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Costory: Your Finops MCP

by io.costory.app-api
OverviewDiscussionsAdmin

publish_virtual_dimension

Destructive

Validate the latest persisted pending virtual dimension draft, then promote it to published and kick off a BigQuery refresh. virtualDimensionId in inputs equals id from list/get/search. Use when the user asks to publish a validated virtual-dimension draft. Requires a pending draft row — call update_virtual_dimension_draft for an existing virtualDimensionId (or create_virtual_dimension_draft for a brand-new VDIM) before publish. Preview alone does not create a draft. Rejects invalid drafts with draftValidation errors (no publish). On success returns virtualDimensionId, immutable bqName, name, computeStatus, and published rules — does not wait for the refresh job. After publish, query using returned bqName for groupBy/filterCel in query — bqName is immutable (set at create from the initial name) and does not change when name is renamed. computeStatus is REFRESHING when the refresh job was queued (async — poll via get or list_virtual_dimensions until COMPLETED before querying), or TO_REFRESH when the draft was promoted but queuing the refresh job failed — do not query yet; retry publish or re-check computeStatus until COMPLETED. Deletes the pending draft version. Clerk MCP only — not available on the service route. Demo orgs are read-only unless the Clerk caller is an org admin on that tenant. EXAMPLE: "Publish the Environment VDIM draft" → { virtualDimensionId: "<virtualDimensionId from create/list>" }

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": {
      "virtualDimensionId": "<virtualDimensionId>",
      "context": "<context>"
    },
    "name": "publish_virtual_dimension"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugNoOrganization slug. Omit to auto-detect from your account (fails if you belong to multiple orgs).
contextYesA few words on why you are calling this tool, for the server developer's analytics. Describe people and things by role, such as "a customer". Do not quote the user or include names, emails, or other personal details.
llm_modelNoThe model identifier you are running as, for example "claude-opus-4-8" or "gpt-5.2". Pass "unknown" if you are not certain. Recorded for the server developer's analytics.
conversation_idNoThe conversation_id this server returned earlier in this conversation, unchanged, including after later user messages. Omit it if you have none. Recorded for the server developer's analytics.
virtualDimensionIdYesVirtual dimension id (stable public identifier)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / context / description
      Previous value: -"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""New value: +"A few words on why you are calling this tool, for the server developer's analytics. Describe people and things by role, such as \"a customer\". Do not quote the user or include names, emails, or other personal details."
    • changedInput schema / required
      Previous value: -[
-  "virtualDimensionId",
-  "context",
-  "llm_model"
-]New value: +[
+  "virtualDimensionId",
+  "context"
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "The conversation_id this server returned earlier in this conversation, unchanged, including after later user messages. Omit it if you have none. Recorded for the server developer's analytics.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The model identifier you are running as, for example \"claude-opus-4-8\" or \"gpt-5.2\". Pass \"unknown\" if you are not certain. Recorded for the server developer's analytics.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "virtualDimensionId"
-]New value: +[
+  "virtualDimensionId",
+  "context",
+  "llm_model"
+]
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false; the description corroborates by disclosing that the pending draft version is deleted and that invalid drafts are rejected with draftValidation errors and no publish. It goes well beyond annotations by describing async refresh semantics and the two distinct computeStatus outcomes (REFRESHING vs TO_REFRESH) plus the remediation for each. No contradiction with the destructiveHint annotation.

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 core action is front-loaded in the first clause, and the example pinpoints the one meaningful input. The text is dense but every sentence carries operational content (prerequisites, error mode, async status handling, bqName usage); it could be broken into shorter paragraphs for scanability but contains little true filler.

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 destructive, async mutation with no output schema, the definition covers prerequisites, what gets destroyed, failure behavior, the returned fields, the polling requirement, and the immutable-bqName gotcha. An agent has everything needed to call it correctly and act on the result.

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 coverage is 100%, so the baseline is 3; the description still adds meaning by stating that `virtualDimensionId` equals `id` from list/get/search and must reference a pending draft row. The bqName immutability note is about returned values rather than inputs, so it does not further enrich parameter meaning.

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 precise verb chain (validate → promote to published → kick off BigQuery refresh) against a specific resource (the latest persisted pending virtual dimension draft). It explicitly distinguishes itself from the sibling preview tool ("Preview alone does not create a draft") and from create/update draft, so an agent can route correctly without opening other schemas.

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?

Gives explicit preconditions (a pending draft row must exist, created via update_virtual_dimension_draft or create_virtual_dimension_draft) and names the specific sibling for each prerequisite case. It also states post-call guidance — query with the returned bqName and poll until computeStatus is COMPLETED — and flags the Clerk-only / demo read-only constraints.

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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