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ryanmichaeljames

Dataverse MCP Server

dataverse_get_total_record_counts

Read-onlyIdempotent

Get approximate row counts for many Dataverse tables in one round trip.

Instructions

Get approximate row counts for many Dataverse tables in one round trip.

Calls the unbound RetrieveTotalRecordCount function with up to 50 table logical names (singular and lowercase — 'account', not 'accounts') and returns a {logical_name: count} map.

The counts come from a snapshot Dataverse takes at most once every 24 hours, so they are approximate and can lag reality by up to a day: a table populated an hour ago may report 0, and recent deletions may still be included. Worse, on an environment where the snapshot job has not run, EVERY count comes back 0 while the tables actually hold data (observed live on an org whose real counts were in the hundreds). The response flags that case with all_counts_zero=true — read it as "unknown", not "empty". Use this tool for cheap bulk sizing (which tables hold data, rough magnitudes, migration planning), and dataverse_count_records whenever an exact, live, or filtered count matters.

Unknown names are all-or-nothing, NOT silently dropped: a single logical name Dataverse does not recognize fails the whole call with HTTP 400 ([0x80040203] "Entity X was not found in the CRM system") and no partial results come back. Pass names you have already confirmed exist — dataverse_list_tables is the cheap way to confirm them. The error message names the offending table so you can drop it and retry.

If the response is not in the expected shape it is returned unchanged under raw_response with normalized=false rather than being guessed at.

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?

Beyond the annotations (readOnlyHint, openWorldHint, etc.), the description reveals critical behavioral nuances: counts are stale by up to 24 hours, all_counts_zero=true means 'unknown' not 'empty', unrecognized names fail the entire call with HTTP 400, and raw_response fallback for unexpected shapes. This is far beyond what annotations convey.

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 front-loaded with the core purpose, then logically organized into paragraphs covering approximation caveats, all-zero edge case, failure behavior, and response fallback. Each sentence contributes specific, non-redundant information, making the length appropriate for the tool's complexity.

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?

Given the tool complexity, existing output schema, and annotations, the description leaves no meaningful gaps: it covers freshness, false-zero indication, error semantics, name validation, and response normalization. It is fully self-sufficient for an agent to select and invoke correctly.

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 context signal of 0% schema description coverage, the description thoroughly explains the entity_names parameter: singular and lowercase logical names, not plural OData set names; the 1-50 array limit and URL length reasoning; name grammar requirements; and that any unrecognized name aborts the call. It also notes splitting for long names, fully compensating for any schema gaps.

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-resource-scope statement: 'Get approximate row counts for many Dataverse tables in one round trip.' It clearly distinguishes itself from sibling dataverse_count_records by emphasizing the bulk, approximate nature and the map return format.

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 guidance is provided: 'Use this tool for cheap bulk sizing... and dataverse_count_records whenever an exact, live, or filtered count matters.' It also recommends dataverse_list_tables for confirming entity names before invoking this tool, giving both positive and alternative usage contexts.

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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