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773,131 tools. Updated 2026-11-12 20:24

"AWS Organizations" matching MCP tools:

  • 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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  • Get information about the organization your agent is linked to. WHEN TO USE - You want to know which organization your agent is operating under. - You need to list the members of your linked org (e.g., to decide which member should review a deliverable). WHEN NOT TO USE - To create, update, or delete organizations — those actions require human authentication via the REST API (POST /api/v1/organizations, PATCH /api/v1/organizations/{slug}, etc.). BEHAVIOR - Read-only. Auth required: agent API key. Rate-limited to 60 req/min. - Returns an error if your agent is not linked to any organization (agents.org_id IS NULL). - action='get_my_org': returns org name, slug, tier, owner, and member count. - action='list_members': returns human_id and role for each member. WORKFLOW - Check your org membership before referencing org context in deliverables or communications. - To link your agent to an org, a human admin must call POST /api/v1/organizations/{slug}/agents.
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  • 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.
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  • Browse or search canonical employers, associations, schools, or worship organizations that appear in positive conditions in current active eligibility publications. Omit query to browse by active-publication graph connectivity; that order is NOT popularity, quality, or a recommendation. With query, normalized exact matches rank before bounded prefix matches. A returned organization is a selectable fact, NOT proof that this person can join any credit union; use resolve_eligibility_entities for authoritative selection, then pass the returned org_id in the matching *_org_ids field to an eligibility tool. This authenticated discovery never loads a client-side corpus and accepts no personal profile.
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  • Toggle the LIFECYCLE flags isPublic and servicialoPublished. ⚠️ This tool does NOT grant public discovery consent — discovery in the Servicialo registry requires Organization.discoveryConsent=true, which can ONLY be set by the human owner via PATCH /api/organizations/:slug/servicialo from the web panel (a deliberate, authenticated consent action). Without consent, the cron registry-sync and resolver-heartbeat skip the org, so calling this tool with discoverable=true on a non-consented org flips local flags but does not publish anywhere. Requires X-Org-Api-Key header.
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  • 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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Matching MCP Servers

  • A
    license
    A
    quality
    B
    maintenance
    Enables searching and reading AWS knowledge from sources the official AWS Knowledge MCP Server does not index, including re:Post community Q&A, kiro.dev documentation, and AWS Builder Center posts.
    3
    Apache 2.0

Matching MCP Connectors

  • 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.

  • AWS cloud service announcements & updates. $0.01/query. Be born in one call — free testnet funds.

  • 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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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
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  • 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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  • [pqc_signature] 使用 ML-DSA 私钥对消息签名(FIPS 204)。 【双模式】sign_mode 支持 RAW(默认)和 EXTERNAL_MU 两种模式。 - RAW 模式:直接签名原始消息(liboqs,最大 256 字节) - EXTERNAL_MU 模式:先计算 mu = SHAKE-256(tr||M', 64),再通过 OpenSSL 3.5+ mu 模式签名(最大 2048 字节),与 AWS KMS ML-DSA EXTERNAL_MU 语义等价 【消息长度策略】 - RAW:最大 256 字节,空消息合法 - EXTERNAL_MU:最大 2048 字节,空消息合法 - > 256 且 <= 2048 字节:使用 EXTERNAL_MU - > 2048 字节:拒绝 【算法】algorithm 支持 ML-DSA-44 / ML-DSA-65(默认)/ ML-DSA-87。 【参数】 - private_key_in_hex:ml_dsa_keygen 返回的私钥 hex - public_key_spki_in_hex:EXTERNAL_MU 模式必填,ml_dsa_keygen 返回的公钥 hex(SPKI DER) - message_in_hex:待签消息 hex - context_in_hex:可选上下文 hex(最大 255 字节) - sign_mode:RAW 或 EXTERNAL_MU(默认 RAW) 【输出】signature_in_hex、signature_in_base64、algorithm、message_bytes、signature_bytes。
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  • Start linking a cloud account so it can be scanned. `provider` is "aws", the only provider supported today; `account_id` is the 12-digit AWS account number. `software` is the Tomorrow Central software the link is for; omit it for Cloud Cost Sentinel. Each software has its own read-only role, so link once per software. This is a two-party flow and you cannot finish it alone — creating the read-only IAM role requires the human's AWS credentials. Returns a `launch_url`. Hand the human these instructions verbatim: 1. Open the launch_url (a pre-filled CloudFormation quick-create link). 2. Review the read-only role it creates, then click Create stack. 3. Tell you when the stack says CREATE_COMPLETE. Then call `verify_connection` with the returned connection_id. Verification fails with a clear message until the role exists, so polling it every ~15 seconds is safe and expected. No ARN or secret needs to be copied by anyone.
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  • Resolve a name or prefix to APITube taxonomy IDs so you can use them in the `search_news` tool's filters. The `search_news` tool's most precise filters require IDs you cannot guess: - entity.id — numeric (persons, organizations, locations, brands, events...) - category.id — IPTC slug, e.g. "medtop:04000000" - topic.id — slug - industry.id — numeric Use this tool FIRST to look those IDs up, then pass them into `search_news`. USAGE: - type: which taxonomy to autocomplete — one of "entities", "categories", "topics", "industries". - prefix: the name (or its beginning) to search, e.g. "Tesla", "Elon", "spo". Returns an array of matches; take the `id` of the best match and put it into `search_news`: suggest({ type: "entities", prefix: "Tesla" }) → [{ id: 12345, name: "Tesla, Inc.", ... }] search_news({ entity: { id: "12345" } })
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  • Price a list of cloud components at real list prices. No account or signup. Use this when the user can name the pieces: "two m5.large servers, a Postgres database and a load balancer in Mumbai". If they cannot name the pieces, use `describe_workload` instead, which asks in units a person actually has. `items` is a list of `{"component": "<id>", "params": {...}}`. Get the ids and each component's parameters from `list_cost_building_blocks` and use them verbatim: a name you invent will be rejected, which is deliberate. `region` is an AWS region id such as `ap-south-1`. The reply is a RANGE, not a number: `monthly_total` has low, expected and high, because several inputs are genuinely uncertain and a single figure would hide that. `swing_factor` names the one input that most widens the range, which is the most useful sentence in the reply: it tells the user what to go and measure. `concentration` ranks the lines by share of the bill. `assumptions` lists every value that was defaulted rather than given, and `traps` lists costs this architecture usually incurs that the caller did not ask for, such as public IPv4 addresses or database storage billed apart from the database. `gaps` lists anything that could not be priced. It is never silently treated as zero. If a line is missing, the total is missing that line, and the gap says so. These are AWS on-demand list prices before any discount, credit or committed-use agreement, and they exclude tax. Treat the result as an upper bound on a real bill and never as a quote.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
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  • Fetch the guidance on one FinOps topic - the billing mechanics, decision rules and worked examples behind a defensible answer - either whole or one section at a time. Use this when you need the actual content of one known reference, after ``list_references`` or ``find_references`` told you which one serves the question. Advisory questions (commitment sizing, chargeback design, allocation methodology) are served by the reference's decision rules and worked examples, which a summary does not carry. ``section`` is useful when the question is narrower than the file. The ``approx_tokens`` hint in the listing shows when this matters: the largest references run to about 15,000 tokens and the Azure pattern catalogue is an enumerated list, so a question about storage lifecycle is served by one section of ``finops-azure-patterns``. Without ``section`` the whole file comes back, with its cross-cutting reasoning. Args: name: Reference name as returned by ``list_references`` (e.g. ``"finops-aws"``, ``"finops-genai-capacity"``, ``"optimnow-methodology"``). section: Optional H2 or H3 heading to return on its own. Matched case-insensitively and partially against the headings, so a natural phrase works - ``"storage"``, ``"commitment decision tree"``. A heading's trailing count is ignored, so ``"storage optimization patterns"`` matches ``"Storage Optimization Patterns (28)"``. If it matches nothing you get the list of available headings back, not the whole file. Without ``section``, returns ``{"name": ..., "content": "...", "lines": N}`` where ``content`` is the file verbatim. With ``section``, returns ``{"name", "title", "section", "section_level", "partial": true, "content", "lines", "full_lines"}`` where ``content`` is that section prefixed by the reference's title, plus ``other_matching_sections`` when the phrase matched more than one heading. On a miss, returns ``{"error": ..., "suggestions": [...]}``. An unknown name gives up to three string-distance matches; an unmatched ``section`` gives ``available_sections`` - every heading in the file - so the retry is exact.
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  • List published workflows across organizations available to the authenticated principal, or in one organization when organization_id is given. Returns {workflows}, each with its owning organization, ordered by most recent update (ties by organization ID, then workflow ID) and capped at limit. There is no cursor: filter by organization_id or raise limit to see more. Use get_workflow to inspect a selected workflow's parameters, side effects and gates before start_workflow.
    ConnectorAPI key