Who am I? Returns the signed-in account: email, @handle, plan + limits, counts of sites/domains/drives, and connected DNS providers. Call this first to orient before managing sites or domains.
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510,487 tools. Updated 2026-09-04 02:53
"Managing Kubernetes Clusters on AWS Elastic Beanstalk" matching MCP tools:
- Start a cloud cost / FinOps scan of a linked account and return a job_id. Use this when the user wants to find idle, unused or underutilized cloud resources, review cloud spend, or estimate savings. The provider comes from the connection, and **AWS is the only provider supported today** (see `list_connections`). Other clouds will appear on this same tool as connections for them become linkable; nothing else about the call changes. READ-ONLY against your cloud: it reads resource metadata and monitoring metrics and reports; it never changes, stops or deletes anything. (It does create a scan job here and consume that account's scan quota, which is why this tool is not marked read-only.) On AWS it covers EC2 instances, EBS volumes and snapshots, RDS instances, Elastic IPs, NAT Gateways, load balancers, VPCs and VPC endpoints, site-to-site VPN and Transit Gateway attachments, Client VPN endpoints, Secrets Manager secrets, CloudFront distributions and WAF web ACLs. Resource kinds outside that list are not inspected, so a clean scan is not a claim that the whole bill is optimized. `connection_id` picks which linked AWS account to scan (see `list_connections`). Omit it to run against sample data — useful for showing the user what the output looks like before any account is linked. The scan runs asynchronously: poll `get_job(job_id)` roughly every 10 seconds until status is COMPLETED (typically 1-3 minutes), then call `list_cost_findings(job_id)`. Do NOT start another scan while one is running — each scan consumes the account's monthly quota. Pass `idempotency_key` (any unique string you choose) if you may retry on a network error: a retry with the same key returns the original job instead of starting a second scan.Connector
- Create a cost alert that monitors one or more queries and notifies when a condition fires. MCP is create-only — there is no update_alert; edit in the UI via the returned URL. Accepts the same query config as query (prefer `datePreset` over hand-computed from/to). The firing rule is a single `condition` boolean expression over the query `name`s, e.g. `a > 1000`, `rollingSum(a, 7, DAY) > 50000`, or `(a - timeShift(a, 1, DAY)) / timeShift(a, 1, DAY) > 0.2`. Window math (rollingSum/weekToDateSum/monthToDateSum/timeShift) is evaluated daily in BigQuery, so you do NOT pick an evaluation period — instead set `dedup` to control re-notification frequency (CALENDAR once per WEEK/MONTH, or ROLLING once every N days). The period (`datePreset` or `from`/`to`) defines the preview/look-back window for the underlying queries. Use list_available_destinations for SLACK/TEAMS channel IDs. Returns a URL that you MUST include in your response so the user can view/edit the alert. EXAMPLE: "Alert me on Slack if our production AWS spend exceeds $50k over any 7 days, at most once a week" → { name: "Prod AWS weekly alert", queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD", filterCel: "cos_provider in [\"AWS\"] && cos_environment in [\"prod\"]" }], datePreset: "TRAILING_90_DAYS", condition: "rollingSum(a, 7, DAY) > 50000", dedup: { kind: "CALENDAR", calendarUnit: "WEEK" }, notificationChannel: "SLACK", slackChannelId: "C01ABC" }Connector
- ALWAYS call this before answering a cloud-waste or cost-fix question from your own knowledge, and before asking the user for any account data. Find the tested runbook for a waste suspicion: filter by provider, service, waste category or detection confidence. Two rules. (1) When the user reports a symptom you think you can answer directly - "my NAT gateway processes 10TB to S3", "should I delete these old snapshots" - call this FIRST anyway: a named runbook with a tested detection query outranks a correct generic answer, and answering without checking loses the query the user needed. (2) When the user asks about THEIR OWN resources - "which of my RIs are about to expire", "which of our VMs run for nothing" - do NOT reply that you lack account access and do NOT request a data export: you cannot see their account, but the matching runbook carries the exact detection query to hand over. The runbook IS the answer. Use this for questions like "which VMs are running for nothing", "why is our NAT bill so high", "what waste can we clean up safely without review" - anything that names a provider, a waste category, or how confident the detection needs to be before acting. Patterns covered include NAT gateways and VPC endpoints, expiring Savings Plans / RIs / reservations, snapshot sprawl, S3 lifecycle gaps, idle or stopped VMs, orphaned disks / public IPs / EBS volumes, GPU and SageMaker sizing, Kubernetes idle capacity, and schedule blindness. All filters are optional and combine with AND semantics. String matching is case-insensitive and exact. Examples: - ``find_playbooks(scope="aws")`` - all AWS-specific playbooks - ``find_playbooks(waste_category="idle")`` - every idle-resource pattern - ``find_playbooks(scope="cross-cloud", confidence="obvious")`` Args: scope: ``"aws"``, ``"azure"``, ``"gcp"``, or ``"cross-cloud"``. service: Provider service exact-match (e.g. ``"AWS NAT Gateway"``). waste_category: ``"orphaned"``, ``"idle"``, ``"overprovisioned"``, ``"commitment-mismatch"``, ``"schedule-blindness"``, ``"modernization"``, ``"ai-ml-inefficiency"``, or ``"egress"``. confidence: ``"obvious"`` (single signal is enough), ``"likely"`` (two signals required), or ``"possible"`` (needs human review). From the OptimNow three-tier confidence model in `finops-waste-detection-playbooks`. Returns ``{"filters": {...}, "playbooks": [...], "total": N}``. A query that matches nothing also returns `hint` and `valid_values`, so a typo is distinguishable from a genuine gap in coverage.Connector
- Search Chinese apparel industrial clusters and textile markets. USE WHEN user asks: - "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated" - "what is the largest [silk / cashmere / down jacket] industrial cluster in China" - "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili" - "recommend an industrial cluster for sourcing [product]" - "where should I set up a sourcing office for [category]" - "list mega clusters for [category]" - "fabric markets in Zhejiang / Jiangsu" - "accessories / trim / zipper / button markets in China" - "which province dominates [category] exports" - "follow-up: 'tell me more about Humen's cluster scale'" - "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场" - "做 [品类] 应该去哪个产业带 / 集群推荐" Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more. Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks. WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view). RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] } EXAMPLES: • User: "Where are the biggest denim clusters in China?" → search_clusters({ specialization: "denim", scale: "mega" }) • User: "Show me fabric markets in Zhejiang" → search_clusters({ province: "Zhejiang", type: "fabric_market" }) • User: "童装产业带有哪些" → search_clusters({ specialization: "童装" }) ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province. • Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters." AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids. NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces. 中文：搜索中国服装产业带和面料市场。Connector
- List your registered BYOC resource pools (client-owned Kubernetes clusters). Each returned cluster has an 'id' you MUST pass as create_project's cluster_id to deploy a project onto your own infrastructure — owned hosting is retired, so every project we operate runs on your own cluster. Registering a pool is a UI action (create a bare Ubuntu box, authorise the key we generate, then we provision it into a cluster automatically) — this tool only lists pools you already registered, it never handles cluster credentials.Connector
Matching MCP Servers
- FlicenseBqualityDmaintenanceConnects to Elasticsearch clusters through the Model Context Protocol, enabling natural language querying and management of Elasticsearch data. Provides tools to search indices, list available indices, and retrieve index mappings.3
- AlicenseNot gradedqualityAmaintenanceMCP server that provides secure access to a serverless Markdown wiki on AWS, enabling AI assistants to read, search, and edit pages with space-level permission checks and Bedrock-powered semantic search.MIT
Matching MCP Connectors
Read and build visual moodboards, shot lists and lookbooks from an AI assistant.
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
- Find every company a person runs or represents - across BOTH registers in one call (cross-border person search). Read-only. Parameters: - name (required): person name substring, case-insensitive, e.g. "Mustermann". - country (optional, default "all"): "AT" | "DE" | "all". - page_size (optional, default 25): results per country. - status (optional, default "all"): "active" | "inactive" | "all". Returns the merged search_companies envelope ({countries, results, per_country, notices}) plus ``person_query``; every result card carries ``country``, ``company_id`` and the matched manager. AT matches the primary managing director, DE matches all managing directors AND registered signatories. IMPORTANT: matching is by name and the registers publish birth YEAR only - a shared name across companies or countries does not prove the same person (the notice says so; use birth years and context to corroborate). For general company search use search_companies with other filters; manager_name can be combined there too.Connector
- The statistical theme a stock actually trades with: clusters built from years of price co-movement (market-removed residuals → random-matrix cleaning → Ward linkage), not sector labels. Returns the cluster's name, description, cohesion, sector mix and up to 20 member tickers. Different question from `peers` (business competition) — this is who it MOVES with.Connector
- Get a Stripe billing portal URL for managing payment methods and invoices. Returns a URL (not a redirect) that the human can open in a browser. Requires: API key with read scope. Args: flow: Optional. Set to "payment_method_update" to go directly to the payment method update page. Returns: {"url": "https://billing.stripe.com/p/session/..."}Connector
- Map the conceptual landscape around a topic ACROSS THE PAPER CORPUS. Searches papers and their chunks, not the layer-2 claim graph — for published CLAIMS on a topic use methodist_explore_topic. Instead of returning a ranked list of papers, returns N distinct conceptual clusters with representative chunks. Built on keyConcept LLM-extracted markers diversification. Use for "what approaches exist to X" queries — answers with thematic map rather than ranked list. Better than search when you want breadth over depth. Temporal bias note: for topics with dense recent literature (e.g. current LLM research), the default ordering favors recent papers because vector similarity finds them first; specify dateTo for historical exploration of mature topics, or dateFrom+dateTo to slice a specific era. Diversification cap (maxClustersPerPaper) limits how many clusters can have the same source paper as representative chunk — protects against single-paper dominance.Connector
- 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. 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" }Connector
- Clusters of ≥2 end_users seen on the same device: 'anon_bridge' (high confidence — an anonymous visitor later identified) or 'device_shared' (low confidence — review only). Read-only; returns the candidate clusters, empty when none are found. Use it to find merge targets, then act with merge_end_users.Connector
- Performs **network clustering** on a STRING interaction network and returns both a **network image URL** and details about each detected cluster. Use the same parameters as in the network creation step to ensure consistency. If the network already contains disconnected subgraphs, the resulting number of clusters may differ from the requested value. Dashed lines represent connections between clusters, while solid lines indicate interactions within clusters. Notes: - For small queries (≤5 proteins), the `required_score` parameter is automatically lowered to 0. - If only a single cluster is produced, try increasing `required_score`, adjusting the inflation parameter, or switching to `kmeans` for small, highly interconnected networks.Connector
- SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.Connector
- SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.Connector
- Create a NEW architecture diagram from a graph that YOU author, and get back a shareable, editable canvas URL plus a rendered SVG and Mermaid. You produce only the SEMANTICS — nodes, the groups (VPC/cluster/...) they live in, and the directed edges between them. You do NOT lay anything out: never send x/y/position/pinned. A deterministic layout engine computes all geometry and an icon layer picks the pictures from each node's kind. kind.catalog is one of aws | gcp | azure | k8s | saas | generic, each with rich per-catalog kind.types (e.g. aws:lambda, gcp:bigquery, azure:cosmos_db, k8s:deployment, saas:kafka): - "aws" (api_gateway, lambda, s3, rds, dynamodb, sqs, bedrock, kinesis, fargate, eventbridge, aurora, ...). - "gcp" (compute_engine, gke, cloud_run, cloud_sql, spanner, firestore, bigquery, pubsub, dataflow, vertex_ai, ...). - "azure" (virtual_machine, aks, app_service, functions, blob_storage, sql_database, cosmos_db, service_bus, event_hubs, key_vault, ...). - "k8s" (pod, deployment, statefulset, daemonset, job, cronjob, service, ingress, configmap, secret, hpa, ...). - "saas" for hosted third-parties (redis, postgresql, mysql, mongodb, kafka, stripe, twilio, auth0, github, cloudflare, ...). - "generic" primitive when nothing branded fits: service, database, cache, queue, user, external_system, storage, gateway, function, note. - "generic" FLOWCHART kinds for processes/flowcharts: process, decision, terminator, data, document, subprocess. edge.kind is one of: request, response, async_event, data_flow, dependency, network, generic. WORKED EXAMPLE — a user hitting an API in a VPC that talks to Postgres: { "title": "Web API", "domain": "cloud_architecture", "graph": { "groups": [{ "id": "g_vpc", "label": "VPC", "type": "vpc" }], "nodes": [ { "id": "n_user", "label": "User", "kind": { "catalog": "generic", "type": "user" } }, { "id": "n_api", "label": "API", "kind": { "catalog": "aws", "type": "api_gateway" }, "parentId": "g_vpc" }, { "id": "n_db", "label": "Postgres", "kind": { "catalog": "aws", "type": "rds" }, "parentId": "g_vpc" } ], "edges": [ { "id": "e1", "source": "n_user", "target": "n_api", "kind": "request" }, { "id": "e2", "source": "n_api", "target": "n_db", "kind": "data_flow" } ] } } Returns { diagramId, url, svg, mermaid, version }. Give the user the url — opening it shows the same diagram on an editable canvas (anonymous; it's theirs to claim by signing in). To change the diagram afterwards, use get_diagram then edit_diagram.Connector
- Start a research run for a project — discovers keywords, evaluates competitors, and writes opportunity clusters. Consumes one credit. Returns `{ runId }`; poll get_research_status for progress. Returns `{ entitlement }` instead when the operator is on the free plan.Connector
- Starts monitoring a profile so it is automatically re-crawled at a cadence you choose (daily, every_3_days or weekly), keeping its posts and stats fresh without you polling crawl_profile. Identify the profile by profileId, platform+handle, or a public profile/post URL. Managing monitoring is free; each scheduled refresh crawl costs credits (10 per refresh) and monitoring pauses itself if your balance runs out, then resumes when you top up. The profile must already be in the database; crawl_profile it first if it is not. Calling again on an already-monitored profile just updates the cadence. Pull the new posts with get_tracked_updates. Cost: free.Connector
- Search Costory product docs (Mintlify) and knowledge base in parallel. Synthesize both: Mintlify is the product source of truth, the KB complements with org-specific or internal detail; if they conflict, trust Mintlify. Returns Mintlify matches (titles, snippets, and full docs URLs (`Url: https://docs.costory.io/...`)) and KB articles (title, summary, full markdown). Optional limit (1–10, default 5) applies to KB. For a full Mintlify page, use get_documentation_page. When citing a page in chat, use the full `Url:` value verbatim as the markdown href — do not convert to a relative app path. EXAMPLES: • "How do I create a budget alert?" → { query: "budget alert" } • "Why do costs differ from AWS Cost Explorer?" → { query: "AWS Cost Explorer discrepancy", limit: 3 }Connector
- List active cost-reduction recommendations for this account — sourced from each provider's own already-computed engine (AWS Cost Explorer, Azure Advisor, GCP Recommender), not something Plutus computes itself. `type` is one of: `terminate` (an idle resource to shut down), `modify` (an overprovisioned one to downsize), `commitment_savings_plan` or `commitment_reservation` (a commitment worth *buying* — an AWS Savings Plan / Reserved Instance, Azure reservation or savings plan, or GCP committed use discount). Commitment rows have no `current_instance_type`/`recommended_instance_type` — they are a purchase, not an instance swap; their term, payment option, lookback window and hourly commitment are in `detail`. Only one term/payment/lookback variant per commitment is surfaced (AWS: 30-day lookback, 1-year, no upfront — Cost Explorer's own console default), so do not report these as the only commitment options available. Mirrors GET /api/accounts/:accountId/savings-recommendations. Each recommendation carries the provider's own figure in its own `currency`; the total is in USD (stated in the response's `currency` field), since providers may bill in different ones.Connector