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Validate Content

validate_content

Run a 10-lens validation gate on AI-generated content, returning per-lens pass/fail, violation details, and a deterministic 0-10 score. Supports finance, marketing, legal, SEO, agency domains.

Instructions

Run the 10-lens validation gate on AI-generated content. Returns pass/fail, per-lens results, violation details, and a deterministic 0-10 score derived from violation counts (10 = clean, floors at 0; computed by the API tier layer, not by the LLM — the LLM-emitted 0-100 score was removed 2026-07-30). Requires SOULFIELD_API_KEY. Supports domains: general, finance, marketing, legal, seo, agency. WIRE CONTRACT (since 2026-08-10): each lens carries status (ran|skipped|error) and passed is null whenever no verdict was produced — parse passed===false as the violation signal, never !passed. HALT SEMANTICS: Rights runs first and halts the run on a critical violation; in a halted response downstream judged lenses read passed:null/status:skipped (unknown, not clean — consciousScan is non-blocking and keeps passed:true, its status:skipped is the never-ran signal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe AI-generated content to validate
domainNoDomain context for validation (default: general)
contextNoAudience/purpose context for Relevance lens (Lens 9). Omit to skip Relevance silently.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.1

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses the deterministic score semantics (10 = clean, floors at 0, computed by the API tier not the LLM), the precise wire contract (passed===false as the violation signal, never !passed), and halt semantics for Rights and consciousScan. This is far more transparent than a generic 'validates content' statement.

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 a dense paragraph covering purpose, outputs, score computation, authentication, domains, wire contract, and halt semantics — every sentence carries essential information and the core action is front-loaded. However, the contract and halt rules are packed into long sentences without visual structure like bullets, making it slightly harder to parse than necessary.

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?

This tool has no output schema and no annotations, so the description must explain return values and edge cases; it does. It covers pass/fail, per-lens results, violations, the 0-10 score, API key requirement, supported domains, the status/passed wire contract, and halt semantics. Nothing an agent needs for correct invocation and result interpretation is missing.

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 100%, so the baseline is 3. The description's domain list mirrors the schema enum exactly and adds no new parameter meaning. The 'context' parameter semantics come from the schema description (Relevance lens, Lens 9), not from the tool description. Thus the description adds no value beyond the schema for parameters.

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 uses a specific verb and resource: 'Run the 10-lens validation gate on AI-generated content.' It clearly states what the tool does and lists its outputs (pass/fail, per-lens results, violation details, 0-10 score). The '10-lens' framing distinguishes it from sibling single-lens tools like lens_catches_relevant, even though no sibling is explicitly named.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives prerequisites (requires SOULFIELD_API_KEY) and supported domains, which helps an agent know when it can be used. However, it never explicitly tells an agent when to choose this aggregate validation gate over a sibling single-lens tool, nor does it state exclusions. Usage must be inferred from the '10-lens validation gate' framing rather than being directly guided.

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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