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lu_load_protocol

Parse Lingua Universale (.lu) protocol definitions to extract structured components including roles, steps, choices, and properties for formal verification.

Instructions

Parse a Lingua Universale (.lu) protocol definition.

Accepts the full text of a .lu file and returns the parsed protocol
structure: name, roles, steps, choices, and declared properties.

Args:
    protocol_text: Content of a .lu file, e.g.:
        "protocol RequestResponse:\n"
        "    roles: client, server\n"
        "    client asks server to process request\n"
        "    server returns response to client\n"
        "    properties:\n"
        "        always terminates\n"
        "        no deadlock\n"

Returns:
    JSON string with keys:
      ok (bool), protocol_name (str), roles (list[str]),
      steps (list), properties (list), error (str on failure).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
protocol_textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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 well by disclosing that it parses and returns a structured JSON output with specific keys, including error handling. However, it lacks details on performance, rate limits, or authentication needs.

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 appropriately sized and front-loaded, with a clear purpose statement followed by structured sections for arguments and returns, all sentences adding value without redundancy.

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?

Given the tool's complexity, no annotations, and an output schema present, the description is complete enough. It explains the input parameter thoroughly and details the return structure, making the output schema redundant but helpful.

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

Parameters5/5

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

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the parameter 'protocol_text' with a clear example and context, fully compensating for the schema's lack of 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?

The description clearly states the specific action ('Parse') and resource ('Lingua Universale (.lu) protocol definition'), and distinguishes it from siblings by focusing on parsing rather than checking properties, listing templates, or verifying messages.

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

Usage Guidelines4/5

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

The description implies usage context (when given a .lu file text) but does not explicitly state when to use this tool versus alternatives like lu_check_properties or lu_list_templates, nor does it provide exclusions or prerequisites.

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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