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Extract access control

extract_access_control

Extract roles, permissions, guards, policies, and path-level access rules from a codebase to generate a role–feature access matrix and surface missing or inconsistent checks.

Instructions

Roles, permissions, guards/policies, path-level rules, backend and frontend access checks, and the role × feature access matrix.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootYesAbsolute path of the project to analyse (a mounted path such as /workspace when running in Docker).
formatNoOutput format (default markdown).
refreshNoRe-scan the project instead of using the cached analysis.
include_checksNoInclude the raw list of inline checks (can be long).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only lists output content and does not state whether the tool scans the codebase, whether it is read-only, how caching/refresh works, or what side effects may occur. This is a significant transparency gap for an analysis tool with four parameters.

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 a single dense list with no filler words, and every listed term is relevant to access control. It is efficient and front-loaded with the most concrete facets, but it is a fragment rather than a complete sentence, which limits its informational value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, four parameters, and a sibling set of extraction/analysis tools, the description is incomplete. It covers what content is produced but omits the action performed, when to use it, behavioral traits, and how it differs from siblings, so an agent lacks critical context for correct 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?

The input schema has 100% description coverage, documenting root, format, refresh, and include_checks with clear descriptions and an enum. The tool description adds little parameter-level meaning, though its mention of 'access checks' loosely aligns with the schema's 'inline checks' parameter. With high schema coverage, the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description enumerates concrete access-control artifacts—roles, permissions, guards/policies, path-level rules, frontend/backend checks, and the role × feature matrix—making the tool's subject unmistakable and distinct from sibling extraction tools. However, it is a noun phrase rather than a sentence with a verb, so the action relies on the tool name 'extract_access_control' rather than being stated in the description itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives such as extract_data_model or extract_endpoints. No explicit conditions, prerequisites, or exclusion criteria are provided, so an agent must infer the intended use case solely from the tool name and content list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.