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Mnemom — Trust Ratings for AI Agents

by io.github.mnemom

report_recipe_fn_fp

Submit a false-positive / false-negative correction for one of Mnemom's automated detection rules (a 'recipe') — technical feedback that improves detection accuracy, like filing a bug report against a spam filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes`fn` = false negative (the recipe should have fired). `fp` = false positive (it fired on legitimate behaviour).
summaryYesA short description of what the recipe got wrong — what it flagged, or what it missed, and why that was incorrect. DESCRIBE the misfire; do NOT paste the conversation, the prompt, the raw payload or the log that triggered it. Do NOT enter personal data (names, email addresses, phone numbers, postal addresses), and do NOT enter health, biometric, government-identifier (e.g. SSN) or payment-card data. This text is stored on the agent's governance card and is readable by everyone in the owning organization.
agent_idNoOptional. The agent the report concerns. Identifier only.
recipeIdYesThe detection recipe the report is filed against (the one that misfired or failed to fire). Identifier only.
checkpoint_idNoOptional. The related integrity checkpoint, so the reviewer can correlate. Identifier only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
typeYes
candidate_idYes
related_recipe_idYes

TDQS

A4/5.0
Behavior3/5

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

Annotations already indicate this is a write operation (readOnlyHint=false) and not idempotent or destructive. The description adds the intended effect of improving detection accuracy but does not disclose side effects such as persistence on a governance card or visibility to the organization; that detail is deferred to the schema's summary parameter description.

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 that states the action, target, and purpose without redundancy. Every phrase earns its place, and it is appropriately sized for the tool's complexity.

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?

The description, combined with a fully covered input schema and an output schema, provides enough context for an agent to select and invoke the tool. It lacks an explicit 'when not to use' statement or mention of the governance-card storage detail, but those are not required given the structured schema information.

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 parameter descriptions already explaining `type`, `summary` constraints, and identifier formats. The main description adds little beyond the domain context of a 'recipe' being an automated detection rule, so it does not need to compensate significantly.

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 uses a specific verb ('Submit') and a specific resource ('false-positive / false-negative correction for one of Mnemom's automated detection rules (a 'recipe')'). It clearly distinguishes this tool from the sibling list, which contains no other submission/report tool.

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 gives clear context: this is for submitting technical feedback that improves detection accuracy, analogous to a bug report against a spam filter. It does not name explicit alternatives or exclusions, but no sibling appears to offer a similar reporting function, so the usage context is clear.

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.

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