"namespace:io.github.majorelalexis-stack" matching MCP tools:

• convoinspect

  InsideOut (Riley)

  INSPECTION: View a session's conversation transcript and metadata Returns the full message history (user / assistant / tool turns) plus the session's meta — workflow step, cloud, deployment status, drift state. This is the transcript-reader companion to the other read tools — combine it with: • `convostatus` for the live stack / config / pricing • `tfruns` for deployment history (apply / destroy / plan / drift) • `stackversions` for the stack-version ladder Use it when a user asks 'what did I say earlier?' or you need to retrace why the session ended up where it did. Read-only; never mutates session state. REQUIRES: session_id (format: sess_v2_...).

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• check_compatibility

  DepScope

  Is this specific multi-package version combo verified to work together? USE WHEN: pinning a stack (next@15 + react@19 + node@22); before recommending a version matrix. RETURNS: {compatible, conflicts[], notes}.

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• detect_stack_from_text

  StackSwap

  Infer a GTM stack from a freeform text blob (a careers page, job posting, public site HTML, RFP, 'What we use' doc, browser DevTools network tab, etc.). Returns ranked tool matches with confidence levels (high/medium/low) and evidence snippets, plus a ready-to-use array for chaining into `scan_stack` or `find_overlaps`. Use when the user says 'I don't know what we use' or pastes a competitor's careers page to scout. Conservative on ambiguous short tokens — multi-mention or canonical-name matches win.

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• deploy_compose

  mcp

  Deploys a MULTI-CONTAINER app — a repo that ships a docker-compose.yml / compose.yaml (app + its own db/redis/worker containers) — onto ONE VM via podman-compose, and exposes ONE service at https://<name>-<id>.redu.cloud. Use this instead of deploy_app when the repo is a compose stack rather than a single Dockerfile. SAME prereqs + source modes as deploy_app: run check_deploy_prerequisites (network_id + keypair_name), then GIT (`repo`, +git_token for private) or UPLOAD (prepare_upload → source_token). PORT: pass the HOST port the exposed service publishes (the LEFT side of its `ports:` mapping) — redu probes + proxies that exact port; pass `service` to name which service it is (plan_deploy detects both). DB: 'compose' (default) uses the stack's own db service (self-contained); 'single_vm'/'managed' provision a Postgres/MySQL and APPEND its conn env (DATABASE_URL/PG*/MYSQL_*) to the project .env — your compose must REFERENCE those vars to use it (we never rewrite your compose file). Build+provision can take 4-40 min (it pulls/builds every service — heavy ClickHouse/Kafka stacks are slow); poll get_deployment until status='ready', and on failure read build_log (it captures podman-compose logs). TIPS: (1) prefer the project's PREBUILT published images — swap any `build:` block for the published `image:` tag (building from source on the VM is less reliable). (2) redu injects APP_URL/PUBLIC_URL (= the app's public URL) into the env — map the app's own URL/cookie-domain var (SERVER_URL/NEXTAUTH_URL/…) to ${PUBLIC_URL}. (3) multi-surface apps (dashboard + API on separate ports) → pass `expose:[{port,service},…]`, each gets its own URL. (4) if the stack needs a ONE-TIME DB migrate/prepare before it serves (Rails `rails db:prepare`, Django `migrate`, Prisma `migrate deploy` — e.g. Lago), pass `migrate_command` (+ `migrate_service`); without it the stack deploys to 'ready' but 502s on real use because the schema is missing. ALWAYS run plan_deploy first and confirm the plan + cost with the user.

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• get_task_result

  OctoPerf

  [Tasks] Poll the lifecycle of an async OctoPerf task by id. Returns status=PENDING while the task is still running (poll again after 2-3 seconds), status=SUCCESS once it has settled successfully, or status=FAILED with the backend stack trace in `message` if it has failed. Use this after any tool that submits an async task (e.g. `apply_correlations_to_virtual_user`).

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• domain_report

  ContrastAPI

  Query DNS, WHOIS, SSL, subdomains, and threat intel for a domain in one call. By default dns.txt is filtered to security-relevant entries (SPF, DMARC, DKIM, MTA-STS, TLS-RPT) and dns.total_txt_records reports the honest pre-filter count; pass include_all_txt=true for the raw TXT list. Use as a starting point for domain investigations; use audit_domain for live headers + tech stack. Response carries next_calls — chain with subdomain_enum (always emitted), ssl_check + tech_fingerprint (when an A record resolves) for the standard recon depth without re-prompting. Free: 30/hr, Pro: 500/hr. Returns domain report with DNS records, WHOIS data, SSL cert, risk score, email config, threat status, recommendation, and next_calls.

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• get_since

  security-intel

  Delta feed for agents that poll on their own clock: what's new since you last checked. Free. Pass the `cursor` from your previous call (omit on first call); poll as often as you like. Returns a lightweight index of new items — id, title, item_type, CVE id, severity, the signed report_id each was published in, and published_at — plus a new `cursor` and `count`. count == 0 means nothing new since you last looked. To get the full bodies (affected ranges, sources, assessment, remediation) for what's new, call the paid get_today (or check_affected to test your own deps). Optional `stack` filters by relevant_for tags (same as get_today). Returns: {cursor, count, index}.

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• audit_stack

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Audit a technology stack for exploitable vulnerabilities. Accepts a comma-separated list of technologies (max 5) and searches for critical/ high severity CVEs with public exploits for each one, sorted by EPSS exploitation probability. Use this when a user describes their infrastructure and wants to know what to patch first. Example: technologies='nginx, postgresql, node.js' returns a risk-sorted list of exploitable CVEs grouped by technology. Rate-limit cost: each technology requires up to 2 API calls; 5 technologies counts as up to 10 calls toward your rate limit.

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• resolve_kb_id

  fixflow

  FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.

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• nanmesh.trust.review

  NaN Mesh

  Cast your expert +1 or -1 review on any entity. Use AFTER evaluating a tool you searched for or tried. Expert reviews are 70% of ranking. One review per agent per entity (overwrites previous). Requires agent_key. For no-auth alternative, use nanmesh.trust.favor instead. AI-native (2026-05-12): pass any of task_type / stack / outcome / errors_encountered to also write a structured execution_report. Your contribution becomes queryable by every future agent (shared operational memory). Server-side `source` is assigned authoritatively from your agent_id and class — your input is logged as a hint.

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• tech_fingerprint

  ContrastAPI

  Detect website technology stack: CMS, frameworks, CDN, analytics tools, web servers, languages (via HTTP headers + HTML analysis). Use for passive reconnaissance; for full audit use audit_domain. Free: 30/hr, Pro: 500/hr. Returns {technologies: [{name, category, confidence%, version}]}.

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• get_tech_stack

  Makuri Showcase (CogniLedger)

  Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• tech_stack_cve_audit

  ContrastAPI

  Composite tech-stack + CVE audit (MCP-only, no REST endpoint). Detects technologies on the target domain, queries CVE database for known vulnerabilities per product, enriches top-10 CVE candidates with CISA KEV federal patch deadlines, and checks public exploit / PoC availability. Identical for every tier — all data is sourced from local DB mirrors (no Shodan/AbuseIPDB), so there is no tier gating. CVE candidate batch: 50. Cost: 10 tokens per call — Free 30/hr ≈ 3 audits, Pro 500/hr ≈ 50 audits. Returns {domain, technologies, cves_by_tech, kev_findings, exploit_findings, summary, next_calls}.

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• simulate_remediation

  FaultKey · CausalLayer

  Counterfactual remediation simulator. Given a certificate's verdict + fourFactorScoring + agents and a list of remediation IDs from the FK-METHOD-2026-003 catalog, return the apportioned shares each remediation would have produced (in isolation) and the composite shares if they all stack. Every remediation cites a specific statute or standard. GET /api/v2/remediation/catalog for the list of IDs. Cost: 1 credit (same price as verify_certificate). Pure deterministic; same inputs produce a byte-identical result.

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• pentest_map_techniques

  pentest-mcp-server

  Given a profile of the authorized test target (technology stack, exposed services, authentication type, OS), return a ranked list of ATT&CK techniques and OWASP test cases most relevant to that profile — not a generic dump of all techniques. Ranking factors: platform match, service match, auth type exposure, technique prevalence. Each result includes why it is relevant to this specific profile, the detection opportunity, and the recommended mitigation. Use when starting an authorized engagement to prioritize the testing scope; pair with pentest_guide to get the full methodology for each top-ranked vector.

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• devops_list_vendors

  devops-status-mcp-server

  List vendors in the built-in registry, optionally filtered by category or name search. Returns slug, display name, category, and status page URL for each entry. Use to discover the correct slug to pass to other tools, or to see which vendors are available before configuring a stack.

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• stacks.recommend

  BuyAPI

  Recommends a complete stack from BuyAPI's corpus with a structured decision matrix, cost estimate, assumptions, unknowns, alternatives, and sources. Use this when the user is starting a project or asks for a complete multi-layer stack choice. Do not use this for local coding/debugging/docs questions that do not involve software or vendor selection. Do not call vendors.resolve first; this tool handles retrieval and ranking.

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• generate_image

  switch

  Generate one or more Switch images. Auto-routes to the right model based on subject (Nano Banana 2 default, GPT Image 2 for swimwear/beach, Switch Model/Ultra/Pro for sexier content, Nano Banana Pro for typography-heavy). Counts <= 8 render inline in chat; counts > 8 queue to your Switch Studio with progress polling. All images persist to your Studio library and folder. Pass an optional `style` (e.g. "wellness/warm_amber_tropical", "high_fashion_editorial/testino_glossy", "movie_scene/neon_noir_action") to apply a curated photographic stack from the apply_* skill tools.

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• score_account_fit

  StackSwap

  Score and rank the user's OWN accounts by StackSignal-style fit: a 0-100 composite blending ICP Match (firmographic fit to a supplied ICP), Intent (engagement/pipeline signals on the account), and an optional Stack Fit layer. Pass `config.icp` (segments / industries / minArr) to drive ICP Match — without it, scoring falls back to Intent only and says so. Stack Fit stays dormant unless `config.icp.idealStack` is supplied. Accepts loosely-typed account records (aliases for segment, industry, arr, lastActivityDays, openPipeline, techStack are normalized). Returns the book ranked highest-fit-first with each layer's sub-score. This scores the accounts the user already owns (the StackSignal product) — it does NOT return net-new accounts to buy. Use when the user asks 'which of my accounts best fit our ICP', 'rank my book by fit', or pastes accounts plus an ICP definition.

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• ultraFastIsHoneypot

  LION - keyless x402 data + RPC for agents

  Pure pre-swap honeypot check for autonomous sniping, MEV and trading agents - is this token a honeypot/trap before you buy? After paying, GET /api/x402/token-risk-indicators-json?token=0x...&view=honeypot with the Payment-Signature header. Returns a compact Ed25519-attested verdict: { isHoneypot, honeypotScore 0-100, high_risk_functions (mint/pause/blacklist/fee-modifier found in verified source), contract_verified, ownership_renounced, attestation }. Use cases: block a honeypot before a snipe; gate every buy on a fast honeypot check; pair with ultraFastLiquidityCheck + lion_token_risk_indicators for a full pre-trade safety stack under $0.02 per decision. Keyless x402, flat $0.005 on Base. [x402 paid tool: GET /api/x402/token-risk-indicators-json?src=mcp returns the 402 challenge with the canonical payTo; price 0.005 USDC on Base eip155:8453.]

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