Trillboards DOOH Advertising

validate_request

Validate a proposed request payload against the registered Zod schema for an operation, returning the exact canonical error envelope the HTTP surface would emit.

WHEN TO USE:

Before calling a write endpoint, to catch payload bugs locally.
Debugging 400 validation_error responses.

RETURNS:

valid: true when the payload would pass Zod validation.
When invalid, the canonical { error: { type, code, message, param, doc_url, details[] } } envelope is included under error.

EXAMPLE: validate_request({ path: "/v1/data/query", method: "POST", payload: { dataset: "inference_outcomes", limit: 9999 } })

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>",
      "method": "<method>"
    },
    "name": "validate_request"
  }
}

Input Schema

Name	Required	Description
`path`	Yes	Operation path (OpenAPI template form).
`method`	Yes
`payload`	No	Shape is operation-dependent. GET → { query?, params? }. POST → body + optional { query?, params? }.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the return shape (valid flag and error envelope fields), the local validation nature ('before calling a write endpoint'), and shows an example. It stops short of explicitly stating it makes no network request, but the 'before calling' phrasing and example imply a safe, non-mutating operation.

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 well-structured with clear sections (purpose, when to use, returns, example) and front-loads the core purpose. It is somewhat longer than strictly necessary, but each section adds value, and the example is directly useful. No redundancy or fluff.

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?

For a 3-parameter tool with no output schema, the description provides a thorough overview: purpose, usage scenarios, return format (including the error envelope structure), and a concrete example. Minor gaps exist (e.g., not explicitly stating that payload is optional for GET), but the schema fills in some of this, and the description is otherwise complete enough for an agent 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 description coverage is 67% (path and payload have descriptions, method does not). The description adds an example showing how to pass path, method, and payload, which clarifies usage, but it does not systematically document the 'method' parameter or explain that payload is optional (as the schema does). This is adequate but not exceptional.

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 a specific verb ('validate') and resource ('proposed request payload against the registered Zod schema'), and distinguishes the tool from siblings by emphasizing its role in validating payloads and returning the canonical error envelope. This is unique among the listed sibling tools, many of which are data-fetching or mutation operations.

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?

The 'WHEN TO USE' section provides two concrete scenarios (before calling write endpoints, debugging 400 responses) that clarify appropriate contexts. It does not mention alternatives or when not to use, but the context is clear enough for an agent to recognize this as a validation tool rather than a general-purpose helper.

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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