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aarifmms

aarifmms/keyblind

store_secret

Store a secret in an encrypted vault using AES-256-GCM, so the value never appears in LLM conversation transcripts.

Instructions

Store a secret in the encrypted vault. The value is encrypted with AES-256-GCM before storage. The secret value is never visible in the LLM conversation transcript after this call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesA unique name for the secret (e.g., OPENAI_API_KEY, DATABASE_URL)
valueYesThe secret value to encrypt and store
Behavior3/5

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

The description reveals AES-256-GCM encryption and that the secret is hidden from the LLM transcript, which is valuable for security. However, it does not state whether storing a duplicate name overwrites or errors, nor does it mention persistence duration or side effects. Without annotations, the description carries the burden but covers some key behaviors.

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 two sentences, front-loaded with the core action, and every sentence adds value (encryption method and privacy guarantee). No unnecessary words.

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?

The description covers the core operation, encryption, and transcript invisibility. However, it omits what happens on duplicate name (overwrite or error) and does not mention the return value (e.g., confirmation or ID). Given the tool's simplicity (2 params, no output schema), it is largely complete but has minor gaps.

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?

The input schema already provides clear descriptions for both parameters (name and value), achieving 100% coverage. The description adds no additional parameter-level meaning beyond the schema, so the baseline score of 3 is appropriate.

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 action ('Store a secret') and the resource ('encrypted vault'), with specific details about encryption and transcript invisibility. It effectively distinguishes the tool from siblings like list_secrets, delete_secret, and resolve_secret.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., resolve_secret or list_secrets). The description does not specify prerequisites or context for usage, leaving the AI to infer without explicit direction.

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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