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alethia-mcp

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by vitron-ai

alethia_tell

Execute natural-language E2E test steps on the current page, returning per-step results and a SHA-256 integrity hash. Blocks destructive actions and sensitive input unless explicitly allowed.

Instructions

Execute natural-language E2E test instructions against the page Alethia is currently driving. Returns per-step results, policy audit records, and a SHA-256 integrity hash. Destructive actions (delete, purchase, transfer, etc.) are blocked unconditionally. Sensitive input (passwords, credit cards, SSN) is blocked unless allowSensitiveInput is true. ~13 ms per step on average.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoShort label for the run (e.g. "signin smoke", "delete-blocked check", "checkout regression"). Strongly recommended — without it the run shows up as "tell" in cockpit history and audit trails, which makes review painful when many runs accumulate. Alternatively, prefix the first line of `instructions` with `name <label>` and the NLP compiler will read it.
instructionsYesOne or more plain-English test instructions, newline-separated. Example: "navigate to http://localhost:3000\nclick Sign In\nassert the dashboard is visible"
allowSensitiveInputNoSet to true to allow typing into password, token, credit card, and other sensitive fields. Only use for legitimate auth or payment flow tests.
Behavior5/5

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

The description discloses significant behavioral traits beyond the annotations: destructive actions are blocked unconditionally, sensitive input is blocked unless allowSensitiveInput is true, it returns per-step results, policy audit records, and a SHA-256 integrity hash, and mentions ~13 ms per step average performance. These details provide substantial transparency about safety constraints, output, and performance, going well beyond the simple boolean hints.

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 concise, four sentences long, and front-loaded with the core purpose. Each sentence provides distinct and valuable information: purpose, outputs, safety constraints, and performance. No redundant or filler content is present.

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 function, return values (per-step results, policy audit records, integrity hash), and key constraints (destructive block, sensitive input gate, performance). It also implies a prerequisite (the page currently being driven). However, it lacks a bit of detail on what 'policy audit records' contain or how results are delivered (e.g., output format), which would be useful given the absence of an output schema.

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%, and each parameter (name, instructions, allowSensitiveInput) has a clear description. The tool description adds no extra parameter semantics beyond the schema; it mentions allowSensitiveInput in behavioral context but does not provide additional syntax, formatting, or usage details that aren't already present in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb ('Execute') and resource ('natural-language E2E test instructions against the page Alethia is currently driving'). However, it does not explicitly distinguish from the sibling tool alethia_tell_parallel, which likely performs a similar action in parallel, so full differentiation is missing.

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 provides context for when to use it (running natural-language E2E tests) and mentions blocking of destructive actions and sensitive input, which implies careful use with flags like allowSensitiveInput. However, it does not explicitly state when to use this tool over alternatives like alethia_tell_parallel, and no exclusionary or alternative guidance is given.

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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