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The-40-Thieves

codecalc

by The-40-Thieves
OverviewSchemaRelated ServersScoreDiscussions
Python
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z3_check

Check an SMT-LIB2 formula to determine satisfiability: returns sat/unsat/unknown, a model, and a proof-grade for unsat with engine version and timeout bound.

Instructions

Check an SMT-LIB2 formula with Z3: sat/unsat/unknown plus a model. Example: '(declare-const x Int)(assert (> x 5))(check-sat)'.

unsat is graded solver_proven — see grade_basis for the engine version and timeout bound it was decided within. sat is graded ungraded: it's a real decided answer, just not a proof — reserving solver_proven for unsat means a counterexample can never wear a proof grade. unknown carries no proof either way and is also graded ungraded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
smt2Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed4 schema fields changedv0.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / smt2 / title
      Added value: +"Smt2"
    • addedInput schema / title
      Added value: +"z3_checkArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It explains the grading semantics thoroughly: unsat is solver_proven, sat and unknown are ungraded, and why a counterexample cannot wear a proof grade. This goes beyond basic expectations for a checker tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose and example, then adds useful grading context. The grading semantics are somewhat detailed but earn their place because they clarify the proof-related meaning of result values.

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 tool is simple: one string parameter, no output schema, no annotations. The description explains the output categories (sat/unsat/unknown plus a model) and the grading distinctions, which is sufficient for this complexity level. It references grade_basis for engine version and timeout details, adding useful context.

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

Parameters4/5

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

The input schema provides only a string parameter named smt2 with no description. The tool description compensates by clarifying the parameter must be an SMT-LIB2 formula and supplies a full example showing acceptable syntax, including declare-const, assert, and check-sat.

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 tool checks an SMT-LIB2 formula with Z3 and returns sat/unsat/unknown plus a model. It includes a concrete formula example and is distinct from sibling tools like solve_expression or execute_code.

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 gives clear context: use this tool when you need to check an SMT-LIB2 formula with Z3 specifically. It does not explicitly name alternatives or exclusions, but the specialized scope is evident from the description and tool name.

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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