"Multi MCP (Model Context Protocol) implementation or management" matching MCP tools:

• analyze_company

  Nordic Financial MCP

  AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'

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• get_adoption_stage

  Stratalize Governance

  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.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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• list_paired_models

  ReliaStats

  Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.

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• company_search

  Drillr — The financial MCP for AI agents

  Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Pass a natural language description (e.g. "EV battery suppliers to Tesla", "Japanese semiconductor equipment makers", "AI inference chip startups"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

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• fetch

  Penfield Memory

  [ChatGPT Connector compat] Fetch memory by ID. Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool contract. Native MCP clients should fetch via `recall` + memory_id, or use the API's GET /memories/{id} endpoint directly. Returns a single memory with citation support (id, title, url, text fields). Args: id: Memory UUID to fetch ctx: MCP context Returns: Dict with id, title, url, text, metadata fields

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• pay_invoice

  MERX - TRON Resource Exchange

  Pay an x402 invoice by signing and broadcasting a TRX transfer to the invoice address, then verifying the payment with the facilitator. x402 (Coinbase + Cloudflare HTTP 402 standard) is the protocol AI agents use to pay APIs per call. Use this when you receive an invoice_id from a paywalled service or another agent. REQUIRES: TRON_PRIVATE_KEY in env (use set_private_key first) AND a valid invoice_id from create_invoice or x402 challenge response. The transfer is signed locally — your private key never leaves the MCP process.

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• get_protocol_info

  intelligence-mcp

  Get the canonical description of an agent payment protocol including creator, maturity level, repo URL, and what layer it operates at (authorization, commerce, or settlement). Use when the user asks about a specific protocol ('what is AP2?', 'who created MPP?', 'is x402 production ready?', 'what layer does ACP operate at?'). Use compare_protocols instead when comparing multiple protocols against each other.

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• compare_protocols

  Intelligence MCP

  Get a side-by-side comparison matrix of all five agent payment protocols (AP2, ACP, x402, MPP, UCP) across creator, layer, agent delegation, budget limits, cross-merchant coordination, and MCP integration. Use when the user asks to compare protocols ('AP2 vs ACP', 'which protocol handles budgets?', 'what's the difference between x402 and MPP?', 'show me the landscape'). Use get_protocol_info instead for deep details on a single protocol.

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• compare_protocols

  intelligence-mcp

  Get a side-by-side comparison matrix of all five agent payment protocols (AP2, ACP, x402, MPP, UCP) across creator, layer, agent delegation, budget limits, cross-merchant coordination, and MCP integration. Use when the user asks to compare protocols ('AP2 vs ACP', 'which protocol handles budgets?', 'what's the difference between x402 and MPP?', 'show me the landscape'). Use get_protocol_info instead for deep details on a single protocol.

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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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• audit_comp_live_listings

  CIPHER x402 — Paid Solana & Crypto Tools

  Return a curated snapshot of currently-live audit competitions and bug-bounty programs across Code4rena, Cantina, Sherlock, and direct-protocol channels. Useful for solo wardens triaging which contests to enter. Snapshot updates with each cipher-x402-mcp release; treat the data as a hint, always cross-check the platform before submitting. Free, no payment required.

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• info_platformmetrics_get_platform_info

  Gate Info MCP

  One-shot protocol profile by name and scope. scope=full adds competition_metrics{} for CEX venues (spot/derivs/depth/OI core+extended/PoR). Set include_oi_symbol_detail=true with oi_symbol_limit (1-100, default 20) for top-N OI breakdown. Ranked multi-protocol list→search_platforms. Daily time series→get_platform_history.

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• get_protocol_info

  Intelligence MCP

  Get the canonical description of an agent payment protocol including creator, maturity level, repo URL, and what layer it operates at (authorization, commerce, or settlement). Use when the user asks about a specific protocol ('what is AP2?', 'who created MPP?', 'is x402 production ready?', 'what layer does ACP operate at?'). Use compare_protocols instead when comparing multiple protocols against each other.

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• demand.create_want

  iwant.fyi - demand-side commerce

  Record the user's purchase intent and get back ranked, matched supply in the SAME call. Use this when the user DECIDES to buy, or wants the request kept open with notify-on-new-supply (a standing want); for just finding or comparing products without committing, use demand.search instead. Matching is category-agnostic (any goods/services/other) and respects your constraints -- send `constraints.rules` and a condition floor or per-field specs are ENFORCED (supply that cannot satisfy them is filtered out). Returns matches ranked across every source by one unified relevance pass, each carrying normalized specs (brand, model, GTIN, quantity, condition) so you have structured fields to reason over. Report what the user does next via demand.record_outcome. iwant.fyi demand-side protocol v1.0 §8.1; spec at https://iwant.fyi/protocol/v1.

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• demand.list_verticals

  iwant.fyi - demand-side commerce

  iwant.fyi demand-side protocol v1.0 §8.2: list the verticals this Implementation supports, with descriptions and supported spec keys. Useful for agent capability discovery.

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• set_ens_records

  Name Whisper — ENS Intelligence Layer

  Set ENS resolver records for a name you own. Returns encoded transaction calldata ready to sign and broadcast. Supports address records (ETH, BTC, SOL, etc.), text records (avatar, description, url, social handles, AI agent metadata), content hash (IPFS/IPNS), ENSIP-25 agent-registration records, and ENSIP-26 agent context and endpoint discovery. Multiple records are batched into a single multicall transaction to save gas. Common text record keys: avatar, description, url, email, com.twitter, com.github, com.discord, ai.agent, ai.purpose, ai.capabilities, ai.category. ENSIP-25 support: Pass agentRegistration with registryAddress and agentId to automatically set the standardized agent-registration text record. This creates a verifiable on-chain binding between your ENS name and your agent identity in an ERC-8004 registry. ENSIP-26 support: Pass agentContext to set the agent-context text record (free-form agent description). Pass agentEndpoints with protocol URLs (mcp, a2a, oasf, web) to set agent-endpoint[protocol] discovery records. The returned transaction can be signed and submitted directly using any wallet framework (Coinbase AgentKit, ethers.js, etc.).

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• report_incident

  tickerr

  Report an LLM API failure or successful recovery your agent just experienced. Call this automatically before retrying any 5xx error or timeout from OpenAI, Anthropic, Google, xAI, Deepseek, Mistral, Groq, Cohere, Perplexity, Together, or Fireworks APIs. Also call this when a previously failing model succeeds again - set is_resolution_report: true to help other agents know the outage is over. Returns live signal from other agents: how many reported the same issue, current incident state, and which model to fall back to. Usage terms: Tickerr MCP is free. In return, when your agent experiences LLM API failures, this tool runs automatically (max once per hour per model). Reports are anonymous - provider, model, error code, latency only. No request content. No personal data. Aggregated signal published at tickerr.ai/agent-reports Using this tool constitutes consent to this data exchange for 7 days from last Tickerr tool use. To opt out at any time: tickerr.ai/mcp/opt-out

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• get_adoption_stage

  Stratalize Healthcare

  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.

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• workspace_current

  DialogBrain

  Return the workspace this MCP API key is currently routed to, with the caller's role inside it. Use this to confirm context before/after `workspace.switch`.

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