MCP server for formal mathematical verification of superintelligence safety proofs using theorem provers
This server has severe definition quality issues. The single tool 'execute_skill_action' has a reasonable name starting with a verb, but the description is vague and lacks actionable context. Input schema is present with two parameters (neural_policy_graph, formal_spec_language), both with descriptions, but the schema lacks type declarations (string types are inferred from examples, not explicit). No output schema is documented anywhere, the client.py shows a mock response structure, but this is not part of the actual MCP server definition. The tool makes extraordinary claims (provably safe superintelligence, 100% loss prevention guarantees) without any error handling, validation, or recovery guidance. No parameter constraints (enums for formal_spec_language), no pagination, no idempotency markers. The implementation is a stub that prints JSON and exits, it does not actually execute any verification logic.
Execute formal mathematical alignment verification and superintelligence safety proof verification
No output schema documented. The tool description does not specify what fields the response contains, their types, or their meaning. Client.py shows a mock response, but this is not part of the MCP server definition and is not visible to agents calling the tool.
Input schema lacks explicit type declarations. Parameters are described as strings in the description text, but the JSON schema does not include 'type': 'string' fields. This forces LLMs to infer types from context.
formal_spec_language parameter should be an enum, not a free-form string. The description mentions 'COQ_LEAN4_THEOREM_PROVER, LEAN_4_FORMAL_PROVER' as examples, but does not constrain the input. LLMs will hallucinate invalid values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 34 | 2026-07-28+ | v2 |
Tool description is vague and makes unsubstantiated claims. 'Execute formal mathematical alignment verification and superintelligence safety proof verification' does not explain WHEN to use this tool, what prerequisites exist, or what the output means. The claims (provably safe superintelligence, 100% loss prevention) are extraordinary and require rigorous error handling and validation, none is present.
No error handling or recovery guidance. The tool has no documented error cases, no validation of inputs, and no guidance for what an LLM should do if verification fails. A tool making safety claims must have robust error reporting.