ALM X++ MCP Server

trace_role_license_tree

Read-onlyIdempotent

WHEN: security design, licence audit, or 'what licence does this role require?'. Triggers: 'arborescence du rôle', 'licence nécessaire pour', 'what licence for role', 'role tree', 'droits du rôle', 'entry points of role', 'privilege tree for'. Builds the COMPLETE tree for ONE role: Role -> Duties -> Privileges -> Entry Points. For each entry point, classifies the required D365 licence per the March 2026 Licensing Guide: Team Members (~~$8/user/mo, read-only + named tasks), Operations-Activity (~~$50, warehouse mobile & production floor), Finance (~~$180), Supply Chain Mgmt (~~$180), Human Resources (~~$22), Project Operations (~~$120), Commerce (~$180). Grant-level aware: NoAccess/Read/View -> Team Members; Activity writes -> Operations-Activity; transactional writes -> full product licence based on functional area. Confidence: High (known module prefix) . Medium (keyword) . Low (fallback). Ends with a Optimization section: Team Members / Activity eligibility, role-split opportunities, per-user/month cost estimates (March 2026 MSRP). Always validate against the Microsoft D365 Licensing Guide. For a full multi-role scan, call trace_role_license_tree multiple times -- once per role. NOT for the pure technical duty/privilege/entry-point chain without licence inference -- use trace_security_chain for that.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`roleName`	Yes	The exact security role name, e.g. 'SystemAdministrator', 'AccountsPayablePaymentsClerk', 'ALMMyCustomRole'
`maxEntryPoints`	No	Maximum entry points to show per privilege (default: 20, max: 100)

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already mark the tool as read-only and idempotent. The description adds substantial behavior beyond that: grant-level license classification, per-tier pricing, confidence levels, and an optimization section. No contradiction with annotations.

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 is long but densely structured: it front-loads usage triggers, then flows into tree construction, license classification, confidence scoring, and optimization. Every section contributes distinct operational or routing information.

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

Completeness5/5

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

Even without an output schema, the description explains what the result will contain, how licenses are assigned, how confidence is determined, and what follow-up action to take. The essential invocation behavior and output expectations are thoroughly covered.

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 description coverage is 100%, so the baseline applies. roleName and maxEntryPoints are already fully documented in the schema, and the description does not add meaningful parameter-specific detail beyond saying it handles one role at a time.

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 the specific action: builds the complete Role -> Duties -> Privileges -> Entry Points tree and infers the required D365 license. It explicitly distinguishes itself from trace_security_chain, which handles the chain without license inference.

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

Usage Guidelines5/5

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

Opens with WHEN scenarios and trigger phrases, names the alternative for non-license chain analysis, and instructs multi-role scans to call this tool once per role. This gives the agent both inclusion and exclusion criteria.

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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TDQS

A4.1/5.0

Disambiguation4/5

Most tools have distinct purposes and clear triggers, reducing ambiguity. For example, PR-related tools are separated into analysis, listing, commenting, and dependency mapping. However, some overlap exists between find_references, find_extensions, and find_callers, which could confuse an agent without careful descriptions.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern with verb_noun structure within subgroups (e.g., ado_*, find_*, search_*, generate_*). There is no mixing of camelCase or other styles, though the variety of prefixes slightly reduces predictability.

Tool Count3/5

With 38 tools, the server feels slightly over-scoped for its domain. While each tool has a specific function, the number is high compared to typical well-scoped servers (10-15 tools). Some tools like find_references and find_callers could be consolidated.

Completeness4/5

The tool set covers a broad range of D365 F&O development and DevOps tasks, including code search, analysis, security, performance, upgrades, and work item management. Minor gaps exist, such as the absence of direct object modification or batch job management, but the core workflows are well covered.