"Using Gemini for execution and Claude for problem-solving" matching MCP tools:

• assets.listA

  AI Design BlueprintA

  Public — list downloadable doctrine and agent asset artifacts (skill packs, rule packs, MCP setup snippets) the user can drop into their AI coding tool to import the Blueprint as native skill/rule files. Returns a list of assets with name, format (one of: zip / md / markdown / mdc / json / toml / text — the full vocabulary), pack_version, download_url, and platform target (Claude Code, Cursor, Codex, Gemini, Qwen). The response also carries `count` (length of `assets`) for symmetry with principles.list / clusters.list / guides.list. WHEN TO CALL: the user asks how to bring the Blueprint into their coding agent, or wants to install it as a local skill/rule file. WHEN NOT TO CALL: for the live MCP tools themselves — those are already available through this server. For doctrine content, prefer principles.list/get and guides.list/get. BEHAVIOR: read-only, idempotent, no auth required. Asset artefacts are regenerated on every deploy from the canonical doctrine.

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• recommend_frameworkA

  jeda-aiA

  Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

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• omnarai_councilA

  omnarai-mcpA

  Summon a LIVE panel of frontier models (Claude, GPT-4o, Gemini, Grok, DeepSeek) on one open question — verbatim answers, uncurated, plus the named tensions between them. Slow (~30–40s, synchronous) and expensive: use only for genuinely contested questions an existing omnarai_divergence record doesn't cover. Every run mints a new divergence record.

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• llmA

  VaayaA

  Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summarize a huge blob cheaply, or query a specific model the user names (Kimi, GPT, Gemini, Claude, DeepSeek, and 300+ more). `model` accepts 'auto' (default: short prompts go cheap, long go mid), 'cheap' | 'mid' | 'best' tiers, or any exact OpenRouter slug like 'moonshotai/kimi-k3'. Typical costs: cheap tier well under 0.1 cents, best tier 1-3 cents per call. Not for the conversation you are already having — it is a one-shot ask to another model.

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• ai_token_arbitrageA

  TrueCalciA

  Calculate multi-provider LLM API inference costs, prompt caching economics (up to 90% discount), batch discounts, and cost disparity across Claude 3.5 Sonnet, GPT-4o, DeepSeek V3/R1, and Gemini 1.5 Pro/Flash. Behavior: Deterministic, idempotent calculation with zero external side effects. Models official pricing cards per million input/output tokens. Incorporates prompt cache hit pricing reductions and asynchronous batch API discounts (50%). Returns comprehensive cost comparison matrix, cheapest model recommendation, cache savings, and cost multiples relative to the lowest-cost model. Usage Guidelines: Use when budgeting AI agent inference costs, evaluating LLM providers, or deciding whether to implement prompt caching. Do not use for general cloud bandwidth transfer costs; use cloud_egress_finops instead.

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• explain_greenfieldA

  SCModelingA

  Reference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.

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• misakanet_submit_intakeA

  MisakaNetA

  [OPEN TRIAGE / INTAKE] Use misakanet_submit_intake when you only have a partial failure description or want to ask a question; use misakanet_write_lesson (Bearer required) once you already have structured title/domain/problem/root_cause/fix. submit_intake is open, rate-limited, no Bearer — output is a GitHub issue (intake,mcp-intake,pending-review) for maintainer triage, NOT a merged lesson. Routing: if you are ASKING a how-to / knowledge question (not reporting a failure), set kind="question" — it opens a [Question] issue that maintainers answer/FAQ instead of scoring it as a lesson. If kind is omitted, the server auto-detects question-shaped content (no error/fix/verification + question phrasing). Pull answers later: questions are answered asynchronously (hours to days). Re-call this tool with the SAME problem text later — the dedup response returns the maintainer's answer once it exists ({answered:true, answer}); or re-run misakanet_search on the topic for FAQ hits. Returns: object {submitted: boolean, intake_id, status, redactions_applied, quality_score, receipt, routing:{kind, auto_detected}, follow_up?}; duplicates: {submitted: false, duplicate: true, previous_issue} or {answered: true, answer} for answered questions. Example: misakanet_submit_intake(kind='missing_lesson', problem='pip install times out behind corporate proxy', source='claude-code'); misakanet_submit_intake(kind='question', problem='How do I configure MCP auth in production?', source='claude-code')

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• get_setup_guideA

  SwapWizardA

  Returns the complete setup and usage guide for SwapWizard. Call this FIRST before using any other tool. Covers: required configuration (API key, Alchemy RPC URL, private key), how to use poolId correctly, step-by-step operational flows for swap/zap in/zap out/analyze, transaction execution details, and approval rules.

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• list_scenariosA

  QueueSimA

  List the four pre-built QueueSim scenarios. Returns key, title, and one-line description for each (Single Server, Coffee Shop, Grocery Checkout, Call Center). Call this when the user's problem matches one of the preset shapes — use describe_scenario for more detail and simulate_scenario to run one.

  ConnectorNo auth

• share_findingA

  redm-mcpA

  Share a verified finding back to the docs corpus so the next agent can find it. Use AFTER solving a non-trivial problem to record what would have saved you time: a gotcha, a working parameter combo, an undocumented constraint, a relationship between two natives that isn't obvious. Other agents will find this via `semantic_search` (findings are merged into default results; `category: 'learnings'` returns only findings). WHEN to use: - You burned multiple iterations on something not in the docs. - You discovered an undocumented quirk (param order, hash collision, framework export that isn't in `vorp`/`rsgcore`). - You verified that a specific combination works (e.g. native A + flag B for behavior C). WHEN NOT to use: - The information is already in the docs (verify with `semantic_search`/`grep_docs` first). - You're guessing — only contribute verified findings. - It's project-specific (your repo's auth flow, your DB schema). Keep it general to RedM/RDR3. Keep `title` short and searchable. `body` should explain WHY, not just WHAT — context, the trap, the fix.

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• assets.listA

  AI Design Blueprint DoctrineA

  Public — list downloadable doctrine and agent asset artifacts (skill packs, rule packs, MCP setup snippets) the user can drop into their AI coding tool to import the Blueprint as native skill/rule files. Returns a list of assets with name, format (one of: zip / md / markdown / mdc / json / toml / text — the full vocabulary), pack_version, download_url, and platform target (Claude Code, Cursor, Codex, Gemini, Qwen). The response also carries `count` (length of `assets`) for symmetry with principles.list / clusters.list / guides.list. WHEN TO CALL: the user asks how to bring the Blueprint into their coding agent, or wants to install it as a local skill/rule file. WHEN NOT TO CALL: for the live MCP tools themselves — those are already available through this server. For doctrine content, prefer principles.list/get and guides.list/get. BEHAVIOR: read-only, idempotent, no auth required. Asset artefacts are regenerated on every deploy from the canonical doctrine.

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• install_snippetsA

  Prism — Data Enrichment for AI AgentsA

  Return ready-to-paste configuration snippets for installing this MCP server in Claude Code, Cursor, Cline, Continue.dev, Windsurf, and Zed. Free.

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• get_iban_formatA

  ibanchecker-mcpA

  Return the IBAN format specification for a country, covering 90 supported IBAN-using countries. Returns JSON describing the country's total IBAN length, the BBAN layout (bank code, branch code, and account number positions and lengths), an example IBAN, and the SEPA-membership flag. Use this to understand or display how a country's IBAN is structured, to build input masks, or to explain a validation failure, not to validate a specific number (use `validate_iban` for that). An unsupported or unknown country code returns an error result describing the problem.

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• tvm_solveA

  mcpA

  Use this when you have four of the five time-value-of-money variables (N periods, I/Y annual rate percent, PV, PMT, FV) and need the fifth - annuity, loan, or investment problems - instead of solving the equation by hand. Solving for I/Y uses Newton-Raphson (no closed form). Supports compoundingPerYear and annuityDue (payments at the beginning of each period). Follows the cash-flow sign convention (outflows negative). Deterministic: same input, same output. Example: solveFor 'fv', n 120, iy 6, pv -10000, pmt -200, compoundingPerYear 12 -> result 50969.84. result holds the solved value; iy is rounded to 4 decimals, all others to 2.

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• projectile_motionA

  TrueCalciA

  Calculate 2D classical mechanics projectile kinematics: maximum trajectory apex height, horizontal flight range, total time of flight, and terminal impact velocity. Behavior: Deterministic, idempotent calculation with zero external side effects. Assumes vacuum projectile motion with constant gravitational acceleration: Flight Time t = (2 * v0 * sin(theta)) / g; Max Height H = (v0 * sin(theta))^2 / (2 * g); Range R = (v0^2 * sin(2*theta)) / g. Returns trajectory coordinates, apex coordinates, and velocity components (vx, vy). Usage Guidelines: Use for ballistic trajectories, physics problem solving, and aerospace launch kinematics without atmospheric drag. Do not use for orbital delta-v rocket staging; use rocket_deltav instead.

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• linear_regressionA

  TrueCalciA

  Compute Ordinary Least Squares (OLS) bivariate linear regression best-fit trend line (y = m*x + c), Pearson correlation coefficient (r), and coefficient of determination (R^2). Behavior: Deterministic, idempotent calculation with zero external side effects. Evaluates sample means, covariance, and variances to solve slope m = Cov(X,Y) / Var(X) and intercept c = mean(Y) - m*mean(X). Computes Pearson r, R^2, standard error of estimate, and generates predicted y-values for each input x. Usage Guidelines: Use for trend forecasting, scientific scatter data fitting, and correlation analysis. Do not use for solving analytical quadratic or linear systems; use casio_991_solve instead.

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• validate_problemA

  Moltline OptimizeA

  Check a problem's shape and obvious feasibility before spending solver time. FREE. Typical input {"type": "route", "problem": {"stops": [...], "matrix": [[...]], "vehicles": [...]}} returns {"ok": false, "issues": ["total demand 34 exceeds total capacity 30"], "size": {"stops": 14, "vehicles": 2}, "tier_hint": "route_plan_fleet (licence) - more than 12 stops"}. Types: route, pack, cut1d, cut2d, roster, knapsack; the problem object uses the same fields as the matching tool. Use first when an agent has assembled the problem from other data. Not a solve: it never calls the solver. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "type must be one of <value>"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• cc.external_signalA

  Central Command — x402 Trading IntelligenceA

  Call cc.external_signal — Public-facing webhook endpoint for TradingView alerts, custom bots, and third-party signal providers. Normalizes and forwards to execution. Purpose: Public-facing webhook endpoint for TradingView alerts, custom bots, and third-party signal providers. Normalizes and forwards to execution. Behavior: DESTRUCTIVE. Normalizes the signal and routes toward trade execution for the authenticated account. Can open/close positions. Auth: X-Api-Key required (and linked exchange credentials for execution actions). Cost: $0.005 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 60/min (per API key). Tier: standard. Returns: Proxied execution response: signal received, validated, normalized, and forwarded to trade engine. Guidelines: Use for research / signal context. Pair with cc.agent_strategy (paper) before any live order. Do not invent fills from this data alone. Tags: webhook, tradingview, alerts, automation, signals.

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  Destructive

  No auth

• count_tokensA

  inktomd MCP ServerA

  Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status

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• tengu_v3_cost_estimateA

  tengu-firmC

  Pre-trade expected execution cost for a ticker: spread, market impact, and commission for a given qty (default 100) and side (buy/sell). Call it to know what a trade will actually cost before sizing or routing it; use twap_plan/vwap_plan for the execution schedule itself.

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• setup_instructionsA

  Coal — Payments for AI agentsA

  Print step-by-step instructions for using Coal MCP from Claude / Cursor / any MCP client. Run this FIRST if you are unsure how to authenticate or which credentials to provide.

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