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opa-mcp-server

Evaluate Rego with execution trace

rego_eval_with_explain

Evaluate Rego policies with a full explanation trace to see why each rule fired or not, enabling precise debugging of policy decisions.

Instructions

Evaluate with --explain=full and return a structured trace alongside the result. Use this when an agent needs to see why a rule fired (or didn't) — the trace is the basis for rego_explain_decision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesRego query to evaluate, e.g. "data.example.allow".
sourceNoInline Rego policy source. Mutually exclusive with `paths`.
pathsNoPolicy / data file or directory paths. Each must be inside an allowed root.
inputNoInline input document.
inputPathNoPath to a JSON input file. Mutually exclusive with `input`.
unknownsNoRefs to treat as unknown during partial evaluation.
partialNoRun partial evaluation rather than full evaluation.
strictBuiltinErrorsNoTreat builtin errors as fatal instead of returning undefined.
Behavior3/5

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

No annotations provided. Description indicates it runs evaluation with full explain and returns trace alongside result. Lacks details on mutation, auth, or output structure beyond 'structured trace'.

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, immediate verb ('Evaluate'), front-loaded with key info. No superfluous content.

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?

Adequately conveys purpose and trace output. No output schema, but description hints at return value. For a tool with many parameters (8) and siblings (31), it gives sufficient context for selection and invocation.

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 covers 100% of parameters with descriptions. Description adds minimal extra meaning (mentions --explain=full but that's implicit from tool name). No further parameter guidance.

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?

Clearly states it evaluates with --explain=full and returns structured trace. Distinguishes itself from sibling eval tools by specifying it's for understanding why rules fired/didn't, and notes it's the basis for rego_explain_decision.

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?

Explicitly says when to use (when agent needs to see why rule fired or didn't). References related tool rego_explain_decision. However, it doesn't mention when not to use or compare to other eval variants like rego_eval.

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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