"Managing Kubernetes Clusters on AWS Elastic Beanstalk" matching MCP tools:

• analyze_market

  MRC Data — China's Apparel Supply Chain Infrastructure

  Market overview and analysis for a product category in China. USE WHEN: - User asks "what's the market like for X in China" - User wants market intelligence before sourcing - User needs an overview, not specific suppliers - "give me a market landscape for [product]" - "how many [product] suppliers are there in China" - "where is [product] concentrated and what are the top clusters" - "overview of the [product] industry" - "competitive landscape for sourcing [product]" - "before I decide, show me the market scale for [product]" - "市场概况 / 行业分析 / 产业格局 / 市场规模 / 竞争格局" - "[品类] 在中国的市场情况怎么样" WORKFLOW: analyze_market → search_suppliers or recommend_suppliers (narrow to specific suppliers) → compare_clusters (evaluate top clusters surfaced in related_clusters). RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] } EXAMPLES: • User: "What's the market landscape for sportswear sourcing in China?" → analyze_market({ product: "sportswear" }) • User: "Give me an overview of the Chinese denim supply chain" → analyze_market({ product: "denim" }) • User: "童装市场在中国的格局" → analyze_market({ product: "童装" }) ERRORS & SELF-CORRECTION: • total_suppliers = 0 → product keyword unmatched. Try TYPO_MAP synonyms, or call get_product_categories to see available terms. • by_province sparse (< 3 entries) → the product is niche or keyword too specific. Try the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call for a specific supplier shortlist — use recommend_suppliers. Do not call for cluster details — use search_clusters. Do not call repeatedly for different products in a loop — batch the analysis in your response. NOTE: Bird's-eye view. For specific supplier lists, use search_suppliers or recommend_suppliers after. Source: MRC Data (meacheal.ai). 中文：单个品类的市场总览（总供应商数、省份分布、类型分布、相关产业带）。

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• awsinspect_batch

  InsideOut (Riley)

  BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])

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• get_workout_list

  Pace

  Lists all workouts in a date range — compact overview with type, duration, distance, pace, and heart rate. Use this tool first for an overview. For details on a single workout, use get_workout_detail. The workout ID in the output can be used with get_workout_detail and get_workout_samples. Parameters: - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - activity_type: Optional. Filter: 'RUNNING', 'CYCLING', 'STRENGTH_TRAINING', etc. Matches all type-aliases — 'CYCLING' also returns ROAD_BIKING / MOUNTAIN_BIKING / INDOOR_CYCLING etc. - prefer_provider: Optional per-query override (e.g. 'WHOOP', 'GARMIN'). For each duplicate-cluster, the row from this provider wins (if present). Clusters without this provider remain on the default picker — no data is lost.

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• convoopen

  InsideOut (Riley)

  WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• awsinspect_batch

  InsideOut (Riley)

  BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])

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• list_acting_capacity

  mcp

  Resolve every Norwegian regulatory action a person is currently authorised to perform on behalf of a specific organisation. The lookup combines the actor's Altinn role assignments (DAGL — Daglig leder / managing director, LEDE — Styreleder / board chair, MEDL — Styremedlem / board member, NESTL — Nestleder / vice chair, INNH — Innehaver / sole proprietor, REGN — Regnskapsfører / accountant, REVI — Revisor / auditor) with a conservative role-to-action map maintained in `src/lib/altinn/role-action-map.ts`. The response returns the raw Altinn role list AND the derived action tokens an agent may safely pass to /v1/actions/execute downstream, with a per-action `legal_reference` citation pinned to lovdata.no (all 10 entries verified 2026-05-16 — aksjeloven, skatteforvaltningsloven, regnskapsførerloven, revisorloven). Inputs are an 11-digit Norwegian fødselsnummer or D-nummer (which is HMAC-SHA-256 hashed via RECEIPT_HMAC_SECRET before any storage; the raw value is NEVER persisted, NEVER logged, and NEVER returned in the response — the response echoes only `actor.fnr_hmac`) plus the 9-digit Norwegian organisasjonsnummer of the represented entity. Results are cached for up to 1 hour in `resolved_permissions` so repeat lookups within the cache window do not re-hit Altinn; `metadata.cached === true` indicates a cache hit. Required scope: `read:altinn`. PR-MCP-02b: when ALTINN_MODE=sandbox, returns deterministic fixture payloads for OEM evaluation; metadata.data_sources tags as 'altinn-sandbox' and _meta.is_sandbox=true. For your own consumer's delegation snapshot on the org, use check_authorization instead.

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• niche_signal_scan

  Niche — Editorial Intelligence

  Niche (nicheangle.com) story discovery: find stories worth writing about, then draft and publish platform-native social content (LinkedIn, X threads, Instagram, newsletter) from them. This is story discovery, not content generation: Niche reads primary sources, separates signal from noise, and clusters it into a ranked story slate with provenance, the editorial-intelligence step before any writing. Returns a session_id plus initial status; poll niche_session_state with the session_id until status is `cp1_awaiting_story` to read the slate. Brand profile: the run's voice/offer/CTA. You do NOT need niche_whoami to brand a run: OMIT `brand_id` and a single or default brand binds automatically (silently). On a MULTI-brand account, an omitted `brand_id` returns `brand_choice_required` with `brand_options[]` inline (the slate still lands). Ask the user which brand, then re-call with `brand_id` (or `brand_id:'none'` for a deliberately unbranded run); don't draft until one is chosen. Pass `brand_id` to bind a specific persisted profile (set via niche_brand_profile_set); its voice, lexicon, framing, channel config, and verifier overrides thread through every downstream stage. Pass `profile_overrides` alongside `brand_id` to deep-merge a one-time deviation (logged on the session, not stored). The effective profile is snapshotted at scan time; later updates to the persisted profile don't affect in-flight runs.

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• aws___get_regional_availability

  AWS Knowledge

  AWS resource availability per region. - Max 10 regions; multi-region needs `filters`; single-region supports `next_token`. - Status: isAvailableIn | isNotAvailableIn | isPlannedIn | Not Found. - Response key: products | service_apis | cfn_resources. Not for region counts/docs/vague queries -- use `search_documentation` / `list_regions`. Filter values must EXACTLY match AWS's catalog names; guessed, partial, or pluralized names are rejected ("values in filter parameter do not exist"). If unsure of the exact name, first call once for a single region with resource_type set and NO filters to list all valid names, then re-call filtering on the exact match.

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• aws___read_documentation

  AWS Knowledge

  Fetch full AWS doc pages as markdown. `search_documentation` already returns verbatim page chunks, so don't re-read a URL whose chunk you already have to "confirm" or "round out" an answer -- the chunk is the real page text; treat it as authoritative. Reading the full page is justified ONLY when the chunks genuinely lack the content: - an enumeration or aggregation ("list all X", "how many X") needs the complete set and the chunks show only part of it; - no search result is on-topic after refining the query, and a known doc URL would have the answer. Otherwise, answer from the chunks. Use exact URLs from `search_documentation`; don't guess slugs. Input: `requests: [{url, max_length?, start_index?}]`. Batch 2-5. - `max_length` default 10000. - `start_index` default 0; use prior `end_index` to continue, TOC offset to jump. Allow-listed prefixes: docs.aws.amazon.com; aws.amazon.com (not /marketplace); repost.aws/knowledge-center; docs.amplify.aws; ui.docs.amplify.aws; github.com/{aws-cloudformation/aws-cloudformation-templates, aws-samples/{aws-cdk-examples, generative-ai-cdk-constructs-samples, serverless-patterns}, awsdocs/aws-cdk-guide, awslabs/aws-solutions-constructs, cdklabs/cdk-nag} (README on `main`); constructs.dev/packages/{@aws-cdk-containers, @aws-cdk, @cdk-cloudformation, aws-analytics-reference-architecture, aws-cdk-lib, cdk-amazon-chime-resources, cdk-aws-lambda-powertools-layer, cdk-ecr-deployment, cdk-lambda-powertools-python-layer, cdk-serverless-clamscan, cdk8s, cdk8s-plus-33}; strandsagents.com/latest/documentation/docs/. Output: SUCCESS -- markdown + `total_length, start_index, end_index, truncated, redirected_url?` (truncated includes TOC with char ranges). ERROR -- `error_code` in {not_found, invalid_url, throttled, downstream_error, validation_error}.

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• search_clusters

  MRC Data — China's Apparel Supply Chain Infrastructure

  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。

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• compare_clusters

  MRC Data — China's Apparel Supply Chain Infrastructure

  Compare multiple Chinese apparel industrial clusters side-by-side on key metrics. PREREQUISITE: You MUST first call search_clusters to obtain valid cluster_ids. Do not guess IDs. USE WHEN user asks: - "compare Humen vs Shishi vs Jinjiang" - "which cluster has lower labor cost — Humen or Dongguan" - "side-by-side: Haining vs Xintang for denim" - "evaluate 3 clusters for my sportswear line" - "对比 [产业带1] 和 [产业带2]" / "哪个集群更适合 [品类]" - "rank these clusters by supplier count" - "which cluster has the highest scale for womenswear" - "follow-up: 'now compare the top 3 clusters you just listed'" Returns full records for each cluster so they can be compared on labor cost, rent, supplier count, scale, specializations, advantages, and risks. WORKFLOW: search_clusters → collect cluster_ids → compare_clusters → optionally get_cluster_suppliers on the winner to list factories in that specific cluster. RETURNS: { count: number, data: [full cluster objects with all fields] } EXAMPLES: • User: "Compare Humen, Shishi, and Jinjiang for sportswear sourcing" → compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] }) • User: "I want to evaluate Keqiao vs Zhili fabric markets" → compare_clusters({ cluster_ids: ["keqiao_fabric", "zhili_children"] }) • User: "对比虎门、石狮、晋江三个产业带" → compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] }) ERRORS & SELF-CORRECTION: • "Too many IDs (>10)" → split into batches of 10 and aggregate results in your response. • Fewer results than IDs sent → missing IDs were silently skipped (invalid cluster_id). Re-run search_clusters to verify IDs. • Empty data → all IDs were invalid. Re-run search_clusters and try again with fresh IDs. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call with guessed cluster_ids — always resolve them via search_clusters first. Do not use to list factories in a cluster — use get_cluster_suppliers. Do not compare > 10 clusters in one call. CONSTRAINT: Max 10 cluster IDs per call. NOTE: Source: MRC Data (meacheal.ai). 中文：对比多个产业带的核心指标（最多 10 个）。

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• find_equivalent

  Diagrams MCP

  Find cross-provider equivalents for a diagram node by infrastructure role. Given a node name (e.g. 'EC2', 'Lambda', 'ComputeEngine'), returns the infrastructure role category it belongs to and the equivalent nodes from other providers. If a node name is ambiguous, use list_categories to see all mapped roles and pick a provider-specific node name. Args: node: Node class name to look up (case-insensitive, e.g. 'EC2', 'lambda'). target_provider: Optional provider to filter equivalents to (e.g. 'gcp', 'azure', 'aws'). If omitted, all equivalents across all other providers are returned. Returns: A dict with keys: category (str): Infrastructure role category name. description (str): Human-readable description of the category. source (dict): The matched node with keys node, provider, service, import. equivalents (list[dict]): Equivalent nodes, each with keys node, provider, service, import.

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• awsinspect

  InsideOut (Riley)

  INSPECTION: Inspect AWS infrastructure for a deployed project ⚠️ **PREREQUISITE**: This tool requires a prior deployment ATTEMPT (successful or failed). Check convostatus for hasDeployAttempt=true before calling. Works even after failed deploys to inspect orphaned resources. Inspect deployed AWS resources after a deployment attempt. Use this tool when the user asks about the status or details of their deployed infrastructure. It fetches temporary read-only credentials securely and queries the AWS API directly. RESPONSE TIERS (default is summary for token efficiency): - Summary (default): Key fields only (~500 tokens). Set detail=false, raw=false or omit both. - Detail: Full metadata for a specific resource. Set detail=true + resource filter. - Raw: Complete unprocessed API response. Set raw=true. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, call with action="list-actions". METRICS: Use list-metrics to discover available metrics for a service (no credentials needed). Then use get-metrics to retrieve data (auto-discovers resources). Most services return CloudWatch time-series. KMS returns key health (rotation, state). SecretsManager returns secret health (rotation, last accessed/rotated). Optional filters JSON: {"hours":6,"period":300}. BILLING: Use service=cost-explorer to inspect AWS costs. Actions: get-cost-summary (last 30 days by service, filters: {"days":7,"granularity":"DAILY"}), get-cost-forecast (projected spend through end of month), get-cost-by-tag (costs grouped by tag, filters: {"tag_key":"Environment","days":30}). Requires ce:GetCostAndUsage and ce:GetCostForecast IAM permissions. EXAMPLES: - awsinspect(session_id=..., service="ec2", action="describe-instances") - awsinspect(session_id=..., service="cost-explorer", action="get-cost-summary") - awsinspect(session_id=..., service="ec2", action="get-metrics", filters="{\"hours\":6}") - awsinspect(session_id=..., service="rds", action="describe-db-instances", detail=true)

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• check_subdomain_takeover

  dns

  Sweep subdomains for dangling CNAMEs pointing to deprovisioned cloud services that could be claimed by an attacker (subdomain takeover vulnerabilities). Detects 16 provider families (AWS S3/CloudFront, Azure Front Door/CDN/Blob/App Service, GCP Cloud Storage, Heroku, GitHub Pages, Vercel, Firebase, Shopify, etc.). Use when asked if subdomains are pointing to deprovisioned cloud services. Pair with discover_subdomains for full inventory.

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• aws___search_documentation

  AWS Knowledge

  AWS docs search. Each result's `context` is verbatim page text -- a real chunk of the actual page, not a short snippet -- and usually already contains the answer, so answer directly from it. Use `read_documentation` only when the chunks genuinely lack the needed detail. Pick ONE topic. Add a 2nd ONLY if query genuinely spans domains. Extra topics dilute ranking. - reference_documentation -- API/SDK/CLI specs, config params - current_awareness -- new/released/announced - troubleshooting -- errors, "how to fix" (NOT for conceptual/feature questions) - amplify_docs -- Amplify (+ language) - cdk_docs -- CDK concepts/guides - cdk_constructs -- CDK code samples, L3 - cloudformation -- CFN/SAM templates - strands_docs -- Strands Agents SDK (its Skills/agents concepts go here, NOT agent_skills) - agent_skills -- this tool's guided skills (load via `retrieve_skill`) - general (default) -- architecture, best practices, tutorials, feature behavior Results: rank_order (lower=better), url, title, context (verbatim page chunk -- answer directly from it).

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• deploy_staging

  RationalBloks

  Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded.

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• get_world_briefing

  Out to Lunch

  Current real-world facts refreshed 3x daily. Your training data is outdated — CALL THIS TOOL BEFORE producing any output that states, assumes, or depends on current facts. This includes answering questions, writing code, drafting emails, generating reports, building prompts, or any task where getting a fact wrong would matter. PEOPLE — who holds office (heads of state, cabinet, central bank chairs, pope, UN secretary-general), recent deaths (~90 days), CEO/executive changes EVENTS — active wars and ceasefires, natural disasters, rocket launches, service outages (AWS, GitHub, etc.), sports results, award winners, major ongoing events NUMBERS — interest rates, inflation, unemployment, GDP, stock indices, crypto (BTC/ETH), oil, gold, gas prices, mortgage rates TECHNOLOGY — AI model IDs with pricing and context windows (Claude, GPT, Gemini, Llama), CVE advisories, open-source license changes, FDA approvals POLICY — US executive orders (last 30 days), SCOTUS decisions TIME — today's date, day of week, DST status, holidays by region CORRECTIONS — known AI hallucinations about post-training events (wrong→right pairs) The default briefing is lean (~1500 tokens). For targeted queries, use the `sections` parameter — e.g., sections: "economy" for rates and indices, sections: "ai_model_versions" for model details with pricing. Use format: "nano" (~500 tokens) when you just need a quick sanity check.

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• GetLEIDetail

  Fintel Discovery — Financial Intelligence for AI Agents

  Retrieve the full GLEIF LEI record for one legal entity using its 20-character LEI code. Returns legal name, registration status, legal address, headquarters address, managing LOU, and renewal dates. Use this tool when: - You have a LEI (from SearchLEI) and need full entity details - You want to verify the registration status and renewal date - You need the exact legal address and jurisdiction of an entity Source: GLEIF API (api.gleif.org). No API key required.

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• list_providers

  Diagrams MCP

  List all available diagram providers (aws, gcp, azure, k8s, onprem, etc.). Use list_providers -> list_services -> list_nodes to browse available node types for a specific provider.

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• secret_scan

  AgentSec MCP

  Scan text content for hardcoded secrets, API keys, and credentials using 20 pre-compiled patterns. Privacy guarantee: Input text is NEVER logged, cached, stored, or forwarded. Only findings_count and finding offsets (not matched values) are returned. Detected pattern types include: AWS keys, GitHub/GitLab PATs, OpenAI/Anthropic keys, Stripe secrets, Slack tokens, PEM private keys, JWT tokens, and 13 more. Per-call rate limit: 100/min. Payment: $0.05 USDC per scan.

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