"Multi-agent AI systems for collaborative code development" matching MCP tools:

• shillbot_complete_task

  Swarm Tips — Aggregated AI Agent Activities

  [IN DEVELOPMENT] [READ] Single-call "what do I do next?" wrapper that collapses the multi-step Shillbot task lifecycle into one ask-then-execute loop. Pass a task_id; the tool reads the current on-chain + Firestore state, figures out whether you're the AGENT (claimer) or CLIENT (campaign owner) for this task, and returns a structured `next_action` block with the exact next tool to call and its arguments. The lifecycle has unavoidable external waits (T+7d oracle window for YouTube, client review, challenge window) — this tool surfaces them as `wait` actions with a `not_before` timestamp instead of a tool call. Re-call after each step (or after the wait elapses). Returns `done` when the task is Finalized. Optional `network`: 'mainnet' (default) or 'devnet'.

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• global_indicator

  dynamic-feed

  A flagship development statistic from Our World in Data: the latest value for a country plus a short multi-year trend, with full source attribution. ONE source, MANY indicators (breadth) — CO2 per capita, population, fertility, urbanisation, GDP-per-capita (a development stat in PPP, NOT a market price), extreme poverty, R&D spend, Human Development Index, literacy, internet access, electricity access. Distinct from `global_macro` (World Bank): OWID adds the long-run development + climate set. `indicator` = a slug/alias from the curated allowlist (default "co2-emissions-per-capita"; aliases: co2, pop, gdp, hdi, literacy, internet, poverty, fertility, urban, rd) — call indicator="list" for the full menu. `country` = ISO-3 code (AUS, USA, CHN, GBR, IND, …); omit for the World aggregate. Source: Our World in Data (ourworldindata.org) — OWID's processing layer is CC BY 4.0, keyless; every response carries BOTH OWID's attribution AND each underlying producer's citation + licence. Only indicators whose underlying sources are cleared for commercial re-serving (CC BY / CC BY IGO / CC0 / public domain) are served — a fail-closed runtime gate refuses any non-redistributable indicator. Annual-ish statistics, not a live-telemetry feed.

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• tool_recommend

  gapup-mcp

  Cross-tool recommendation system: given a free-text intent, returns the most appropriate tools from the 170+ Gapup MCP catalogue, ranked by confidence, with pre-filled input suggestions and an optimal multi-tool chain when applicable. Use this first when you are unsure which tool to call — it navigates the full catalogue for you. Supports 15+ static pre-designed chains for frequent intents (M&A due diligence, sanctions screening, ESG 360, AI Act compliance, FTO patent clearance, crypto wallet tracking, etc.). Domains: compliance | finance | intel | legal | content | data | trade | infra. Pure compute — $0.01/call, no external fetch. Ideal as a first call in any multi-step agent workflow.

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• agent_build

  mcp

  Hand a natural-language prompt to the FreeAppStore VibeCode AGENT — the platform's own AI writes the code AND deploys it. This is different from create_app/update_files (where the CALLING model writes the code): here you just prompt, and the platform builds. Uses your stored AI key (provider must be in your vault). Long-running; it builds in the background. Returns the session_id — poll agent_status to watch it and get the live URL. Tip: include the app id in your prompt, e.g. 'Build a dice roller and deploy it as dice-roller'.

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• scan_site

  AEO Scanner

  Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.

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• scout.market_pulse

  Scout

  Public-data-backed market snapshot for any US zip code. Returns Census ACS demographics (population, median household income, median age, owner-occupancy %, median home value, median gross rent), Scout's indexed agent density for that zip, and listings activity if the zip is in our MLS-live coverage area. Call this when an AI user asks 'what's the market like in [zip/city]'. Free, no API key required.

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• academy_welcome

  StudioMeyer Academy

  ALWAYS call this first when a user connects or asks what this is. Returns a short orientation for StudioMeyer Academy — a free 6-level 'Memory-First AI Operator' curriculum (Levels 1-3 fundamentals, 4-6 memory/MCP/multi-agent), plus playbooks and build recipes. Read it back to the user in their language and offer to start at their level.

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• agent_brief

  Colour Memory

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.

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• list_services

  Parallax Ventures

  List Parallax’s services with real pricing. Filter by track: "ai" (done-for-you AI agent teams), "music" (Parallax Records / Baba Studio production), or "all".

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• tbs_get_info

  Trust Boundary Systems

  Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.

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• audit_site

  AEO Scanner

  Full AI visibility audit across 67+ checks in 12 categories (4 AEO + 4 GEO + 4 Agent Readiness). Returns detailed per-check scores with specific issues and recommendations, AI Identity Card with mention readiness and detected competitors, and business profile. GEO checks include 3 research-backed citation signals: factual density, answer frontloading, and source citations. Agent Readiness covers emerging agent-discovery standards Cloudflare's isitagentready.com evaluates: RFC 9727 api-catalog, SEP-1649 MCP Server Card, and IETF Content-Signal (draft-romm-aipref). Does NOT generate fix code — use fix_site for that, or compare_sites to benchmark against a competitor. Pay per call ($1.00) via x402 — USDC on Base or Solana. Machine payment via signed X-PAYMENT header; see https://www.x402.org/. On payment_required, the response includes the full x402 payload with payTo/amount/asset.

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• x711_web_search

  x711 — Universal Agent Gas Station

  Multi-source web search with automatic fallback chain: HackerNews Algolia → Wikipedia REST → DuckDuckGo → x711 Hive collective intelligence. Always returns results — if live web sources are unavailable, falls back to community-sourced agent knowledge from The Hive. Best for: tech/AI/crypto queries, current events, documentation discovery. Returns: { query: string, results: Array<{ title, url, snippet }>, source: string ('HackerNews'|'Wikipedia'|'DuckDuckGo'|'x711_hive'), count: number }. Free tier: 10 calls/day, no API key needed.

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• submit_feedback

  InsideOut (Riley)

  FEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use this tool to report issues it encounters during operation. REQUIRES: session_id, category, message OPTIONAL: user_email (for follow-up), user_name, source (default: 'mcp'), initiator ('user' or 'agent') Categories: bug_report, feature_request, general_feedback, question, security The 'initiator' field tracks who triggered the report: - 'user' — the user explicitly reported the issue or requested feedback submission - 'agent' — Riley detected an issue and initiated the feedback flow Examples: - User says 'the deploy button is broken' → submit_feedback(category='bug_report', message='...', initiator='user') - User says 'I wish it had dark mode' → submit_feedback(category='feature_request', message='...', initiator='user') - Deployment failed with Terraform error → submit_feedback(category='bug_report', message='Deployment failed: Terraform apply error on aws_alb resource — timeout waiting for ALB provisioning', initiator='agent')

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• vertical_ai_agent_governance

  gapup-mcp

  Generates a comprehensive vertical AI agent workforce integration plan for CHROs, including governance frameworks, human-AI collaboration metrics, and upskilling recommendations. Inputs: industry vertical, workforce size, and current AI adoption level. Outputs: role-specific AI integration roadmaps, skill gap analysis, and performance benchmarks. Uses O*NET skill taxonomies and Gartner AI adoption trends. For best results with large datasets, pass async:true to avoid timeout.

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• agents_list

  dialogbrain

  List all AI agents configured in the workspace. Returns agents with their basic info, trigger count, and knowledge collection count. Each agent's `description` field tells you when that agent is useful. If you're a router-style agent deciding whether to delegate via `agent.handoff`, read descriptions and pick the best fit. Use this to: - See all configured AI agents - Filter by status (active/paused/archived) - Get agent IDs for further operations

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• tbs_get_info

  Dr. Maggie Geller, OD

  Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.

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• x711_web_search

  x711

  Multi-source web search with automatic fallback chain: HackerNews Algolia → Wikipedia REST → DuckDuckGo → x711 Hive collective intelligence. Always returns results — if live web sources are unavailable, falls back to community-sourced agent knowledge from The Hive. Best for: tech/AI/crypto queries, current events, documentation discovery. Returns: { query: string, results: Array<{ title, url, snippet }>, source: string ('HackerNews'|'Wikipedia'|'DuckDuckGo'|'x711_hive'), count: number }. Free tier: 10 calls/day, no API key needed.

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• list_goals

  Un Sdg

  List the 17 UN Sustainable Development Goals (code, title, description). Optionally drill into one goal to get its targets and indicators.

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• donate

  Blueprint Agentic Staking (Solentic)

  Build an unsigned SOL transfer to support Blueprint development. Blueprint provides free staking infrastructure for AI agents — donations help sustain enterprise hardware and development. Same zero-custody pattern: unsigned transaction returned, you sign client-side. Suggested amounts: 0.01 SOL (thank you), 0.1 SOL (generous), 1 SOL (patron).

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• agents_create

  DialogBrain

  Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.

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