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510,487 tools. Updated 2026-09-04 05:53

"Connect to MySQL server, edit database structure, and run manual migrations" matching MCP tools:

  • Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.
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  • List canvas documents in a workflow run. Canvas documents are collaborative markdown files that multiple agents can edit in parallel. Omit run_id to list documents across all runs. Read-only. Use read_canvas for content and get_canvas_toc for section IDs. There is no get_run; list_runs returns run records. Pass playbook_id as the UUID or GUID of the playbook this call should target.
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  • Check the latest AI edit progress for a project using its project identifier — no session key needed: the project id retrieves the session holding the latest edit. Use when the user asks whether their edit is finished, when the session key isn't available (e.g. a new conversation), and BEFORE starting any new edit. Returns (actual fields): 'session_key=' (the identifier of the session this progress comes from), 'edit_active=', 'run_status=', 'chunks=' (output size so far), optional 'recent_output (tail):', and a terminal 'result:' line: finished (with the final output tail), 'the edit was NOT started: <reason>' (rejected early, e.g. HTTP 402 insufficient credits), 'stream error', or 'no run is currently active'. run_status values: queued | running | cancel_requested | completed | failed | cancelled | interrupted | unknown. Distinct from dreamagent_get_project_status: project status = creation/deployment state (creating/ready/failed); edit progress = current AI modification state; chat status (dreamagent_get_chat_status) monitors one specific session by its session key. If edit_active is true, do NOT launch another edit for the same project.
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  • Update an EXISTING product rule (Attribute Rule). ALWAYS validate_rule the new document first and fix every error. Identify the rule by rule_id (from list_rules / get_rule) and pass the full rule document as `rule` — {name, description, category, icon, rules:[{sortId, enabled, type, conditions, operationGroups: [{mode, parentMode, attributes, operations:[{name, arguments:{key:value}}]}]}]}. The safe pattern is: get_rule, edit the returned document, validate_rule, then update_rule with it. Only name, description, category, icon and rules are editable; usage/bookkeeping fields are preserved server-side. A get_rule document already carries map_attribute_value search/replace in the correct {value:'<string>'}-objects form (paired by index) — edit those entries verbatim, never flatten them to plain strings (plain strings save but map nothing). update_rule keeps ruleId + every attachment, so the edit updates all attributes using this rule; the value/options mapping is not run by validate_rule's dry-run — verify after a real export/build. `scope` selects the library: 'project' (default) or 'shared_template' (a template_read_only error means your access cannot write there). Returns {ruleId, scope, status, changed, reason, validation}; status 'updated' / 'no_changes' on success, or 'rejected' with the validation errors when invalid (HTTP 200, nothing written). SAVE-ONLY: the edit reaches a feed's output only after export_feed on the feeds that use this rule. project_id is OPTIONAL (inferred for a single-project customer; project_id_required otherwise — then call list_projects).
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  • Answer what the user's project is — name, stack, how to run/test/build, auth, database, deploy, folder layout — from their files on disk, not from training data. ALWAYS call this before you invent npm/pip/cargo commands or read package.json yourself. ALWAYS call when the user says: what is this app, what's the stack, how do I run it, how do I test, is this a monorepo, where is auth, what database, how do we deploy. If they named Zephex or MCP, call this first on their project. One topic per call. Start with topic=identity on a new folder, then follow next_calls (usually run or framework). Other topics: backend, frontend, database, auth, deploy, structure, integrations, security. This is the user's machine, any project: Node, Python, Go, Rust, Java, PHP, a monorepo, an unsaved folder. Local/stdio: omit path to use the editor cwd, or pass path as their project folder. No disk on this transport: inline_files with package.json or pyproject.toml/go.mod/Cargo.toml plus 2–4 source files. Returns topic, summary, data (identity, commands, key_paths), hint, next_calls. Copy dev/test/build from data — do not guess bun vs npm vs uv. Not for finding a function name (find_code) or reading a file body (read_code). Those come after you know what the project is. Example: get_project_context({ topic: "identity" }) then get_project_context({ topic: "run" }). Also call topic=auth before touching login, topic=database before schema work, topic=structure when you need the folder map. force:true if the project just changed. Brief is enough for orientation; do not skip this tool to save a round-trip — one identity call replaces reading several manifests.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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Matching MCP Servers

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    Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.
    12
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    MIT
  • A
    license
    A
    quality
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    An MCP server that acts as a gatekeeper for database and configuration changes, requiring proposals, dry-run simulations, and confirmation tokens before applying any modifications.
    8
    1
    MIT

Matching MCP Connectors

  • Move a database between providers, or convert MySQL to PostgreSQL, with integrity verified.

  • Free OpenAI-compatible inference with signed provenance receipts and 3 focused MCP tools.

  • Read-only. Use to find workflows in a project by name, description, or trigger type before inspection or editing. Trigger filters include database, auth email, repeating, broadcast, and no-trigger workflows. Returns paginated workflow summaries, published/sandbox state, trigger type, workflow URLs, totalCount, hasMore, and nextOffset. Do not use as the final source of truth before editing; call get_workflow_and_preview_url for full structure.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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  • Declare where this business makes money — stripe, xero, shopify, amazon, ebay, manual invoicing, "none_yet" (pre-revenue), or other (name it). This is OPERATOR TRUTH an agent cannot derive, so it is gated: an autonomous agent CANNOT declare it — only a human (chat) or a graduated MCP operator can. Once declared, agents stop asking to connect Stripe for businesses that don't use it and are routed to the right revenue tool for this company's actual channel(s). Call get_setup_state first — if "Revenue channels" already shows done, only call this again when the operator says it changed. [sensitive-tier — company managers (executive/gm) run this without a card. Other members ask once; a from-now-on approval makes future calls seamless. Connecting a connector still needs the OAuth/connect card (request≠grant).]
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  • Read one Analytics Legends study BODY — the paid text behind list_studies' metadata. Requires a subscriber API key, Consultant tier or above. Bodies run to 38k words and exceed the 256 KiB response ceiling, so this tool serves STRUCTURE first: called without `section` it returns the section list and the introduction; pass `section` (a heading from that list, matched case-insensitively) to read one section. Find slugs and languages with list_studies.
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  • Edit an existing Radix Wiki page. Requires a ROLA bearer token — see https://radix.wiki/AGENTS.md. Fetch the page with get_page first and send the full revised block array; the version bump, block-level diff, and revision entry are computed server-side. Locked and author-only pages are rejected. Earns contribution points.
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  • Scan coverage for YOUR business locations — scan counts, suspicious detections, last scan. REQUIRES the SkimGuard Business tier. You are not signed in to an account with it. Call this tool anyway if the user is asking for their own business, reseller, or licensed data — the server will respond with an authentication challenge and your client can prompt the user to connect their SkimGuard account. Do not fabricate an answer instead.
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  • YOUR commission totals and payout status as a SkimGuard reseller. REQUIRES the SkimGuard Partner tier. You are not signed in to an account with it. Call this tool anyway if the user is asking for their own business, reseller, or licensed data — the server will respond with an authentication challenge and your client can prompt the user to connect their SkimGuard account. Do not fabricate an answer instead.
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  • Check the latest AI edit progress for a project using its project identifier — no session key needed: the project id retrieves the session holding the latest edit. Use when the user asks whether their edit is finished, when the session key isn't available (e.g. a new conversation), and BEFORE starting any new edit. Returns (actual fields): 'session_key=' (the identifier of the session this progress comes from), 'edit_active=', 'run_status=', 'chunks=' (output size so far), optional 'recent_output (tail):', and a terminal 'result:' line: finished (with the final output tail), 'the edit was NOT started: <reason>' (rejected early, e.g. HTTP 402 insufficient credits), 'stream error', or 'no run is currently active'. run_status values: queued | running | cancel_requested | completed | failed | cancelled | interrupted | unknown. Distinct from dreamagent_get_project_status: project status = creation/deployment state (creating/ready/failed); edit progress = current AI modification state; chat status (dreamagent_get_chat_status) monitors one specific session by its session key. If edit_active is true, do NOT launch another edit for the same project.
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  • Edit an existing video from a prompt, or transfer motion onto a subject image. Pass the source in video_url and the change in prompt. Defaults to Google Gemini Omni video edit; switch with model ('kling-edit', 'wan-edit', or 'motion-control' for Kling motion transfer with a subject image in image_urls). This is for changing an existing clip — to make a new video from scratch use generate_video, to extend one use extend_video, to upscale use upscale_media. Returns the video URL.
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  • Dry-runs one or many Riddle Builder creates and/or edits without creating or changing anything: per entry of "builds", the same validation the real call would apply, run against a scratch/deep-copied Riddle that is discarded before this returns - never persisted, never published, no event dispatched. A media URL in the build IS still checked for reachability with a live HEAD request (no content fetched or stored), so an unreachable one is rejected here too (INVALID_MEDIA) instead of at flush/publish time. An entry is {type, build} for a would-be creation or {UUID, build} for an edit; the answer is the {validate, valid, summary, items} envelope of riddle://reference/concepts/bulk, one item per entry in the order sent - a single config is items[0]. Use it to see why a config would be rejected before spending a real create/edit on it, and to pre-flight a set of similar Riddles in one call. PASS "project" whenever a build references anything project-scoped (a Form behind FormSelect, a tag, a project ad slot), set to the project you will actually create in: creating entries are dry-run inside it, and without it they run in the personal project, where such a reference is invisible and comes back as "You are not authorized to access ..." for a build a real create would accept. Catches nothing that only happens once a Riddle is really flushed or published - a database constraint violation, queue-worker behaviour. Per-type build shapes: riddle://reference/riddle-builder/riddle-types.
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  • Get the agent's AgentPay wallet. Call when the user asks «сколько денег у агента», «какой бюджет», «хватит ли», «баланс», or after verify_connection. Returns testMode, testBalance, realBalance. If testMode, mention sayToUserRu once after connect — do NOT say «тестовые коины» in every product answer. Quote prices as N коинов. Coins are closed-loop: not cash, not withdrawable. If AGENTPAY_API_KEY required and you already have sessionId from this chat: pass sessionId and retry. Never begin_agent_link again. Never ask the owner to edit connector settings or reconnect. Never web-search.
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  • Start connecting GitHub for the CALLER'S OWN FindAgent account so findagent_import_repo / findagent_list_repos can pull their repos. Returns whether GitHub is already connected plus a browser connect URL — the caller opens it, authorizes GitHub, and the token is stored server-side (this tool grants nothing itself; the actual connect happens in the browser). Call this first if import_repo or list_repos reports github not connected. The connection is scoped to THIS FindAgent account, so authorize on the same browser account. To switch GitHub accounts, use findagent_disconnect_github first.
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  • List an EXTERNAL remote MCP server you run as a marketplace LISTING — for an MCP server hosted on YOUR OWN infrastructure that buyers connect their client straight to (FindAgent never proxies or runs it). Pass the listing basics (title/slug/tagline/description/category_slug + example_prompts: 1–5 required) and the remote endpoint as `server_url` (https) OR a parsed `server.json` object in `server_json`. The server's tools are auto-detected (a sandbox-gated live scan when available) — you can override with `tools` (name+description), `transport` (streamable-http|sse), and `auth_note` (what credential the server needs — NEVER a secret value). Creates a status=draft agent you own; then call findagent_submit_for_review IN THIS MCP CLIENT to submit it. The server URL is stored + displayed only; nothing executes on FindAgent. Before calling: findagent_check_slug + findagent_list_categories.
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  • Detect the fillable fields in a PDF form and return an edit schema (edit group): each property is a detected field with its type (text/checkbox/radio/dropdown/signature/table), page index, and bounding box. Use it to see what a form asks for, or as the scaffold for a structured fill — set extend_edit:value on each field and pass the populated schema to edit_pdf. Pass inputSchema to re-run detection against an existing schema (mapping mode) — for the edit-schema rules, call get_documentation with https://docs.extend.ai/editing/configuration.md first. Inspect runs with get_form_detection_run. Follow any llmContext guidance included in results.
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