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Costory: Your Finops MCP

by io.costory.app-api
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list_label_dimension_usages

Read-only

List stored objects that reference a label dimension column (dashboards, reports, alerts, events, virtual dimensions, budgets) before rename or delete. Use when the user asks where a label dimension is used before renaming or deleting it. Pass id (from list_label_dimensions) and/or name; id wins when both are set. Native cos_* names work with name alone. Hits are JSON/CEL scans of groupBy, filterCel, conditionsCel, and conditionCel — not live BigQuery. Empty usages is success (safe to delete from a reference point of view). Capped at 100 hits (truncated: true when more). EXAMPLE: "Where is environment used?" → { id: "label_…" }

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": {
      "context": "<context>"
    },
    "name": "list_label_dimension_usages"
  }
}

Input Schema

NameRequiredDescriptionDefault
idNoEngineered label id from list_label_dimensions.
nameNoColumn name (LabelDimension.name). At least one of id / name is required; id wins when both are set.
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 Do not quote the user or include names, emails, or other personal details.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.

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: -[
-  "context",
-  "llm_model"
-]New value: +[
+  "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"
+}
    • addedInput schema / required
      Added value: +[
+  "context",
+  "llm_model"
+]
  3. Added

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare a safe read-only, non-destructive, closed-world operation, yet the description adds genuinely new behavior: hits come from static JSON/CEL scans of groupBy, filterCel, conditionsCel and conditionCel rather than live BigQuery, empty results mean success, and output is capped at 100 with a truncated flag. That is well beyond what the annotations convey, though pagination or how to retrieve beyond the cap is not addressed.

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?

Front-loaded with the purpose and the pre-rename/pre-delete condition, followed by invocation details and edge-case semantics. It is dense but nearly every clause carries information; the example line is a small extra but illustrates the argument shape usefully.

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?

With no output schema, the description takes on responsibility for return semantics and does so: it names the usages result, defines empty as success, and discloses the 100-hit truncation flag. Combined with the source-of-scan caveat, an agent has enough to call it and interpret results correctly.

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% and the schema already states that id wins when both are set, so that part is partly redundant. The description still adds value by noting that native cos_* names work with name alone, which the schema does not say, and by anchoring id to list_label_dimensions.

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 ('List stored objects that reference a label dimension column') and enumerates the object types scanned (dashboards, reports, alerts, events, virtual dimensions, budgets). This distinguishes it clearly from siblings like list_label_dimensions, which enumerates dimensions rather than their usages.

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 when-to-use framing ('before rename or delete', 'Use when the user asks where a label dimension is used') and names the upstream tool to source the id. It also handles the id-vs-name selection rule, leaving no ambiguity about how to invoke it.

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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