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ALM X++ MCP Server

by io.github.alimbenhelal-pro

odata_export_entity

Export any D365 F&O data entity via OData (transactional, no DMF project required). Universal: works for ANY public entity. Supports $select, $filter, $orderby and follows server paging automatically. Returns CSV (default) or JSON. Use for live/ad-hoc exports and small-to-medium volumes. For very large bulk exports prefer dmf_export_package. Resolve the entity set name from the KB first (find_entity_for_table / get_data_entity_info) -- do not invent entity names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoOptional OData $filter expression.
formatNoOutput format: 'csv' (default) or 'json'.csv
selectNoOptional $select (comma-separated fields). Empty = all fields.
maxRowsNoMax rows to return (0/empty = no cap, follows all pages). Default 1000.
orderByNoOptional $orderby expression.
entitySetYesOData public entity set name, e.g. 'CustomersV3', 'ReleasedProductsV2'.
outputPathNoOptional file path to also write the full result to (e.g. C:\temp\export.csv).
crossCompanyNoSet true to query across all legal entities (adds cross-company=true).

TDQS

A4.9/5.0
Behavior5/5

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

No annotations are present, so the description carries the full burden, and it does so well. It discloses supported OData operators ($select, $filter, $orderby), automatic server paging, output formats (CSV/JSON), and that the export is transactional and does not require a DMF project. No contradictions with the schema or annotations exist.

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 compact and front-loaded with the core purpose, then packs scope, capabilities, formats, usage guidance, alternatives, and prerequisites into a few dense sentences. There is no filler or repetition, and every sentence earns its place.

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?

For a tool with no output schema and no annotations, the description is complete enough for correct selection and invocation. It covers the use case, volume boundary, alternative, prerequisite lookup step, supported OData features, paging, and return formats. An agent has everything needed to call this tool confidently.

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 the baseline is 3, but the description adds meaningful param context beyond the schema: it confirms $select/$filter/$orderby support, automatic paging (relevant to maxRows), CSV/JSON output (relevant to format), and the requirement to resolve entitySet from the KB. It doesn't deeply document every parameter, but it adds real value beyond the schema.

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 opens with a specific verb and resource: 'Export any D365 F&O data entity via OData'. It further clarifies universal scope ('works for ANY public entity') and distinguishes itself from DMF-based exports. An agent can immediately understand what this tool does and how it differs from siblings.

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 usage guidance is provided: 'Use for live/ad-hoc exports and small-to-medium volumes', with the alternative 'dmf_export_package' named for very large bulk exports. It also instructs the agent to resolve the entity set name from the KB first using find_entity_for_table or get_data_entity_info, preventing invented entity names.

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.

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