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Aidress-ai
by Aidress-ai

review_transaction

Submit a trust review after a confirmed agent exchange; automatically locates the most recent unreviewed transaction and updates the receiver's trust score using success and rating.

Instructions

Submit a trust review after a confirmed exchange with another agent.

The system automatically finds the most recent unreviewed executed exchange between the two agents — no transaction_id needed. Reviews without a real prior /call exchange are rejected.

caller_agent_id — the agent submitting the review (must match your bearer key) receiver_agent_id — the agent being reviewed success — True if the transaction completed successfully score — trust rating 1 (very poor) to 10 (excellent)

Auth (always required): on the hosted remote connector, your own Authorization: Bearer header on the MCP connection is used automatically. Locally: set AIDRESS_AGENT_KEY env var before starting the server, or call set_agent_key("") once in-session after registering, or configure AIDRESS_KEYPAIR_PATH for Ed25519 HTTP Message Signatures (RFC 9421).

Anti-gaming rules enforced:

  • Caller trust_score must be >= 50

  • Cannot review your own agent

  • Cannot review agents from the same org domain (collusion block)

  • One review per executed exchange

  • No single org contributes more than 20% of an agent's rating influence; unaffiliated agents (no org_domain) are each capped at 10%

Returns the updated trust object for the reviewed agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoreYes
successYes
caller_agent_idYes
receiver_agent_idYes
Behavior5/5

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

With no annotations provided, the description carries the full transparency burden. It thoroughly discloses the action's effects: it modifies trust ratings, enforces anti-gaming rules, and returns the updated trust object. It also explains rejection conditions and auth requirements, leaving no significant behavioral ambiguity.

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 longer than average, but every section adds value: purpose, parameter semantics, auth options, and anti-gaming constraints. It is well-structured with clear separations, but the auth section is slightly verbose and could be tightened without losing essential guidance.

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?

The tool has moderate complexity with four required parameters, no output schema, and no annotations. The description fully covers all necessary context: what triggers a valid review, auth methods, constraints, and the return value. It leaves no major questions unanswered for an agent deciding whether and how to invoke it.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate, and it does. It explains each parameter: caller_agent_id (who submits), receiver_agent_id (who is reviewed), success (boolean for completion), and score (1-10 trust rating). It also clarifies that no transaction_id parameter is needed, which prevents incorrect parameter usage.

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 opens with a specific verb and resource: 'Submit a trust review after a confirmed exchange with another agent.' It clearly distinguishes this from sibling tools by noting that no transaction_id is needed and that reviews without a real prior /call exchange are rejected, which highlights its unique role in the trust review process.

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 states when to use the tool ('after a confirmed exchange') and lists prerequisites (caller trust_score >= 50, not self-review, not same org domain, one review per exchange). It also provides actionable auth setup instructions for both remote and local use, giving clear context for invoking the tool correctly.

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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