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Costory: Your Finops MCP

by io.costory.app-api
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get_dashboard_widget_data

Read-only

Run a saved widget by id and return its cost data — no need to re-specify the query config. Loads the widget's stored request, applies the dashboard's conditionsCel to cost and usage series unless extendDashboardConditions is false on the widget or on that series, and returns the same series/timeSeries or comparison breakdown as the query tool (hidden series with visible: false are omitted from display). Use after get to get data for a specific widgetId. EXAMPLE: "Show me data for the EC2 widget" (after get returned widgetId "wid_123") → { dashboardId: "clx9abc", widgetId: "wid_123" }

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": {
      "dashboardId": "<dashboardId>",
      "widgetId": "<widgetId>",
      "context": "<context>"
    },
    "name": "get_dashboard_widget_data"
  }
}

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 Do not quote the user or include names, emails, or other personal details.
widgetIdYesWidget id (from get or get_context widgets list).
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.
dashboardIdYesDashboardV2 id.
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: -[
-  "dashboardId",
-  "widgetId",
-  "context",
-  "llm_model"
-]New value: +[
+  "dashboardId",
+  "widgetId",
+  "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: -[
-  "dashboardId",
-  "widgetId"
-]New value: +[
+  "dashboardId",
+  "widgetId",
+  "context",
+  "llm_model"
+]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds genuinely non-obvious behavior: it loads the widget's stored request, applies the dashboard's conditionsCel unless extendDashboardConditions is false on the widget or that series, and omits hidden series with visible: false. Pagination or result-size behavior is not mentioned, keeping this from a 5.

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 core action, then the conditions logic, then a usage hint and example — a sensible ordering with no filler sentences. The middle sentence is long and stacks clauses (conditionsCel, extendDashboardConditions, series/timeSeries, visible: false), which slightly hurts scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter read tool with no output schema, the description covers input prerequisites and return shape by reference ("same series/timeSeries or comparison breakdown as the query tool"), which is enough to call it correctly. The analytics-only parameters (context, llm_model, conversation_id) are handled by the schema, so nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema and the baseline of 3 applies. The description reinforces that widgetId comes from `get` and that dashboardId is required, but adds no syntax or format detail beyond what the schema states.

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?

Opens with a specific verb+resource ("Run a saved widget by id and return its cost data") and immediately distinguishes itself from the query tool by noting the stored request means "no need to re-specify the query config." An agent can tell this apart from `get` and `query` without opening any schema.

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?

"Use after get to get data for a specific widgetId" gives an explicit prerequisite and sequencing, and the worked example (EC2 widget after get returned wid_123) makes the workflow concrete. It stops short of stating when to prefer `query` over this tool, but the "no need to re-specify the query config" framing implies the distinction.

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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