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RTBlint

by io.github.aleksUIX

validate_artf_response

Read-onlyIdempotent

Validate an ARTF RTBResponse mutation set against the RTBRequest it answers: envelope id echo, declared intent against applicable_intents, operation and payload coherence, and whether each semantic path (/imp/{id}, /imp/{id}/pmp/deals/{id}, /user/data/segment, /seatbid/{seat}/bid/{id}) resolves to something the auction actually carries. With apply=true the mutations are written into the payloads and revalidated, reporting the OpenRTB findings the mutations introduced. Call this before proposing mutations to an orchestrator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
applyNoApply the mutations and revalidate the result (default false). Returns the mutated payloads and the findings the mutations introduced, with pre-existing findings filtered out.
payloadYesThe ARTF RTBResponse as a raw JSON string.
versionNoOpenRTB version id to validate against (default 2.6-202606). One of: 2.0, 2.1, 2.2, 2.3, 2.3.1, 2.4, 2.5, 2.6-202204, 2.6-202210, 2.6-202211, 2.6-202303, 2.6-202309, 2.6-202402, 2.6-202409, 2.6-202501, 2.6-202505, 2.6-202606, 3.0
rtb_requestYesThe ARTF RTBRequest envelope this response answers, as a raw JSON string. Required: a mutation is only meaningful relative to the auction it targets.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
validYes
issuesYes
appliedYes
versionYes
applicationNo
payload_typeYes

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context about apply=true causing mutations to be written and revalidated, reporting introduced findings, which goes beyond the annotations without contradicting them.

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 dense but well-structured, front-loaded with the primary purpose followed by specific validation checks, conditional behavior, and usage guidance. It's slightly long but every sentence adds meaningful information without redundancy.

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?

With an output schema present and full parameter coverage, the description sufficiently covers the tool's purpose, usage context, conditional apply behavior, and the specific validation paths. It's complete for an agent to understand when and how to invoke it, though slightly more detail on return structure could be added.

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 all parameters are described in the schema. The description adds context about the validation logic and the relationship between payload and rtb_request, but doesn't provide additional parameter-level meaning beyond what the schema already gives, so it stays at baseline.

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 the tool validates an ARTF RTBResponse mutation set against its RTBRequest, listing specific checks like envelope id echo and semantic path resolution. It distinguishes itself from sibling validators by focusing on mutation sets and referencing the orchestrator use case.

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 explicitly says 'Call this before proposing mutations to an orchestrator,' giving a clear when-to-use directive. The specific focus on ARTF RTBResponse mutation sets implies when this is appropriate versus other validators, though it doesn't explicitly exclude alternatives.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct aspect of the RTBlint domain: protocol discovery (get_adcp_capabilities), version enumeration (list_openrtb_versions), ARTF request and response validation (validate_artf_request, validate_artf_response), and OpenRTB payload validation (validate_bid_request, validate_bid_response). The descriptions clearly differentiate their purposes and invocation order.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: get_adcp_capabilities, list_openrtb_versions, validate_artf_request, validate_artf_response, validate_bid_request, validate_bid_response. The naming is uniform, predictable, and clearly indicates the action and target.

Tool Count5/5

With 6 tools, the set is well-scoped for the domain of RTB validation and protocol discovery. Each tool addresses a necessary function without redundancy or excess, making the server easy to navigate and integrate into an agentic pipeline.

Completeness4/5

The tool set covers key lifecycle steps: discovery, version selection, request validation, response validation, and cross-validation. A minor gap is the lack of a tool for validating mutations that don't involve ARTF, but the ARTF tools handle the core workflow well.

Resources