"Learning to Create an Expert Python Program to Exit GitHub" matching MCP tools:

• find_examples

  Senzing

  Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.

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• contacter_expert

  payotte-mcp

  Call this ONLY when the user explicitly asks to contact, reach out to, or request a quote/appointment from a Payotte-listed professional. DOUBLE OPT-IN: this tool does NOT email the expert directly — it sends a confirmation link to the USER’s email, and the request reaches the expert only after the user clicks it (link valid 48 h). Tell the user to check their inbox. BEFORE calling: (1) show which expert will be contacted (use trouver_expert first if needed), (2) collect their name, email and message, (3) get their explicit approval — then set consentement=true. Never invent contact details. The expert replies directly to the user; Payotte keeps no copy of the content.

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• get_strategy_code

  quantifyme

  Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.

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• ctrl_create_workflow

  CTRL

  Create a CTRL workflow draft. ONE trigger + an ordered chain of up to 20 actions/conditions/utilities. Returns { workflowId, activateUrl }. Pass `targetChain` to pick which chain the workflow runs on — "base" (default, launchpads + Aerodrome + UniV4) or "ethereum" (UniV3 only, no launchpads, no clanker/zora). CRITICAL: call ctrl_get_block_catalog FIRST (with the same `chain` value) to discover field names — every key in trigger.config and chain[].config must exactly match catalog fields[].key. Populate EVERY field the user expressed intent for. For pool.created (Token Launch, Base-only) set launchpad (e.g. ["bankr"]), keywordIncludes ("ai,agent,claw"), keywordMatchMode "any", keywordCategories (["ai_agents"]), safetyEnabled true, safetyRejectHoneypot true, safetyMinScore 50. For cypher.swap set tokenIn ("ETH"), tokenOut ("{{trigger.tokenAddress}}"), tokenOutMode "dynamic", amount (ETH units, e.g. 0.005 — ASK USER if not specified), slippage (15 for snipes), and autoSell* if user wants an exit (autoSellEnabled true, autoSellMode "multiple", autoSellMultiplier 2, autoSellPercent 100, autoSellReceiveToken "USDC"). For notify.telegram set message with {{token}}/{{amount}}/{{txHash}} placeholders. Interview the user for missing critical fields (amount, exit strategy, keywords) — do not silently default.

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• handoffs.agency

  AI Design Blueprint

  Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer <token> (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.

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• list_user_lp_positions

  SwapWizard

  Maps to GET /positions. Reads all LP positions a wallet holds on a given chain by calling the SwapWizard API, which discovers positions across all supported protocols: Uniswap V2/V3/V4, Aerodrome, Thena, SushiSwap, PancakeSwap, Algebra, Balancer, Curve, and all Solidly forks. Each position includes positionId, nftManager, dexName, liquidityKind, token addresses, amounts, fees, in-range status, APR, and USD values. EXIT SIGNAL: each position also carries `momentumSignal` for its pool — "exit" means the pool's volume is dying or LPs are leaving (consider zapping out), "watch"/"entry" mean momentum is still alive. When `momentumSignal` is ABSENT, the pool has dropped out of the momentum ranking (momentum exhausted) — also a reason to review and likely exit the position. `momentumScore` is the composite strength. Use this to drive exit decisions, mirroring the entry signal from search_liquidity_pools. The API uses Alchemy's NFT APIs for optimal position discovery — pass an Alchemy RPC URL via rpcUrl for fastest results. Without an Alchemy key, the API falls back to on-chain scanning which may be slower and newly created positions may take longer to appear. IMPORTANT: Always call this BEFORE zap_out_of_lp_position — pass the returned positionId, nftManager, dexName, and liquidityKind directly to zap_out_of_lp_position.

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• load_technical_workflow

  Shibui Finance

  Load technical workflow for RSI, MACD, SMA, Bollinger Bands, entry/exit. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about RSI, MACD, moving averages, Bollinger Bands, support/resistance, overbought/oversold, momentum, trend, chart patterns, golden cross, entry/exit signals, or "is X oversold/overbought". Can be combined with other workflow tools.

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• search_golf_programs

  PGA Golf

  Search for upcoming public PGA Coach programs (clinics, camps, classes, and other group events) near a location (required). Use this tool when the user asks to find programs, clinics, camps, classes, or group events — not private one-on-one lessons. For private lesson coaches, use `search_golf_coaches` instead. Internal record identifiers (for example program `id` and coach `slug`) are for tool calls only and must never be shown or mentioned to the user. - Search results include program details plus the hosting coach and facility for each program. - The response also includes top-level `coaches` and `facilities` arrays summarizing the unique coaches and venues in the result set — always present those to the user. - Results are ordered by program start instant and filtered to the requested radius. - Discovery uses nearby PGA Coach profiles (nearest 20 in the radius), then loads up to 30 upcoming public programs per coach in a 180-day window in one batched GraphQL request. Facility coordinates still have to fall inside the radius. - Results are paginated. The pagination cursor carries the remaining nearby programs so additional pages do not repeat GraphQL discovery. Reuse that cursor with the original location and radius. - Direct golfers to the program URL to view details and register. Coach profile and booking URLs are also included when the golfer wants to learn more about the coach.

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• revert_to_version

  AIOProductOS MCP

  Restore an earlier artifact version (F5) to current and return the now-current version: the existing current version is flipped to 'reverted' (kept for the learning signal) and the chosen version becomes current again. A write — not idempotent, since re-running reverts again. version_id is the version you want to RESTORE; get it from list_artifact_versions and never guess it. Optional reason is recorded.

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• list_expert_profiles

  Taste

  Returns currently-available expert taglines (pseudonymous descriptions of the kinds of expertise on hand) plus the real-time count of online expert seats and estimated wait. Use this as a cheap pre-flight check before calling a paid tool. Taglines describe expertise kinds, not individuals: no per-expert PII is exposed. Free.

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• search_docs

  Devdocs Io

  Find which documentation SETS exist whose NAME matches a substring (e.g. "python" → Python 3.x, "react" → React). Returns doc SETS, NOT their content — this does NOT look up a function/method/API name. To search inside a doc for an entry like "Array.map" or "fetch", use search_index (slug + query).

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• liquidity_exit_cost

  AgentData - Executable Liquidity / Exit-Cost

  Size-aware exit cost, depeg risk, and liquidity fragility for a token on Base. Given a token and a sell size, returns the realized cost of exiting (price impact + fee, in bps and units), the best route across venues, an aggregated fragility score, and — for pegged assets — a depeg-risk score. Computed deterministically from on-chain AMM state, so the result is reproducible and auditable. Three tiers by compute depth (default 'risk'): 'quote' = best-route exit cost for one size; 'risk' = exit cost + fragility (+ depeg for pegged tokens); 'deep' = adds a multi-size exit-cost curve, max-size-before-cost thresholds, and an internal cross-check. Pricing per call (USDC): quote = $0, risk = $0, deep = $0. Status: testnet/preview — payment runs over the x402 HTTP surface, not this tool.

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• list_domains

  mcp-server

  List the taxonomy domains the company has indexed — with document counts, expert counts, and coverage levels — so an agent can decide whether to query before spending a Knowledge Token. Returns one row per domain with the canonical `taxonomy_domain` slug, document/chunk counts, expert count, coverage level (expert | partial | none), the single_expert risk flag, and the top contributor by authority. Use the slug as the `domain` filter on a follow-up `query_knowledge` call. Zero Knowledge Tokens consumed.

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• generate_ci_workflow

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA. Two gate types, combinable: "eval_contract" runs a .ia-eval.yaml through ia-qa-com/eval-action@v1 (LLM quality gate, needs a provider API key as a repo secret), and "cli_checks" runs deterministic primitives via npx @ia-qa/cli (secret scan, prompt-injection scan, security headers…) whose exit code fails the build. Deterministic template — no LLM call, no API key, same inputs give the same file. Returns the YAML, the secrets to create, and the remaining steps. Pair with generate_eval_yaml to produce the contract itself.

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• nifra_learn

  Nifra docs

  Walk the guided path to build a nifra app end to end - an ORDERED sequence (create → page route → loader → typed API → typed client → auth → background jobs → deploy), not the random-access search of nifra_docs/nifra_example. Call with no args for the step index; pass `step: N` for that step's goal, how to do it (which tool emits the correct artifact), and how to verify it. Use it when scaffolding a new app or learning nifra's flow - each step composes the other nifra_* tools.

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• exit_capacity

  ensotrade

  How fast a crypto position can be exited and what it costs. Use for "how long to get out of $5M of SOL", "can I exit this position", "what's my liquidity risk". Returns: immediate exit cost by walking the live book; direction skew (up-cost vs down-cost, since a lopsided book matters); time to exit at 5%/10%/20% participation; max exitable in 24h; and how concentrated the depth is on one venue. IMPORTANT when quoting the capacity numbers: they are computed on REPORTED venue volume, which is an UPPER BOUND on genuinely tradable capacity — say so, and treat the time-to-exit figures as optimistic. An "effective volume" adjustment was built and deliberately NOT shipped: comparing how much volume each venue needs to move price one bp collapses into a pure size proxy, because arbitrage equalises price travel across venues. The response carries a live degeneracy_check showing this for the requested asset. Do not present the capacity figures as adjusted or verified volume. `coin` = ticker e.g. 'sol'. `position_usd` = position size in USD. Requires an EnsoTrade Pro API key.

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• cap_table_simulate_cap_table_exit

  Lovie Company Formation

  Computes what every stakeholder takes home at ONE hypothetical exit valuation, running the liquidation-preference waterfall and converting SAFEs as if the exit were the price round. Read-only — nothing is written. When the user asks to "run a simulation" without naming a number, do not pick round numbers out of the air: read the company's own valuations first (GetListCapTableRounds for pre/post-money, GetListCapTableSecurities for SAFE valuation caps) and call this once per valuation, plus one clearly below the cheapest and one or two well above the richest, so the ladder shows both a down-round and an upside. Keep it to about five valuations. Report each scenario as: the exit value, the total distributed, and one line per investor with the payout and the cash-out multiple (payout / capital invested, e.g. "20.0x"); call out anyone crossing $1M and $30M, and say plainly when an exit below the last round means liquidation preferences are repaid before common stock. Never hand-calculate any of these numbers.

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• create_project

  Keelen

  Create a brand-new from-scratch project (no Chrome onboarding needed). Use this ONLY when the user wants a NEW repo scaffolded. If they already have a repo, use import_project(repo_full_name) instead — this tool would create a second, empty one beside theirs (list_github_repos() browses what the workspace can see). Scaffolds a new GitHub repo, a bootstrap-mode project, and submits `build_description` as the project's first Roadmap Request. `name` is a concise GitHub short repo slug (no owner); `project_kind` is REQUIRED and one of library | node_library | python_library | service | cli | web_app | godot_game | roblox_game; `preview_command` is required iff `project_kind == 'web_app'`. `engine` is OPTIONAL — one of claude_code | codex | glm | kimi (defaults to claude_code); codex, glm, and kimi require the workspace to have a matching connected credential. `org` is OPTIONAL — a GitHub organization login to create the repo inside (e.g. your company org); omit it to land the repo on a member's personal account. `private` defaults to True. `ci_runs_on` is OPTIONAL — the CI runner labels for the scaffolded workflow, e.g. ["self-hosted", "linux", "x64", "my-fleet"]. Omit it to inherit the workspace default (ubuntu-latest if unset). Labels no registered org runner carries are rejected, because GitHub would queue such a job forever rather than fail it. `framework` is OPTIONAL and `web_app`-only — one of vite | next (defaults to vite). It picks the scaffolded frontend rails: `vite` a vanilla-TypeScript SPA, `next` a Next.js app-router app. Passing it with any other `project_kind` is an error. The repo is created on the GitHub account of a workspace member with repo-create OAuth access (this path has no specific caller user), so the returned `repo` owner is whichever member's token resolved (or the chosen `org`). If no member has repo-create access — or the resolving member can't create in `org` — the call returns an actionable error. Returns {project_id, repo, thread_id, next_action, poll_after_seconds, next_step}; follow next_step (poll get_request_status with the returned thread_id). On the rare arm where the first Request failed to submit, next_action is "call_tool" with next_tool="submit_request".

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• load_technical_workflow

  Shibui Finance

  Load technical workflow for RSI, MACD, SMA, Bollinger Bands, entry/exit. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about RSI, MACD, moving averages, Bollinger Bands, support/resistance, overbought/oversold, momentum, trend, chart patterns, golden cross, entry/exit signals, or "is X oversold/overbought". Can be combined with other workflow tools.

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• handoffs.agency

  AI Design Blueprint Doctrine

  Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer <token> (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.

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