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Aidress-ai
by Aidress-ai

protocol_reference

Look up worked examples for edge-case protocol flows on demand. Get step-by-step guidance for MCP handshake, capability confirmation, and advanced fields.

Instructions

Look up the worked example for an edge-case protocol flow, on demand.

Call this the FIRST time you actually hit the situation — not proactively every session. Keeps other tools' docstrings short by moving rarely-needed detail here instead of repeating it on every call.

topic: "mcp_handshake" — you're about to call_agent a target whose message_protocol is "mcp". Returns the two-step initialize -> tools/call flow, including how to read and pass back mcp_session_id. "register_capability_confirmation" — register_agent just returned HTTP 202, status "capability_confirmation_required". Returns the two-step confirm/reject flow to complete registration. "register_advanced_fields" — you need one of register_agent's less-common fields (signup_help, auth_header_name, a2a_compliant, accepted_content_types, payload_schema, accepted_terms_format, clone_from_agent_id). "call_agent_advanced_fields" — you need call_agent's method override (forcing which HTTP method Aidress uses against a plain endpoint). "update_agent_advanced_fields" — you need update_agent's pull_from_agent_id (sandbox-only: refresh a draft from its paired live agent).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states the tool 'looks up' and returns 'worked example' flows, which implies a read-only, non-destructive operation. It also details what each topic returns (e.g., 'two-step initialize -> tools/call flow'), providing useful context. However, it does not explicitly mention side effects or lack thereof, and omits return format/pagination, so it falls short of a perfect score.

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 concise yet comprehensive, front-loading the key instruction in the first sentence. It uses a clear, scannable list for topics, and every sentence serves a purpose—either explaining when to call or what to expect. No redundancy or fluff, so it scores high for structure.

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?

For a single-parameter reference tool, the description is complete: it covers all possible topics, when to use each, and what information they return. It also explains the design rationale (keeping other docstrings short), which helps the agent understand the tool's role. No output schema is provided, but the description sufficiently conveys the return value for each topic, so the context is fully adequate.

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?

The input schema only lists an enum without descriptions, but the description explains each enum value in detail with trigger conditions and expected content. This goes far beyond the schema, turning bare values into actionable guidance (e.g., 'register_capability_confirmation' is for when register_agent returns HTTP 202). The description fully compensates for the 0% schema description coverage.

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 this tool 'looks up the worked example for an edge-case protocol flow, on demand', which is a specific verb and resource. It explicitly differentiates itself from sibling agent-management tools by focusing on protocol reference and on when to call it. The topic list further clarifies the exact scope, making the purpose unmistakable.

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 gives explicit usage guidance: 'Call this the FIRST time you actually hit the situation — not proactively every session.' It also provides concrete scenarios for each topic (e.g., 'you're about to call_agent a target whose message_protocol is "mcp"'), which helps the agent decide when to invoke it versus relying on other tools.

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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