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opa-mcp-server

Partially evaluate a Rego query

rego_compile_query

Perform partial evaluation on a Rego query to substitute known values and return the residual policy. Use for offline policy slicing or pre-computing decision sets.

Instructions

Run partial evaluation on a query — substitute known values and return the residual policy. Defaults unknowns to ["input"] (treat input as unknown), so the residual encodes "given input X, this is what would have to be true." Use this for offline policy slicing or pre-computing decision sets.

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 are provided, so the description must convey behavioral traits. It explains the default `unknowns` and the concept of residual policy, but does not disclose potential side effects, mutability, or safety aspects. The description is adequate but lacks explicit behavioral depth.

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?

The description consists of two concise sentences. The first sentence states the core purpose with essential details; the second adds context and use-case. No extraneous information is present.

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 provides a high-level understanding but lacks details on return format or handling of other parameters. Given the complexity (8 parameters, no output schema), the description is somewhat brief but sufficient for an agent to grasp the tool's primary function.

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?

Schema description coverage is 100%, establishing a baseline of 3. The description adds value by explaining the default for `unknowns` and contextualizing the partial evaluation process, enhancing meaning beyond the schema definitions.

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 'Run partial evaluation', the resource 'a Rego query', and the outcome 'the residual policy'. It distinguishes this tool from siblings by specifying it's for partial evaluation, which is a unique operation among the listed tools.

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 explains when to use it ('for offline policy slicing or pre-computing decision sets') and describes default behavior for `unknowns`. However, it does not explicitly contrast with alternatives like `rego_eval` or `rego_check`, leaving some ambiguity for the agent.

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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