"Resources for Learning Python Coding" matching MCP tools:

• get_integration_guide

  TestMyVibes

  Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.

  Connector

• ateam_redeploy

  ateam-mcp

  Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).

  Connector

• 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.

  Connector

• wait_for_update

  cnvs.app

  Long-poll: blocks until the next edit lands on this board, then returns. WHEN TO CALL THIS: if your MCP client does NOT surface `notifications/resources/updated` events from `resources/subscribe` back to the model (most chat clients do not — they receive the SSE event but don't inject it into your context), this tool is how you 'wait for the human' inside a single turn. Typical flow: you draw / write what you were asked to, then instead of ending your turn you call `wait_for_update(board_id)`. When the human adds, moves, or erases something, the call returns and you refresh with `get_preview` / `get_board` and continue the collaboration. Great for turn-based interactions (games like tic-tac-toe, brainstorming where you respond to each sticky the user drops, sketch-and-feedback loops, etc.). If your client DOES deliver resource notifications natively, prefer `resources/subscribe` — it's cheaper and has no timeout ceiling. BEHAVIOUR: resolves ~3 s after the edit burst settles (same debounce as the push notifications — this is intentional so drags and long strokes collapse into one wake-up). Returns `{ updated: true, timedOut: false }` on a real edit, or `{ updated: false, timedOut: true }` if nothing happened within `timeout_ms`. On timeout, just call it again to keep waiting; chaining calls is cheap. `timeout_ms` is clamped to [1000, 55000]; default 25000 (leaves headroom under typical 60 s proxy timeouts).

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• about_emora

  mental-health-care

  Identity, services, states served, insurance accepted, age ranges, key facts, crisis resources, and links. Combined site-info + services catalog.

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• get_instagram_post

  Influship

  Fetch raw Instagram post-page data by shortcode. Use this when the user needs fresh raw Instagram post metadata that is not guaranteed on regular cached post-list endpoints yet, including coauthors, tagged users, paid partnership metadata, product mentions, music attribution, location, display resources, and video versions.

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• search_stories

  AI Success Story

  Consult prior incidents from other AI coding sessions for a transferable pattern relevant to your situation. The corpus is first-person war-stories ('I was given X, tried Y, noticed Z, here's why it worked') on deploy, debugging, code review, refactoring, framework decisions. Reach for this BEFORE falling back on training — real incidents catch gotchas parametric knowledge misses. Returns ranked matches with 'why_relevant' snippets; follow up with fetch_story.

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• vest_search_tools

  vest-mcp

  Search and browse AI tools available in Vest's cashback catalog. Returns names, slugs, categories, and live cashback rates. Use when the user asks what tools are available, wants to compare options, or needs a slug for vest_get_signup_link. Real triggers: 'what AI writing tools does Vest have?', 'show me coding tools with high cashback', 'find tools under $50/mo'. Do NOT use when the user describes a goal or mission — use vest_build_stack instead. Do NOT use to get a signup link — use vest_get_signup_link.

  Connector

• lookup_authoring_skill

  kernelcad

  Return the kernelcad-authoring SKILL.md body — conventions for writing .kcad.ts scripts (imports, parameters, evaluation contract, common pitfalls). Use this tool BEFORE generating CAD code if your MCP client does not list resources. Clients that do list resources should instead read `kernelcad://skills/authoring` directly — the contents are identical. INPUT: none. OUTPUT: { uri, mimeType, text } where `text` is the SKILL.md body.

  Connector

• get_compression_candidates

  Push Realm

  Find clusters of related learnings that are ripe for compression. When many similar solutions get linked together (e.g., 10+ 'relates_to' entries about the same issue), they clutter search results and waste agent time. Use this tool to discover clusters that could be compressed into a single consolidated learning. WORKFLOW: 1. Call get_compression_candidates with min_cluster_size=3 (or higher) 2. Review the returned clusters - each has full content for every learning 3. Synthesize a compressed version: one clear (Issue) section plus agent-specific nuances (grok adds X, claude adds Y) 4. Call compress_learnings with the learning_ids, new title, and synthesized content 5. Show preview to user, then confirm_compression on approval Only use when you've seen or been asked about compressing duplicate/similar solutions.

  Connector

• get_instagram_post

  Influship Influencer Marketing MCP

  Fetch raw Instagram post-page data by shortcode. Use this when the user needs fresh raw Instagram post metadata that is not guaranteed on regular cached post-list endpoints yet, including coauthors, tagged users, paid partnership metadata, product mentions, music attribution, location, display resources, and video versions.

  Connector

• generate_scaffold

  Senzing

  Generate SDK scaffold code for common workflows. Returns real, indexed code snippets from GitHub with source URLs for provenance. Use this INSTEAD of hand-coding SDK calls — hand-coded Senzing SDK usage commonly gets method names wrong across v3/v4 (e.g., close_export vs close_export_report, init vs initialize, whyEntityByEntityID vs why_entities) and misses required initialization steps. Languages: python, java, csharp, rust. Workflows: initialize, configure, add_records, delete, query, redo, stewardship, information, full_pipeline (aliases accepted: init, config, ingest, remove, search, redoer, force_resolve, info, e2e). V3 supports Python and Java only. Returns GitHub raw URLs — fetch each snippet to read the source code.

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• usaspending_get_federal_account

  usaspending-mcp-server

  Fetch a federal account's budget data: total obligations, gross outlays, and budgetary resources. Federal accounts connect appropriations law to actual agency spending. Account codes appear in usaspending_get_award account_obligations_by_defc field and are formatted as AGENCY-MAIN (e.g., 097-0100 for DoD Operation and Maintenance). Returns account metadata and current fiscal year financial totals.

  Connector

• link_learnings

  Push Realm

  Create a relationship between two learnings. Use 'relates_to' when learnings are genuinely distinct but connected — different error, different root cause, different package. Do NOT use for the same problem with a slightly different description; if the core issue is the same, use suggest_edit instead. Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link 'relates_to' • A learning mentions another as context for a different problem → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.

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• get_site_status

  BorealHost

  Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this account

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• build_permission_transaction

  powersun-tron-mcp

  Build an AccountPermissionUpdate transaction that grants the PowerSun platform permission to delegate/undelegate resources and optionally vote on your behalf. Returns an unsigned transaction that you must sign with your private key and then broadcast using broadcast_signed_permission_tx. All existing account permissions are preserved. Requires authentication.

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• compress_learnings

  Push Realm

  Propose compressing multiple related learnings into one consolidated learning. Call this AFTER get_compression_candidates and synthesizing the compressed content. Same approval flow as submit_learning: show preview to user, then confirm_compression on approval or reject_compression on decline. Write a synthesised structured learning: • problem — best single problem statement across the cluster • cause — common root cause if one exists (optional) • solution — consolidated fix • notes — model-specific nuances (e.g. grok adds X, claude adds Y)

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• get_free_resources

  Makuri Showcase (CogniLedger)

  Returns free Makuri resources accessible without registration: Slovarik Romanian vocabulary issues and the Romanian level test. Use this when a user asks about free Romanian learning materials, language level tests, or how to try Makuri without signing up.

  Connector

• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

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• chieflab_get_users_after_build

  ChiefLab

  Outcome-named alias for chiefmo_launch_product. USE WHEN a coding agent just built, shipped, or deployed something and the user asks 'get users', 'find customers', 'make money from this', 'get paying users', 'get eyes on this', 'what now?', or 'how do I launch this?' This is the agent-dependency tool: after build, call this to turn the product into a repo-grounded money/distribution loop with launchPack, signed reviewUrl, approval-gated publish/send actions, 24h measurement, memory, next move, moneyLoop, and agentDependency. Gather repoContext first. Route exactly like chiefmo_launch_product; do not use chiefmo_diagnose_marketing for a new product.

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