Skip to main content
Glama

odata_upsert_rows

DestructiveIdempotent

Idempotent import of rows into any entity via OData: PATCH when the record exists (matched by keyFields), otherwise POST. Safe to re-run -- duplicates are updated, not re-created. Best for small-to-medium transactional loads (e.g. <= a few thousand rows). For bulk loads use dmf_import_file. Provide rows as a JSON array of objects; resolve key fields from the KB (get_data_entity_info) -- do not guess them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsJsonYesJSON array of row objects, e.g. [{"CustomerAccount":"C0001","Name":"Acme"}].
entitySetYesOData public entity set name, e.g. 'CustomersV3'.
keyFieldsYesComma-separated business key fields used to detect existing records, e.g. 'CustomerAccount' or 'dataAreaId,ItemNumber'.
legalEntityNoOptional legal entity (dataAreaId) injected into each row when absent.
crossCompanyNoSet true to allow cross-company writes.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare idempotentHint and destructiveHint, but the description adds meaningful behavior: PATCH/POST mechanism, duplicate handling ('duplicates are updated, not re-created'), and re-run safety. It does not fully describe response/error behavior, but it enriches the annotation profile without contradicting it.

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, starting with the core verb and purpose. The sentences each carry distinct value: operation semantics, idempotency/re-run safety, usage boundary with alternative, and invocation guidance. No words are wasted.

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 5-parameter tool with no output schema, the description covers purpose, operation behavior, scale guidance, and the alternative tool. It omits return/response format and error-prevention details beyond the key-fields warning, so it is nearly complete but slightly short of fully self-sufficient.

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 description coverage is 100%, so the baseline is 3. The description adds value by explaining how to supply rows ('JSON array of objects') and by warning the agent to resolve key fields from the knowledge base rather than guessing, which goes beyond the schema's basic field descriptions.

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 states a specific verb and resource: 'Idempotent import of rows into any entity via OData'. It further clarifies the exact operation semantics (PATCH when the record exists, otherwise POST), which unambiguously distinguishes it from read/export tools like odata_export_entity.

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 explicitly states when the tool is appropriate: 'small-to-medium transactional loads' and gives a size example. It also names the bulk-load alternative, dmf_import_file, and tells the agent to resolve key fields from get_data_entity_info rather than guessing them.

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

Try in Browser

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.