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by com.krusgraph
OverviewDiscussions(1)Admin

Create or update the GraphQL API (dry run by default)

create_backend
DestructiveIdempotent

Creates a Fabric API for GraphQL item over the source's tables with the plan's keys and relationships, or updates the API of that name in place. Requires workspace, source and name. Returns JSON {summary, dry_run, result, card (endpoint, portal steps left), skipped_relationships}; the dry run returns instead whether the item exists and would be updated or created, the definition and what changes in it, and the checks that passed. Changes Fabric only with dry_run=false: an update replaces the API's definition; tables outside tables or gone from the source leave it, field names of unchanged relationships are kept, and no item is deleted. When an update would drop relationships the API has, nothing is written and the answer (kind would_remove) lists them as confirmed entries. It does not run validate_plan; plan_backend proposes, validate_plan checks, plan_drift previews an update. Errors: {error, kind, hint}.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "workspace": "<workspace>",
      "source": "<source>",
      "name": "<name>"
    },
    "name": "create_backend"
  }
}

Input Schema

NameRequiredDescriptionDefault
keysNoPrimary keys replacing the inferred ones. Default: inferred. Example: {"order_items": ["order_id", "line_no"]}.
nameYesDisplay name of the API for GraphQL item; an existing item with this name is updated in place. Example: "shop_api".
schemaNoSchema in the source. Default "dbo".dbo
sourceYesLakehouse, warehouse or SQL database, name or id.
tablesNoExact table names the API exposes; the plan is still made from the whole schema. Default: all tables. Example: ["orders", "customers"].
dry_runNotrue (default) returns what would be created and the REST calls, changing nothing; false creates or updates.
writableNotrue includes create, update and delete mutations in the API; false (default) gives a read-only API. Example: true.
confirmedNoRelationships the user confirmed, added to the plan. Default none. Example: [{"source": "orders", "source_cols": ["ship_to_id"], "target": "addresses", "target_cols": ["address_id"]}].
workspaceYesFabric workspace, name or id.
allow_removalsNotrue lets an update drop relationships the existing API has; false (default) writes nothing and lists them instead. Example: true.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "create_backendOutput",
-  "type": "object"
-}New value: +null
  2. Changed1 schema field changed
    • addedInput schema / properties / dry_run
      Added value: +{
+  "default": true,
+  "description": "true (default) returns what would be created and the REST calls, changing nothing; false creates or updates.",
+  "title": "Dry Run",
+  "type": "boolean"
+}
  3. Changed9 schema fields changed
    • addedInput schema / properties / allow_removals / description
      Added value: +"true lets an update drop relationships the existing API has; false (default) writes nothing and lists them instead. Example: true."
    • addedInput schema / properties / confirmed / description
      Added value: +"Relationships the user confirmed, added to the plan. Default none. Example: [{\"source\": \"orders\", \"source_cols\": [\"ship_to_id\"], \"target\": \"addresses\", \"target_cols\": [\"address_id\"]}]."
    • addedInput schema / properties / keys / description
      Added value: +"Primary keys replacing the inferred ones. Default: inferred. Example: {\"order_items\": [\"order_id\", \"line_no\"]}."
    • addedInput schema / properties / name / description
      Added value: +"Display name of the API for GraphQL item; an existing item with this name is updated in place. Example: \"shop_api\"."
    • addedInput schema / properties / schema / description
      Added value: +"Schema in the source. Default \"dbo\"."
    • addedInput schema / properties / source / description
      Added value: +"Lakehouse, warehouse or SQL database, name or id."
    • addedInput schema / properties / tables / description
      Added value: +"Exact table names the API exposes; the plan is still made from the whole schema. Default: all tables. Example: [\"orders\", \"customers\"]."
    • addedInput schema / properties / workspace / description
      Added value: +"Fabric workspace, name or id."
    • addedInput schema / properties / writable / description
      Added value: +"true includes create, update and delete mutations in the API; false (default) gives a read-only API. Example: true."
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool as destructive and not read-only, and the description adds concrete behavior: dry runs change nothing, updates replace the API definition, tables outside the requested set leave the API, unchanged relationship field names are kept, and no item is deleted. It also explains the allow_removals guard and the would_remove confirmation path, which directly clarifies how destructive actions are constrained.

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 front-loaded with the core operation and efficient given the tool's complexity, but it uses long semicolon-separated sentences and repeats some schema-default information. It is dense rather than bloated, with no major wasted sentences.

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 complex ten-parameter mutation-like tool with no output schema, the description covers return shape, dry-run behavior, update semantics, deletion/removal safeguards, error shape, and sibling-tool boundaries. An agent has enough information to invoke it correctly and understand the consequences of dry_run and allow_removals.

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 schema already documents defaults and examples for all parameters. The description adds behavioral meaning beyond the schema, especially for `tables` (tables outside the list leave the API) and `confirmed` (would_remove entries are listed as confirmed), though some details simply reinforce what the schema already says.

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?

States a specific verb and resource: creates a Fabric API for GraphQL item over source tables, or updates the API of that name in place. It clearly distinguishes the create and update modes and names related sibling tools later in the description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context: requires workspace, source and name, uses dry_run=true by default, and only changes Fabric with dry_run=false. It also distinguishes plan_backend, validate_plan, and plan_drift, though it does not explicitly state prerequisites such as running validate_plan before applying.

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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