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ryanmichaeljames

Dataverse MCP Server

dataverse_retrieve_record_change_history

Read-onlyIdempotent

Retrieve a record's complete change history from Dataverse, showing audit details for attribute, relationship, and share modifications, including user, timestamp, and operation.

Instructions

Retrieve the full audit change history for a specific record.

Calls the unbound RetrieveRecordChangeHistory function, which returns the same AuditDetailCollection container as its column-scoped sibling dataverse_get_attribute_change_history — the entries sit TWO levels down (AuditDetailCollection -> AuditDetails), not one.

AN HTTP 200 IS NOT PROOF OF ANYTHING, AND ON A 404 READ THE ERROR CODE. Live- confirmed on this function, and the earlier note that auditing being off produces an HTTP error was WRONG:

  • auditing disabled at organization/table level returns HTTP 200 carrying the audit-configuration rows described below and ZERO genuine changes — not an error, and no error message to read;

  • MOST tables do NOT validate that the target record exists: a well-formed but NONEXISTENT record id with the CORRECT plural entity set returns HTTP 200 with zero genuine changes (12 of 15 entity sets swept on one org behaved this way, 'accounts' among them). An empty answer is therefore never evidence that the record is there, or that it never changed;

  • a 404 is NOT automatically a naming mistake — the error CODE decides. [0x80060888] "Resource not found for the segment ''" NAMES the bad segment: the entity set is wrong (typically the singular slipped in for the plural) or that table is not provisioned on this org, so fix the name with dataverse_get_entity_sets rather than hunt a deleted row. [0x80048d02] has been seen instead from a CORRECT plural entity set ('audits') and there means what it says — the row really is absent. So some entity sets DO validate the target. One org and 15 entity sets were swept, so treat neither group as a complete list and read the code that actually came back. Use dataverse_get_attribute_change_history when the question is about one column; it additionally diagnoses which level auditing is switched off at.

NOT EVERY ENTRY IS A RESULT. Dataverse MAY add org-level audit-CONFIGURATION rows (records of auditing itself being switched on or off) to a response. They arrive when an audit-configuration change falls inside the TARGET RECORD'S history window, so their presence and count VARY BY TARGET — a record created after the last such change gets none, while older records on the same org got four each, live-measured. audit_configuration_events_count: 0 is a normal, expected answer. They are identified by their SHAPE — no @odata.type, AuditRecord and nothing else, and an all-zero AuditRecord._objectid_value — never by their position, which is not a contract. They are split out into audit_configuration_events (with audit_configuration_events_count) and are NOT counted: audit_details, count and has_more cover this record's own changes only.

ENTRIES ARE POLYMORPHIC — read each one's @odata.type, and detail_types counts the values present on the returned page. A RECORD-scoped call spans everything that happened to the record, so expect a wider mix than a column-scoped one: AttributeAuditDetail (OldValue/NewValue per changed field), RelationshipAuditDetail, ShareAuditDetail (live-confirmed here), RolePrivilegeAuditDetail and UserAccessAuditDetail are all documented subtypes. Every subtype carries an AuditRecord navigation property (who, when, what operation). An entry with an UNRECOGNIZED @odata.type is reported as a change, never quietly dropped, and unclassified_typeless_count reports how many entries carrying NO @odata.type were kept as changes because they did not match the configuration shape — it is 0 on every response observed so far, and a non-zero value means this tool met an entry it could not name rather than that anything was lost.

RESPONSE SHAPE IS CHECKED, NOT ASSUMED. If the AuditDetailCollection container is absent or is not a list of entries, the tool returns normalized: false with the raw body — a missing container is NOT reported as "no changes".

PagingInfo is not sent, so changes are trimmed client-side to top and has_more reports the server's MoreRecords OR anything the trim cut. total_record_count appears ONLY when Dataverse supplied a real count: it is live-confirmed to arrive as -1 here ("not counted"), and a negative value is suppressed rather than passed on as a number that reads like a count.

URL form: GET /api/data/v9.2/RetrieveRecordChangeHistory(Target=@p1) ?@p1={'@odata.id':'()'}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description exceeds annotation disclosures with rich behavioral detail: HTTP 200 can mask disabled auditing or nonexistent records, 404 error codes distinguish naming errors from missing rows, entries are polymorphic, configuration rows may appear, response shape is validated, and paging is trimmed client-side. These are critical operational nuances not conveyed by the annotations alone.

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 quite long, but it is well-organized with bolded section headers (e.g., 'HTTP 200 IS NOT PROOF', 'NOT EVERY ENTRY IS A RESULT', 'ENTRIES ARE POLYMORPHIC') that aid navigation. The front-loaded purpose sentence and each subsequent paragraph add substantive value; the length is justified by the tool's complexity, though a bit more editing could make it leaner.

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?

The description covers error handling, edge cases, polymorphic subtypes, configuration rows, response shape validation, and paging semantics comprehensively. Given the tool's complexity and the presence of an output schema, the description goes far beyond what is required, leaving little room for ambiguity.

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?

Despite the schema including descriptions, the tool description adds essential meaning for each parameter: entity_set_name must be the plural OData collection name and should be discovered via dataverse_get_entity_sets, record_id is a GUID, top controls client-side trimming, and dataverse_url is the required org URL. This compensates effectively for the 0% schema description coverage signal.

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 'Retrieve the full audit change history for a specific record,' a specific verb+resource statement that clearly identifies the tool's function. It also differentiates from the sibling dataverse_get_attribute_change_history by emphasizing record-scoped vs column-scoped history, making the purpose unambiguous.

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 directs the agent to use dataverse_get_attribute_change_history for single-column questions, providing a clear alternative. It also advises using dataverse_get_entity_sets when a 404 indicates a naming issue, and warns against interpreting empty responses as evidence of absence, giving concrete when-to and when-not-to guidance.

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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