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Resolve an agent's chain of accountability (free)

resolve_agent_identity
Read-onlyIdempotent

Trace who stands behind an autonomous paying agent before accepting payment by checking agent, principal, mandate, credential, and wallet links to find where accountability stops.

Instructions

Resolves who stands behind a paying agent: agent → principal → mandate → credential → wallet. Each link reports whether it holds, and completeness counts how many do — it is a factual count, not a score. Wherever the chain breaks is where accountability stops, which is what you actually need to know before accepting a payment from an autonomous agent. Publication is opt-in and only posture is exposed (that a ceiling exists, never its value), so found:false means the agent has not published — absence of information, not a bad signal. Free, no API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentIdYesThe agent id to resolve.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.1/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), it discloses rich behavioral semantics: completeness is a factual count not a score, a broken link marks where accountability stops, publication is opt-in with only posture exposed (ceiling existence, never its value), and found:false means absence of information rather than a negative signal. This is exactly the extra context annotations can't convey.

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 on the longer side but front-loads the purpose before moving into behavioral nuance, and each sentence (completeness semantics, opt-in publication, found:false meaning, free/no-key) carries distinct information. Minor redundancy with the title's 'free' claim keeps it from a 5.

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?

There is no output schema, so the description must explain return values, and it does: the per-link hold status, the completeness count, and the meaning of found:false. For a single-parameter resolution tool this is complete enough to call and interpret correctly.

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?

With a single parameter and 100% schema description coverage, the schema already defines agentId fully. The description explains what the resolved subject is but adds no syntax, format, or constraint detail beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (resolves) and resource (the chain of accountability behind a paying agent), enumerating the chain links agent → principal → mandate → credential → wallet. This is more specific than a bare restatement, but it does not distinguish itself from siblings such as deep_trace or payee_decide, so no routing help is offered.

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?

It gives a clear use context — gathering what you need before accepting a payment from an autonomous agent — which implies the trigger condition. However, it names no explicit alternative tool or when-not-to-use case, so an agent must still infer when another sibling is a better fit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.