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Costory: Your Finops MCP

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Search the authorized Costory workspace for dimensions and saved dashboards, reports, alerts, events, virtual dimensions, and budgets. Use to resolve a resource name or discover dimension fields and values for a query. An empty query with type dimensions returns the dimension catalog. Each dimensions result includes dimension (the exact CEL/groupBy name, e.g. cos_sub_account_id), label, and topMatches. Use type: ["dimensions"] to focus on dimensions only. An empty query (query: "") with type: ["dimensions"] returns every dimension with its top values — use this when you need the full field catalog before building filterCel. Keys that are not imported yet are invisible here — use list_dimension_candidates for those. With a keyword, results are filtered to matching values (e.g. query: "prod" finds production values across dimensions). Use this when a user mentions a product, team, project, or service name and you need to discover where it appears in the cost data before querying. Returns matching dimension values, related events, alerts, dashboards, dashboardTemplates, reports, virtualDimensions, budgets. Virtual dimension hits include id, name, bqName (immutable query field — set at create, never changes), status, and description. Each dashboard result carries a "conditionsCel" string — the dashboard's CEL filter (empty when none) — so before calling update_dashboard you can decide whether to set "extendDashboardConditions: true" on your new widget. Budget results include id (parent budget id for URLs) and name/year; call get with the budget id to obtain the budgetVersionId needed for query. IMPORTANT: Use short, concise search terms — e.g. if the user says 'my kubernetes dashboard', just search for 'kubernetes', not the full phrase. Optional "type" array restricts results to specific entity buckets (dashboards, reports, alerts, budgets, dimensions, virtual_dimensions, events). FOLLOW-UP: After calling search, use get to fetch full details for dashboards, budgets, reports, virtual dimensions, and cost alerts by ID. For dimension values, use "query" to query data grouped by or filtered on the matched dimensions. When the user wants to add to a dashboard, use the id from the dashboards bucket as input to update_dashboard. EXAMPLES: • "List all CEL dimensions" → { query: "", type: ["dimensions"] } • "Find account-related dimensions" → { query: "account", type: ["dimensions"] } • "Show me kubernetes costs" → { query: "kubernetes" } • "Find the data team dashboard" → { query: "data team" }

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": {
      "query": "<query>",
      "context": "<context>"
    },
    "name": "search"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoEnd date for event search (YYYY-MM-DD). Defaults to today.
fromNoStart date for event search (YYYY-MM-DD). Defaults to 90 days ago.
slugNoOrganization slug. Omit to auto-detect from your account (fails if you belong to multiple orgs).
typeNoRestrict results to these entity types. Omit for all.
queryYesSearch term (e.g. 'kubernetes', 'account'). Case-insensitive partial matching. Pass empty string with type: ['dimensions'] to list all CEL field names and top values.
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: -[
      -  "query",
      -  "context",
      -  "llm_model"
      -]New value: +[
      +  "query",
      +  "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: -[
      -  "query"
      -]New value: +[
      +  "query",
      +  "context",
      +  "llm_model"
      +]
  3. First observed

TDQS

A4.4/5.0
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 goes further by disclosing result shapes per bucket (dashboard conditionsCel, budget id/name/year and the budgetVersionId path via get, virtual dimension bqName immutability) — valuable because there is no output schema. It does not mention result limits, pagination, or ordering, which are the main remaining unknowns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded, but the body is long and repeats the empty-query-plus-dimensions behavior twice in near-identical wording, and mixes return-field documentation with routing guidance. The content is useful but would benefit from consolidation; several sentences do not earn their repetition.

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 an eight-parameter, multi-entity search with no output schema, the description covers query semantics, bucket scoping, return fields per entity, and follow-up steps, which is close to complete. It stops short of stating result caps or pagination, which matters for a fan-out search over seven entity types.

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 parameter definitions are already documented; the description nonetheless adds semantics the schema lacks — that the type array focuses buckets, that an empty query scoped to dimensions yields the full field catalog before building filterCel, and the IMPORTANT guidance to use short, concise terms rather than full user phrases. The from/to event-search parameters are never elaborated on in prose, a minor gap.

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 (search) and enumerates the resources it covers (dimensions, dashboards, reports, alerts, events, virtual dimensions, budgets) within the authorized workspace. It also distinguishes itself from siblings by naming list_dimension_candidates for unimported keys and get for full details, so an agent can route correctly without opening 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 when-to-use triggers ('when a user mentions a product, team, project, or service name'), explicit empty-query behavior for the catalog case, an exclusion (keys not imported yet → list_dimension_candidates), and a FOLLOW-UP section routing to get, query, and update_dashboard. Examples map user phrasings to concrete arguments.

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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