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Aethis-ai

aethis-mcp

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by Aethis-ai

aethis_generate_and_test

Generate rules from source text, poll generation to completion, run all test cases, and return pass/fail results with regression detection.

Instructions

Generate rules from source text and run all test cases. Triggers generation, polls until complete, then runs tests. Returns pass/fail with regression detection. Usually takes 60-120 seconds; if polling times out, use aethis_generation_status before retrying, and aethis_cancel_generation only when the caller wants to stop the run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
openai_keyNoRetired and refused: Aethis LLM tools use Anthropic models only.
project_idYesThe project ID
anthropic_keyNoAn Anthropic API key the user explicitly provided for this call. [sensitive — do not echo or log] Deprecated: the raw value is written verbatim to the host's session transcript. Never fill this from the environment.
anthropic_key_envNoOptional. Only honoured when it equals the env var the user configured via AETHIS_ANTHROPIC_KEY_ENV in this MCP server's config; that configured key is used automatically, so this can be omitted. Do not guess a variable name: the server refuses any name the user did not configure.
anthropic_key_keychainNomacOS keychain reference the user created for Aethis: either 'service:account' or just 'account' (service defaults to 'aethis-anthropic-key'). The server reads it via the `security` command at call time.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.22.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false and openWorldHint=true, so the safety profile is covered. The description adds genuinely non-obvious behavior: a 60-120 second runtime, synchronous polling until completion, and the timeout recovery path. It stops short of disclosing cost implications of an LLM-backed run or whether a partial/timed-out generation leaves state behind.

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?

Three tight sentences with no filler, front-loading what the tool does and then the operational caveats. Every clause (duration, timeout escape hatch, cancel condition) carries information an agent needs at call time.

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?

With no output schema, the description correctly takes on return semantics (pass/fail plus regression detection) and runtime/recovery behavior, which is the right coverage for a long-running compound operation. It could be more complete about prerequisites (a valid Anthropic key is mandatory per the schema) or what happens on a failed run.

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 the parameter docs are unusually rich (key-source precedence, deprecation and refusal notes). The description adds no parameter-level meaning of its own, so the baseline 3 for a fully documented schema 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 first sentence names a specific compound action (generate rules from source text + run all test cases), and the second clarifies the mechanism (trigger, poll, run). It reads distinctly from siblings like aethis_generation_status and aethis_cancel_generation rather than blurring into them.

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

Usage Guidelines5/5

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

It gives explicit routing conditions: on polling timeout use aethis_generation_status before retrying, and reserve aethis_cancel_generation for a caller who wants to stop the run. Both the alternative tool and the selecting condition are stated, leaving nothing to inference.

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