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Dynamics 365 Finance & Operations MCP Server

by mafzaal

d365fo_search_entities

Search for Dynamics 365 Finance & Operations data entities using keywords to find specific records like customers, orders, or groups by breaking multi-word queries into individual terms.

Instructions

Search for D365 F&O data entities using simple keyword-based search.

IMPORTANT: When a user asks for something like "Get data management entities" or "Find customer group entities", break the request into individual keywords and perform MULTIPLE searches, then analyze all results:

  1. Extract individual keywords from the request (e.g. "data management entities" → "data", "management", "entities")

  2. Perform separate searches for each significant keyword using simple text matching

  3. Combine and analyze results from all searches

  4. Look for entities that match the combination of concepts

SEARCH STRATEGY EXAMPLES:

  • "data management entities" → Search for "data", then "management", then find entities matching both concepts

  • "customer groups" → Search for "customer", then "group", then find intersection

  • "sales orders" → Search for "sales", then "order", then combine results

Use simple keywords, not complex patterns. The search will find entities containing those keywords.

Args: pattern: Simple keyword or text to search for in entity names. Use plain text keywords, not regex patterns. For multi-word requests like 'data management entities': 1) Break into keywords: 'data', 'management' 2) Search for each keyword separately: 'data' then 'management' 3) Run separate searches for each keyword 4) Analyze combined results. Examples: use 'customer' to find customer entities, 'group' to find group entities. entity_category: Filter entities by their functional category (e.g., Master, Transaction). data_service_enabled: Filter entities that are enabled for OData API access (e.g., for querying). data_management_enabled: Filter entities that can be used with the Data Management Framework (DMF). is_read_only: Filter entities based on whether they are read-only or support write operations. limit: Maximum number of matching entities to return. Use smaller values (10-50) for initial exploration, larger values (100-500) for comprehensive searches. profile: Configuration profile to use (optional - uses default profile if not specified)

Returns: Dictionary with matching entities

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patternYes
entity_categoryNo
data_service_enabledNo
data_management_enabledNo
is_read_onlyNo
limitNo
profileNodefault
Behavior4/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes the search behavior (e.g., 'simple text matching', 'combine and analyze results'), usage constraints (e.g., 'Use simple keywords, not complex patterns'), and practical advice (e.g., 'Use smaller values (10-50) for initial exploration'). However, it lacks details on error handling, rate limits, or authentication needs, which are common for API tools, keeping it from a perfect score.

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 well-structured with clear sections (purpose, important strategy, examples, args, returns) and front-loaded key information. However, it is somewhat lengthy due to repetitive examples and detailed parameter explanations, which, while helpful, could be more streamlined. Every sentence earns its place, but minor trimming could improve conciseness.

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?

Given the complexity (7 parameters, no output schema, no annotations), the description is highly complete, covering purpose, usage, parameters, and return format. It addresses the lack of structured data by providing rich context. The only gap is the absence of output schema details, but the description states 'Dictionary with matching entities', which is adequate for a search tool. A perfect score is reserved for cases with output schema or more exhaustive behavioral details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed semantics for all parameters. It explains 'pattern' with examples and a multi-step strategy, clarifies filtering options (e.g., 'entity_category', 'data_service_enabled'), and offers practical advice for 'limit'. This adds significant value beyond the bare schema, ensuring parameters are well-understood.

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 tool's purpose: 'Search for D365 F&O data entities using simple keyword-based search.' It specifies the verb ('search'), resource ('D365 F&O data entities'), and method ('simple keyword-based search'), distinguishing it from sibling tools like 'd365fo_query_entities' or 'd365fo_get_entity_record' which likely have different approaches or purposes.

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?

The description provides explicit guidance on when and how to use this tool, including detailed examples for multi-word requests (e.g., 'data management entities' → search for 'data', then 'management'). It contrasts with alternatives by emphasizing keyword-based search over complex patterns, and the 'IMPORTANT' section outlines a specific strategy for breaking down user queries, making it clear when this tool is appropriate versus other search or query methods.

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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