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richashworth

tlaplus-mcp

by richashworth

tla_evaluate

Evaluate constant TLA+ expressions by running TLC to compute and output the result, enabling quick checks of expressions without writing full specifications.

Instructions

Evaluate a constant TLA+ expression using TLC. Creates a temporary spec that prints the result of the expression.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expressionYesTLA+ expression to evaluate (e.g., '1 + 2', '{1,2,3} \\union {4,5}')
importsNoModules to EXTEND (e.g., ['Integers', 'Sequences']). Defaults to ['Integers', 'Sequences', 'FiniteSets', 'TLC']
output_fileNoOptional. If provided, raw TLC output is written to this file and the response contains output_file instead of raw_output.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that a temporary spec is created and the result is printed, but lacks details on error handling, side effects, or response format.

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, no wasted words, highly concise and immediately readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core mechanism and purpose, but lacks details on the return format and how output_file works, given no output schema and no annotations.

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 coverage is 100% with detailed parameter descriptions. The description adds minimal additional meaning beyond the schema, so a 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 verb 'evaluate' and the resource 'constant TLA+ expression', and distinguishes from sibling tools like tlc_check by focusing on a single expression evaluation rather than model checking.

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 use for constant expressions (no temporal logic, no state search) but does not explicitly state when to use or avoid it compared to siblings.

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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