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Mnemom — Trust Ratings for AI Agents

search_reputation_directory

Read-onlyIdempotent

Resolve an agent name or id-prefix to a real agent_id over the PUBLIC reputation directory (only agents whose reputation visibility is public). Zero-auth. The arriving-agent entry point: discover a concrete agent_id, then call get_reputation / verify_reputation on it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoName search (ilike) or agent-id prefix match.
pageNo1-based page number for pagination. Default 1.
sortNoResult ordering. Default "score" (highest-rated first); other supported keys order by recency or name.score
gradeNoFilter to one grade (e.g. `AAA`, `B`, `NR`).
per_pageNoNumber of results per page. 1–100, default 20.
confidenceNoFilter to agents at a given reputation-confidence level (driven by how much evidence backs the score).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYes
totalYes
agentsYes
per_pageYes

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds key behavioral info: the tool only searches agents with public reputation visibility, requires zero authentication, and returns agent_ids. 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?

Two sentences, front-loaded with core action, followed by usage guidance. No redundant words. Every sentence earns its place.

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?

Covers purpose, scope (public directory), auth requirement, and next steps. With an output schema present, the description doesn't need to detail return format. Minor gap: does not explicitly state that results are paginated, but schema covers pagination params. Overall good completeness.

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?

All 6 parameters have descriptions in the schema (100% coverage), so the description adds no new param-level detail. It provides context for the 'q' parameter ('name or id-prefix') but that is already in the schema. Baseline 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 'Resolve an agent name or id-prefix to a real agent_id over the PUBLIC reputation directory', using a specific verb and resource. It distinguishes from siblings like get_agent (requires id) and list_agents (different scope), and positions itself as the entry point for discovering agent IDs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool: as the entry point to discover a concrete agent_id, then delegate to get_reputation/verify_reputation. It also notes 'zero-auth', setting expectations. This provides clear guidance on alternatives and preconditions.

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.