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Costory: Your Finops MCP

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Read-only

Unified search across your entire Costory workspace — dimension values, events, alerts, dashboards (with their conditionsCel), dashboard templates, reports, virtual dimensions, and budgets. PRIMARY tool for discovering CEL field names: 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" }

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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 read-only/safe, but the description adds substantial behavior beyond them: non-imported keys are invisible in results, keyword queries filter to matching values, each dashboard carries a conditionsCel string usable to decide extendDashboardConditions, virtual-dimension hits expose bqName as immutable, and budget results expose only id/name/year, requiring get for budgetVersionId.

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-loads the core purpose and then organizes guidance into scoping, filtering, return shapes, and follow-ups. It is long (~300 words) and the entity list is restated once with camelCase result keys, but that restatement doubles as return-shape documentation for a tool with no output schema, so most sentences earn their place.

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 5-param, no-output-schema tool spanning seven entity types, the description covers return shapes per bucket, special-case fields (conditionsCel, bqName, budgetVersionId), and the full follow-up chain. An agent has everything needed to call it and act on results.

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 baseline is 3, but the description adds real semantics: query+"type: [\"dimensions\"]" returns the full field catalog, empty query semantics, and the IMPORTANT guidance to use short terms rather than full phrases. It does not mention from/to/slug, so it stops short of a 5.

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 ("Unified search across your entire Costory workspace") and enumerates every entity bucket it spans. It clearly distinguishes itself from siblings by explicitly routing non-imported keys to list_dimension_candidates and full-detail fetches to get.

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?

Explicit when-to-use ("PRIMARY tool for discovering CEL field names", "when a user mentions a product, team, project, or service name"), named alternatives (list_dimension_candidates), and step-by-step follow-up routing (get for dashboards/budgets/reports, query for dimension values, update_dashboard with the dashboards id). Nothing is left to inference.

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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