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by io.audifact
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register_agent

Idempotent

Register or look up an agent identity under the API key's organization. When to use: before client-side signing flows that need a known public_key, or to attach a human-readable name to an existing key. When NOT to use: Managed Mode log_decision (pass agent_name instead — Audifact provisions the key); do not use this to log decisions or outcomes. Idempotent on public_key: repeating the same key returns the existing agent. Requires X-API-Key. Returns the agent record (id, public_key, name).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional human-readable agent name. On lookup of an existing key, may update the stored name when provided.
public_keyYesBase64 Ed25519 public key. Idempotency key — same value always resolves to the same agent in this org.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / name / description
      Previous value: -"Human-readable agent name."New value: +"Optional human-readable agent name. On lookup of an existing key, may update the stored name when provided."
    • changedInput schema / properties / public_key / description
      Previous value: -"Base64 Ed25519 public key."New value: +"Base64 Ed25519 public key. Idempotency key — same value always resolves to the same agent in this org."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare idempotentHint=true and destructiveHint=false, and the description reinforces idempotency with specificity ('same value always resolves to the same agent'), plus adds auth requirements (X-API-Key) and the return shape, which annotations do not cover.

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?

Front-loads the purpose, then structures when-to-use/when-not-to-use and behavioral notes compactly. Every sentence earns its place with zero redundancy.

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?

With no output schema, the description compensates by naming the returned fields (id, public_key, name), and it covers idempotency, auth, and routing. An agent has everything needed to invoke it 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?

Schema coverage is 100%, so both parameters are already documented in detail, including the name-update-on-lookup behavior and Base64 Ed25519 format. The description adds little parameter syntax beyond the schema, so the baseline 3 applies.

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?

States a specific verb (register/look up) plus resource (agent identity) and scope (under the API key's organization). It distinguishes itself from siblings like log_decision by explicitly telling the agent not to use it for logging decisions.

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?

Explicit 'When to use' and 'When NOT to use' sections name the alternative (Managed Mode log_decision with agent_name) and the condition that selects it. Nothing is left to inference.

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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