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Ontology CLI

MicrosoftOfficialPublished by Microsoft

  • Skill
  • Tool integration
  • Data and AI
  • MIT
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From the author

Manages Microsoft Fabric Ontology items: authors legacy V1 entity/relationship definitions and answers questions for new-experience (V2) Ontologies through ask_ontology. Use for Ontology item operations or grounded questions; V2 authoring/upgrades and V1 queries are unsupported. Power BI data questions use fabriciq, report authoring powerbi-report-cli, and model/DAX changes semantic-model-authoring.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The ontology-cli skill provides a specialized interface for interacting with Microsoft Fabric Ontology items. It operates in two distinct, non-convertible modes: an authoring mode for managing legacy V1 entity and relationship definitions, and a consumption mode for querying new-experience (V2) Ontologies. The skill acts as a mode dispatcher, requiring users to read specific reference documentation before executing commands to ensure correct payload generation and state management. It does not support V1-to-V2 upgrades or V2 authoring.

What it does

  • Creates and updates legacy V1 Ontology entity and relationship definitions.
  • Answers natural-language questions about existing new-experience (V2) Ontologies.
  • Generates grounded code or data insights from V2 Ontologies via the Ontology Agent.
  • Manages data bindings for V1 Ontology entity types.
  • Provides a structured workflow for resolving workspace and item identifiers.

When to use it

  • When you need to define or modify the structure of a legacy V1 Ontology.
  • When you need to query data or metadata from a new-experience (V2) Ontology.
  • When you need to generate runnable code based on V2 Ontology rules.
  • When you are working within the Microsoft Fabric ecosystem and require specific Ontology item operations.

Advantages

  • Ensures grounded, validated responses by using the Ontology Agent for V2 queries.
  • Provides a clear separation between legacy authoring and modern consumption workflows.
  • Reduces errors by enforcing mandatory reference reading before command execution.
  • Maintains consistency in Fabric item management through standardized CLI patterns.

Usage examples

“Using the ClinicOperations ontology, how many clinics are there?”
The agent enters consumption mode, verifies the ontology, and queries the data to return the clinic count.
“Create a legacy V1 ontology item with Customer and Order entity types bound to the lakehouse.”
The agent enters authoring mode, prepares the required JSON definition tree, and executes the creation call.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/ontology-cli/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip ontology-cli.zip -d .agents/skills

Documentation by Microsoft

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: ontology-cli (az rest: --headers "x-ms-fabric-skill=ontology-cli"), including every LRO poll, fabric_lro and retry. Snippets omit it — add it anyway.

CRITICAL NOTES

  1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering

  2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering

  3. Skill disambiguation: use ontology-cli for operations on an Ontology item and questions grounded in that Ontology. Natural-language data questions over existing Power BI reports/models are fabriciq; report creation, editing, publishing, or rebinding is powerbi-report-cli; semantic-model or DAX authoring is semantic-model-authoring. Naming an Ontology as a report's data source does not move report authoring into this skill.

  4. Consumption runs through the Ontology Agent MCP server, never around it. Do not decode an ontology definition to hand-compose KQL / T-SQL / Spark SQL, and do not delegate ontology data questions to eventhouse-cli / sqldw-cli / spark-cli. That path loses the agent's grounding and validation and is not metered.

  5. Natural-language consumption supports only new-experience (V2) Ontologies. Call tools/list first. If ask_ontology is absent, stop and tell the user that V1 natural-language queries are unsupported and the Ontology must be upgraded or recreated in the new experience outside this skill's authoring flow. Never fall back to search_ontology.

  6. Authoring is legacy V1-only. The JSON EntityTypes/.../definition.json item-definition flow below cannot create, convert, or upgrade a new-experience (V2) Ontology. Do not route a V2 upgrade/recreation request into authoring.

  7. Kusto bindings use the parent Eventhouse item ID, never the KQL database ID. Validate the staged binding against discovered source metadata before presenting an authoring preview; unresolved or mismatched source identities block the write.

Ontology CLI Skill

This skill owns two explicit, non-convertible surfaces: legacy V1 JSON-definition authoring, and read-only questions over existing new-experience (V2) Ontologies through the Ontology Agent MCP endpoint. It does not upgrade V1 items to V2 or author V2 items.

It is a mode dispatcher and contains NO procedures. Pick the mode that matches the request from the table below, then read the matching references/<mode>.md file end to end with your file-reading tool BEFORE issuing a single command. That file holds the endpoints, payload shapes, templates and gotchas; acting without it produces wrong payloads and wrong results.

Mode selection

ModeUse when the request ...Example triggersRead this first
authoringcreates or updates a legacy V1 Ontology's JSON entity/relationship definitions or data bindings; V2 creation/upgrade is out of scopecreate legacy ontology item, bind V1 entity type, add V1 relationship type, update legacy ontology definitionreferences/authoring.md
consumptionasks a question about an existing new-experience (V2) Ontology -- its data, entities, or rules -- or wants grounded runnable code generated from ithow many clinics are there, which treatment rooms are busiest, what entities exist, list the ontology rules, generate KQL from this ontologyreferences/consumption.md

Consumption sub-references (read directly, not through chained links):

Mode boundary rule

consumption is read-only. Any definition write goes through the authoring mode's preview-and-confirm flow -- never call updateDefinition from a consumption request.

If a request genuinely spans modes, handle them one at a time and read each reference before you start that part. If the mode is ambiguous after reading this table, ask one short clarifying question instead of guessing.

Terminal write -- the step you must not skip

Reading the reference and planning the change is NOT completing the task. Each mutating mode ends with one state-changing call. If you did not issue it, nothing was persisted -- say so explicitly rather than reporting success.

ModeTerminal write
authoringcreateItem (POST /v1/workspaces/{ws}/items) to create a legacy V1 Ontology, then POST .../updateDefinition to persist entity types, relationships and bindings. This cannot create or upgrade V2.
consumptionnone -- this mode is read-only

Before you report the task done, confirm the terminal call returned success and, where the reference documents a readback, read the artefact back to prove the change landed.

Shared essentials (all modes)

Resolve the workspace and item first; every mode depends on it.

TaskReferenceNotes
Finding Workspaces and Items in FabricCOMMON-CLI.mdMandatory -- read before resolving any workspace or item id
Catalog SearchCOMMON-CORE.mdRequest/response shape, Catalog.Read.All scope, 24 h indexing delay
Pagination PatternCOMMON-CLI.mdCatalog Search and item listings both page via continuationToken
Fabric Topology & Key ConceptsCOMMON-CORE.mdItem types, workspaces, capacities
Environment URLsCOMMON-CORE.mdSovereign / non-public cloud hosts
Authentication & Token AcquisitionCOMMON-CORE.mdWrong audience = 401; read before any auth issue
Authentication RecipesCOMMON-CLI.mdaz login flows and token acquisition
Core Control-Plane REST APIsCOMMON-CORE.mdPagination, LRO polling, rate limiting
Gotchas & TroubleshootingCOMMON-CLI.mdaz rest audience, shell escaping, token expiry

Rules

MUST
  • Select exactly one mode from the table above before doing anything else.

  • Read references/<mode>.md end to end, as your FIRST tool call, before the first command of that mode. Read it ONCE, in a single full read: do not re-open it, do not grep it again, and do not page through it. You already have it.

  • Apply the same read-once discipline to every other file you load, including nested references/<mode>/*.md sub-references and shared common/*.md files: open only the ones you actually need, read each ONCE end to end, and never grep or re-open one you already loaded.

  • Resolve workspace and item ids by listing and filtering, never by guessing a GUID.

  • In consumption, call tools/list before the first tools/call. Continue only when ask_ontology is present; otherwise report that V1 natural-language consumption is unsupported and stop without calling another ontology tool.

  • Announce a mode switch explicitly when the request crosses a boundary.

  • Treat the reference as instructions, never as the deliverable. After reading it, RUN the documented commands against the live workspace and report the real results. Quoting what the reference says instead of executing it does not answer the request.

PREFER
  • The narrowest mode that satisfies the request.

  • Reading exactly ONE mode reference. Load a second only when the request genuinely spans modes, and say so before you do.

  • Reporting the mode you chose in your first response so the user can correct you.

AVOID
  • Acting from this dispatcher alone -- it intentionally omits the operational detail.

  • Answering with a summary of the reference instead of executing it.

  • Re-reading or re-grepping a reference you already loaded; it costs turns and tokens.

  • Mutating anything while in a read-only mode.

  • Answering an ontology data question with a hand-composed source query instead of the Ontology Agent MCP (see CRITICAL NOTE 4).

  • Loading a different skill for work this family already owns (see CRITICAL NOTES 3).

Examples

User requestModeReference to read
"Using the ClinicOperations ontology, how many clinics are there?"consumptionreferences/consumption.md
"Create a legacy V1 ontology item with Customer and Order entity types bound to the lakehouse."authoringreferences/authoring.md
"Upgrade this V1 ontology to the new V2 experience."out of scopeExplain that V2 upgrade/recreation is unavailable through this skill's authoring flow
"What entity types exist in the Sales ontology and how are they bound?"consumptionreferences/consumption.md
"Generate Eventhouse-runnable KQL for appointments per practitioner from this ontology."consumptionreferences/consumption.md

Files

references/consumption/examples.md

<!-- Sub-reference for the `ontology-cli` skill, `consumption` mode. Indexed directly from `skills/ontology-cli/SKILL.md`. -->

ontology-cli consumption — Verified calls and troubleshooting

Verified Ontology Agent MCP calls and troubleshooting. Selection rules live in the consumption mode reference indexed from SKILL.md.

Contents

SectionCovers
Call templateThe shape every example below reuses
Verbatim JSON repliesA machine-checked reply draft that preserves opaque identifiers
0. Resolve the IDsWorkspace-scoped listing or Catalog Search
1. Discover the surfacetools/list and reading the result
2. Ask a business questionThe core ask_ontology call
3. Ask for runnable codequeryLanguage plus execution target
4. An unsupported V1 ontologyStop before tools/call and request upgrade
5. Inspect the schemalist_ontology_entities, filtered
TroubleshootingVerified symptoms and fixes

Call template

WS and ONT are resolved workspace/item GUIDs, never guesses. Resolve within the named workspace; use Catalog Search only when none was supplied (see 0. Resolve the IDs). APIHOST is https://api.fabric.microsoft.com. Use a body file: inline questions break shell quoting.

az rest --method post \
  --url "$APIHOST/v1/mcp/dataPlane/workspaces/$WS/items/$ONT/ontologyEndpoint" \
  --resource "https://api.fabric.microsoft.com" \
  --headers "Content-Type=application/json" "Accept=application/json, text/event-stream" \
            "x-ms-fabric-skill=ontology-cli" \
  --body "@/tmp/payload.json" 1>ont_resp.json 2>ont_err.txt
cat ont_resp.json

The --resource audience is https://api.fabric.microsoft.com. Allow at least 120 seconds per ask_ontology call.

PowerShell: prepare an absolute UTF-8-without-BOM payload and validate it locally before POST, following consumption/request-files.md, indexed in SKILL.md. Pass the same quoted "@$payloadPath" to the CLI; recreate variables in fresh shells.

Do not call the variable HOST. In PowerShell $host is a reserved automatic variable, the assignment fails, and the call has to be re-issued -- which bills the agent twice.

Capturing the answer without paying twice

Each tools/call is billed. Save the complete first response with 1> and read it in the same command. Never call once for --query result.isError and again for content: Keep $payloadPath pointing to the actual request you prepared. In a fresh shell, recreate that request's absolute path; do not replace it with a discovery filename.

# correct -- the answer is on disk before anything tries to display it
$null = Get-Content -LiteralPath $payloadPath -Raw -ErrorAction Stop |
  ConvertFrom-Json -ErrorAction Stop
az rest --method post --url "$APIHOST/v1/mcp/dataPlane/workspaces/$WS/items/$ONT/ontologyEndpoint" `
  --resource "https://api.fabric.microsoft.com" `
  --headers "Content-Type=application/json" "Accept=application/json, text/event-stream" `
            "x-ms-fabric-skill=ontology-cli" `
  --body "@$payloadPath" 1>ont_resp.json 2>ont_err.txt
function ConvertFrom-McpResponseText([string]$text) {
  try {
    $json = $text | ConvertFrom-Json -ErrorAction Stop
    if ($null -ne $json.id -and ($json.PSObject.Properties['result'] -or $json.PSObject.Properties['error'])) {
      return $json
    }
  } catch {}
  $events = [regex]::Split($text.Trim(), '\r?\n\r?\n')
  for ($i = $events.Count - 1; $i -ge 0; $i--) {
    $data = @(($events[$i] -split '\r?\n') |
      Where-Object { $_ -match '^data:' } |
      ForEach-Object { $_ -replace '^data:\s?', '' }) -join "`n"
    if ($data -and $data -ne '[DONE]') {
      try {
        $json = $data | ConvertFrom-Json -ErrorAction Stop
        if ($null -ne $json.id -and ($json.PSObject.Properties['result'] -or $json.PSObject.Properties['error'])) {
          return $json
        }
      } catch {}
    }
  }
  throw 'Ontology MCP response contained neither JSON nor a valid SSE data frame.'
}
$j = ConvertFrom-McpResponseText (Get-Content ont_resp.json -Raw)
if ($j.PSObject.Properties['error']) {
  throw ("Ontology MCP JSON-RPC error: " + ($j.error | ConvertTo-Json -Depth 30 -Compress))
}
"isError=" + $j.result.isError
$j.result.content | Where-Object type -eq 'text' | ForEach-Object text

Do not capture stdout into $resp, guard on it being empty, then write it: a wrapper can lose stdout after a successful slow call. Redirect to disk first. Recover display or projections from that file; never re-send the body.

For PowerShell-native HTTP, use Invoke-WebRequest so either JSON or SSE remains raw text; do not redirect an Invoke-RestMethod object through the table formatter:

$headers.Accept = 'application/json, text/event-stream'
$response = Invoke-WebRequest -Method Post -Uri $uri -Headers $headers -Body $body
$response.Content | Set-Content response.txt
$j = ConvertFrom-McpResponseText $response.Content
Verbatim JSON replies

For an explicit raw-schema request, do not reconstruct JSON or shorten identifiers while composing the answer. In the same PowerShell invocation that parses the saved response into $j, construct and check the exact reply draft:

function Get-VerifiedOntologyReply([string]$serviceText, [string]$draftPath) {
  if ([string]::IsNullOrWhiteSpace($serviceText)) { throw 'Saved Ontology response has no text to deliver.' }
  $draft = Get-Content -LiteralPath $draftPath -Raw -Encoding UTF8 -ErrorAction Stop
  if (-not $draft.Contains($serviceText)) {
    throw 'Reply draft changed the service text. Rebuild it from the saved response and recheck.'
  }
  return $draft
}
$serviceText = @($j.result.content | Where-Object type -eq 'text' | ForEach-Object text) -join "`n"
$draftPath = Join-Path ([IO.Path]::GetTempPath()) ('ontology-answer-' + [guid]::NewGuid().ToString('N') + '.md')
$fence = '```'
$draft = "isError=$($j.result.isError)`n`n$fence" + "json`n$serviceText`n$fence"
try {
  [IO.File]::WriteAllText($draftPath, $draft, [Text.UTF8Encoding]::new($false))
  Get-VerifiedOntologyReply -serviceText $serviceText -draftPath $draftPath
} finally {
  if (Test-Path -LiteralPath $draftPath) { Remove-Item -LiteralPath $draftPath -ErrorAction Stop }
}

Use that checked draft unchanged for the final chat response. Do not retype its JSON or identifiers after checking. Recheck any edited draft; if it fails, recover the text from the saved response, never by issuing another tools/call. In a new PowerShell invocation, redefine the helpers and reparse the saved response first. Do not silently switch to file-only delivery when the user requested the content in chat.


0. Resolve the IDs

Choose the path that preserves the user's scope:

  • Workspace supplied: resolve that workspace, then list its Ontology items with GET {host}/v1/workspaces/{WS}/items?type=Ontology and exact-filter the name or ID locally. Do not broaden to tenant-wide Catalog Search.

  • No workspace supplied: use Catalog Search. Every hit carries hierarchy.workspace.id beside id, so one result provides both halves of the MCP URL. This path requires the Catalog.Read.All delegated scope.

Inside a supplied workspace — exact-filter the item locally:

az rest --method get \
  --url "$APIHOST/v1/workspaces/$WS/items?type=Ontology" \
  --resource "https://api.fabric.microsoft.com" \
  --headers "x-ms-fabric-skill=ontology-cli" \
  --query "value[?displayName=='ClinicNetwork'].{name:displayName,id:id}" -o table

With no workspace supplied, by name — the user said "the ClinicNetwork ontology":

cat > /tmp/find.json << 'EOF'
{"search": "ClinicNetwork", "filter": "Type eq 'Ontology'", "pageSize": 30}
EOF
az rest --method post --url "$APIHOST/v1/catalog/search" \
  --resource "https://api.fabric.microsoft.com" \
  --headers "x-ms-fabric-skill=ontology-cli" \
  --body @/tmp/find.json \
  --query "value[].{name:displayName, id:id, ws:hierarchy.workspace.id}" -o table

By bare GUID — drop search entirely and match id yourself. Do not pass the GUID as search; verified on a live tenant, that returns the derived <name>_lh_<guid> and <name>_graph_<guid> artefacts plus unrelated noise, and not the ontology:

cat > /tmp/all.json << 'EOF'
{"filter": "Type eq 'Ontology'", "pageSize": 100}
EOF
az rest --method post --url "$APIHOST/v1/catalog/search" \
  --resource "https://api.fabric.microsoft.com" \
  --headers "x-ms-fabric-skill=ontology-cli" \
  --body @/tmp/all.json \
  --query "value[?id=='$ONT'].hierarchy.workspace.id | [0]" -o tsv

If the result is empty and continuationToken is non-empty, repeat with that token before concluding the ontology is absent — but cap the walk at three pages and then ask for the workspace instead.

Verified search behaviour

search matches case-insensitive word prefixes across name and description — it is neither an exact match nor typo-tolerant. The behaviour below was measured on a live tenant; the names are illustrative, standing in for two ontologies called ClinicNetwork and ClinicOperations:

QueryHitsWhy it matters
Clinic, clinic, CLINIC2case-insensitive
Clin, Cli2a prefix is enough — the user need not type the full name
nic0prefix, not substring
Operati4camel-case names are tokenised
Clinnic0no fuzzy matching; a typo finds nothing
Operations6matched half the tenant, several only via description
radiology1description-only match

So more than one hit is the normal case. Show displayName, workspace and id, and let the user choose; picking the first silently will eventually answer from the wrong ontology.


1. Discover the surface

cat > /tmp/payload.json <<'JSON'
{"jsonrpc":"2.0","id":1,"method":"tools/list"}
JSON

Add --query "result.tools[].name" to the template to see just the names:

["search_ontology", "ask_ontology", "list_ontology_rules", "list_ontology_entities"]

ask_ontology is present, so this is a supported new-experience (V2) Ontology and the rest of the session may use it. If ask_ontology is absent, stop before tools/call.


2. Ask a business question

User: "Using the ClinicOperations ontology, how many clinics are there?"

cat > /tmp/payload.json <<'JSON'
{"jsonrpc":"2.0","id":2,"method":"tools/call",
 "params":{"name":"ask_ontology","arguments":{"request":"How many clinics are there?"}}}
JSON

Response:

{ "jsonrpc": "2.0", "id": 2,
  "result": {
    "_meta": { "conversationId": "8b319d38-6472-4bd8-8351-80959687c0d3" },
    "content": [ { "type": "text", "text": "There are **5 clinics**." } ],
    "isError": false } }

Answer to the user: There are 5 clinics in the ClinicOperations ontology.

Note what is not here: no definition fetch, no base64 decode, no hand-written SQL, no delegation to a sibling skill. One discovery call and one question.


3. Ask for runnable code

queryLanguage accepts KQL, SQL, DAX, Python, the ontology DSL, or Ontology/Eventhouse-runnable KQL that joins KQL, SQL and DAX sources. Name it and the execution target inside request:

{"jsonrpc":"2.0","id":3,"method":"tools/call",
 "params":{"name":"ask_ontology","arguments":{"request":
   "Generate runnable code that returns the number of appointments per practitioner. queryLanguage: KQL. Execution target: Eventhouse."}}}

The returned text is KQL grounded in the ontology's real bindings — a concrete connection string and real table and column names, not placeholders. Present it to the user; hand execution to eventhouse-cli only if they ask for it. This call is slower than a plain question.


4. An unsupported V1 ontology

When tools/list omits ask_ontology — for example, it returns only ["list_ontology_entity_types", "search_ontology"] — stop before tools/call. Do not use search_ontology as a fallback.

Tell the user:

Natural-language queries in ontology-cli support only new-experience (V2) Ontologies. This Ontology uses the earlier V1 experience, so I did not run the query. Upgrade or recreate it in the new Ontology experience, then try again.


5. Inspect the schema

Only when the user actually asked a schema question. Filter whenever you can — the unfiltered form returned roughly 44,000 characters for a 13-entity ontology.

{"jsonrpc":"2.0","id":5,"method":"tools/call",
 "params":{"name":"list_ontology_entities","arguments":{"entityName":"Appointment"}}}

result.content[0].text parses to:

{ "values": [ { "name": "Appointment", "lineageTag": "...", "keyProperties": [ ... ],
                "backingTable": { ... }, "additionalBackingTables": [ ... ],
                "properties": [ ... ], "synonyms": [ ... ], "annotations": [ ... ],
                "resourceLinks": [ ... ] } ] }

6. A follow-up question

Every tools/call is independent -- the server keeps no conversation, so a follow-up that relies on the previous turn will be answered without that context. Fold the carried-over facts into the new request:

The user saysSend
"Of those 5, which is busiest?""Among the 5 clinics in this ontology, which one records the most appointments?"
"Now break that down by month""Break the 47 cancelled appointments in this ontology down by month."

Keep result._meta.conversationId in your notes; it identifies the call in a support ticket even though it cannot be replayed.


Version gating, as observed

tools/list against two ontologies in the same workspace returns different sets:

V2 ontology -> search_ontology, ask_ontology, list_ontology_rules, list_ontology_entities
V1 ontology -> list_ontology_entity_types, search_ontology

Only the first surface is supported for natural-language consumption. The V1 list is shown solely so the client can recognize and reject it; do not call search_ontology.

Calling a gated tool anyway returns HTTP 200 carrying:

{ "result": { "content": [{ "type": "text",
  "text": "Tool 'ask_ontology' is not supported for this ontology artifact version." }],
  "isError": true } }

Phrasing the request

request is the only input, so the whole question has to live in that one string.

User saidWeak requestStrong request
"how many clinics"clinicsHow many clinics are there?
"cancellations last month"cancellationsHow many appointments were cancelled during the previous calendar month, broken down by clinic, highest first?
"busiest rooms"roomsWhich treatment rooms handled the most appointments? Return room, site and appointment count.
"departments per site"departments by siteFor each site, return the site ID, the site name and the number of departments.

The last row matters more than it looks: site names repeat, so a request that groups by name merges distinct sites into one row. Ask for the identifier whenever the question says "for each".


Troubleshooting

SymptomCauseFix
Catalog Search by GUID returns <name>_lh_<guid> / <name>_graph_<guid> but not the ontologyThe GUID was passed as search, which matches names, not IDsDrop search, filter on Type eq 'Ontology', and compare id locally
Catalog Search returns nothing for an ontology you know existsWrong tenant or identity, wrong host, missing access, or (rarely) the 24 h indexing lagCheck az account show against the tenant that owns the item before scanning wider; a bigger scan cannot fix an identity or environment mismatch
The bare-GUID scan keeps paging and never matchesfilter-only walks every ontology in the tenant; large tenants run to hundredsStop after ~3 pages per host and ask for the workspace ID. Do not switch to workspace enumeration — it is more expensive, not less
Catalog Search returns many unrelated ontologiessearch also matches the description, so common words are broadNarrow the term, or show the hits and ask the user which one
result.isError: true, text Tool 'ask_ontology' is not supported for this ontology artifact version.The ontology is V1 (feature level 1)Stop; explain that only new-experience (V2) Ontologies support natural-language consumption and request upgrade/recreation
tools/list omits ask_ontologyThe item is V1 or otherwise not on the supported new experienceStop before tools/call; do not fall back to search_ontology
HTTP 404 with The entity could not be foundThe route resolved but the workspace or ontology ID is wrong for this host, or you lack accessRe-resolve both IDs within the original requested scope on the same host; check item access
HTTP 404 with The requested resource could not be foundA legacy route alias was used, or the host does not match the environmentUse /v1/mcp/dataPlane/workspaces/{ws}/items/{id}/ontologyEndpoint, and confirm the host matches the environment the IDs came from
HTTP 401Not signed in, or wrong token audienceaz login; confirm --resource "https://api.fabric.microsoft.com"
Service-principal token failsOntology MCP currently does not support service principalsSign in interactively with az login; do not retry the service-principal token
HTTP 403The signed-in identity lacks read access to the ontology itemAsk for a role assignment on the item or the workspace
JSON-RPC error instead of resultBad tool name, missing required argument, or a request outside 3-10000 charactersRe-read tools/list for the exact name and schema, then fix the argument
Shell reports an unterminated string, or the body arrives mangledInline --body with a quoted questionWrite the payload to a file and use --body @file.json
The call hangs, then the client gives upNormal agent latency exceeded the client timeoutRaise the timeout to 120 s or more. Do not retry blindly — narrow the question instead
The answer says an entity or property was not foundThe wording did not resolve against the ontology modelCall the schema tool once, show the user the real entity names, and re-ask with corrected vocabulary
A schema tool response looks like an opaque stringThe JSON payload is nested inside content[0].textParse content[0].text as JSON, then read values[]
The answer is vague or averages across everythingThe request omitted scope, filters or a time rangeRe-ask with one focused question that names the entity, the filter and the period

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills-for-fabric · skills/ontology-cli · commit f8fac11
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
microsoft/skills-for-fabric

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download ontology-cli.zip