Skip to main content
Glama

decision_card_validate

Validates AI Procurement Decision Card JSON documents against v0.1 schema, enforcing conditional rules for approval statuses and publication requirements.

Instructions

Validate an AI Procurement Decision Card JSON document against the v0.1 schema. Enforces conditional rules: status=approved-with-conditions and status=rejected-with-remediation require at least one entry in conditions; status=withdrawn requires a withdrawal block; publication.is_public=true requires publication_uri.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_jsonYesDecision Card as inline JSON.
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses that it 'enforces conditional rules', which is behavioral. However, it does not explain return behavior on validation failure/success, side effects, or performance characteristics. Adequate but not comprehensive.

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?

Two sentences, front-loaded with purpose, then lists specific rules. No fluff or repetition. Every sentence provides necessary information.

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?

Given no output schema and one parameter, description covers validation rules well. It lacks explicit mention of return format (e.g., success/failure, error details), which would be helpful for completeness. Still, the core behavior is clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds value by detailing the conditional rules that govern validation (e.g., status-specific requirements, publication rules). This context goes beyond the schema's parameter description.

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?

Description clearly states it validates 'AI Procurement Decision Card JSON document against the v0.1 schema', specifying the exact resource and action. It lists conditional rules, distinguishing it from sibling tools like decision_card_inspect or other card validation tools.

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?

Description implies use for validation but does not explicitly state when to use vs alternatives like decision_card_inspect or decision_card_fetch. No 'when not' guidance is provided, though the purpose is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mizcausevic-dev/mcp-kinetic-gain'

If you have feedback or need assistance with the MCP directory API, please join our Discord server