Solves boolean satisfiability problems with two-literal clauses by building an implication graph and checking strongly connected components.
Solve a 2-SAT boolean satisfiability problem using implication graph and SCC.
| Name | Required | Description | Default |
|---|---|---|---|
| clauses | Yes | Clauses as [a, b] pairs where positive = true, negative = negated | |
| variable_count | Yes | Number of boolean variables (1-based) |
Changes observed during successful MCP inspections.
v0.1.11Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It doesn't say whether the result is a satisfying assignment or just a satisfiability verdict, what happens on unsatisfiable input, or how malformed clauses (e.g. wrong arity) are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler. Every word contributes to identifying the operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter computational tool with full schema coverage this is minimally sufficient, but with no output schema the return value (assignment vs. boolean) is undocumented anywhere, which is a real gap for chaining.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already explains the clause encoding and 1-based variable counting. The description adds nothing about parameters, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Solve a 2-SAT boolean satisfiability problem') and even names the solving technique (implication graph + SCC). It is unambiguous and no sibling competes with it in the flat tool list, but it stops short of any sibling differentiation or scope statements.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to reach for this tool, what inputs are invalid, or how to interpret results. An agent gets a definition of the problem but nothing about choosing or applying it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.