0–100 weighted overall TRUST score.
accessNoThe independent Access/discoverability axis (never blended with Trust). Present from the two-axis rubric (0.3.0+).
cachedNoTrue when served from the scanner's 24h cache rather than a fresh scan.
schemaYesiitr-scan schema version string (e.g. "iitr-scan/v0.N").
targetYesNormalized host that was scanned.
permalinkNoShareable /r/ permalink (only on /r/ responses; transport field).
scannedAtNoWhen this scorecard was produced. Results are cached up to 24h — pass fresh:true to scan_trust to force a re-scan.
signatureYesEd25519 signature over the canonical result (transport field; stripped before verify).
categoriesNoTrust-axis categories with per-category scores + checks.
verificationNoSelf-describing in-band verification block {alg, kid, jwks, canonicalization} — how to verify this scorecard's signature. Self-describing, so signed-EXCLUDED (stripped before verify).
rubricVersionNoRubric version (e.g. "0.4.0").

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint false), the description adds significant behavioral details: results are cached for 24h, the `fresh` parameter bypasses cache but is rate-limited, it proxies to an SSRF-locked scanner, and preserves Ed25519 signature and permalink. There is no contradiction with 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?

The description is concise with 4-5 sentences, each serving a distinct purpose: core action, caching behavior, forced re-scan, proxy details, and a link to docs. There is no fluff, and the most critical information is front-loaded.

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 existence of an output schema (not shown), the description does not need to detail return values. It mentions result fields (cached, scannedAt), signature preservation, permalink, and a link to rubric docs. It also covers caching, rate limiting, and zero-auth. This provides a comprehensive understanding of the tool's behavior and output.

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% with basic descriptions for `url` and `fresh`. The description adds context: `fresh` is 'equivalent to the scanner's rescan flag' and explains caching implications. This adds value beyond the schema, raising the score above baseline 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 scans a website's agent-trust-readiness and returns a signed scorecard, specifying the verb 'scan' and resource 'website's agent-trust-readiness'. This distinguishes it from sibling tools like claim_agent, get_reputation, etc., which focus on agents and reputation rather than website scanning.

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 provides clear context: zero-auth, caching behavior, and the option to force a fresh scan with the `fresh` parameter. It implicitly tells when to use this tool (when you need a trust-readiness scan) but does not explicitly mention when not to use it or name alternatives. The sibling list contains verify_scan, which might be related, but no exclusion is given.

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