Skip to main content
Glama

ALM X++ MCP Server

by io.github.alimbenhelal-pro

plan_and_execute

CHAIN-OF-TOOLS PLANNER -- Execute a multi-step JSON plan in ONE call by chaining tools. Pass an EXPLICIT JSON array of steps (NL parsing disabled to prevent ambiguity). Maximum 5 steps per plan. The chain stops on any critical failure (ado_analyze_workitem, get_object_details).

Required Format (JSON array)

[{"tool":"get_object_details","args":{"objectName":"SalesTable"}},{"tool":"validate_best_practices","args":{"objectName":"SalesTable"}}]

When to use

  • You already know the exact sequence of 2-5 tool calls needed

  • The output of step N is independent of step N+1 (or matches the carried context: analysis_output)

  • You want SSE progress events between steps

When NOT to use

  • Single tool call (use the tool directly)

  • Plan depends on the user's intermediate decision (call tools one at a time)

  • More than 5 steps (split into separate calls)

Cost: $0 -- pure in-process execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesNatural language goal OR a JSON array of explicit steps. Examples: 'analyse WI #6587 and create a task', 'check best practices for ALMMyClass'.
projectNoOptional: Azure DevOps project name. Falls back to DEVOPS_PROJECT env var.
assignToNoOptional: assignee email for task creation step, e.g. 'dev@company.com'.
objectNameNoOptional: object name to use when goal references a D365 object (overrides name parsed from goal).
workItemIdNoOptional: work item ID to use when goal references 'WI #N' (overrides ID parsed from goal text).
descriptionNoOptional: additional description text to pass to task creation step.

TDQS

A4.6/5.0
Behavior4/5

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

The description discloses important behavior beyond the sparse annotations: NL parsing is disabled to avoid ambiguity, the plan must be an explicit JSON array, execution stops on critical failures, and execution is pure in-process with no cost. It does not explain final return format or partial-failure behavior for non-critical tools, but the disclosed behavior is substantial.

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 well-structured with headings, a concrete example, and succinct bullet lists. Every section serves a purpose, and the core usage constraint is front-loaded. Redundancy is minimal and acceptable.

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?

For a complex orchestrator tool, the description covers invocation format, constraints, failure behavior, and cost. The main gap is that it does not describe what the aggregated response/result looks like, especially since there is no output schema. Overall it is still sufficiently complete for an agent to select and invoke it correctly.

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 coverage is 100%, so parameters are already documented. The description adds valuable semantics for the key 'goal' parameter by providing a concrete JSON array example and clarifying that it can be either natural language or explicit steps. It also adds context about the carried 'analysis_output' between steps, which the schema alone does not convey.

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 identifies the tool as a chain-of-tools planner/executor that runs a multi-step JSON plan in one call. This distinguishes it from the sibling tools, which are individual domain operations rather than an orchestrator.

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?

Explicit 'When to use' and 'When NOT to use' sections state exactly when this tool is appropriate: 2-5 independent steps, known sequence, SSE progress desired. It also tells the agent to use single tools directly for one-off calls or when user decisions are needed, providing clear routing guidance.

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

All ALM X++ MCP Server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources