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694,558 tools. Updated 2026-10-24 01:09

"Utilizing Local LLMs for Query Preprocessing with Minions Framework" matching MCP tools:

  • Estimate the credits required to run a Disco analysis. Returns `required_credits` for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes `account.available_credits`.
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  • Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.
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  • Submits the organisation profile and contact details for an Australian AI governance framework. The profile determines which legislation the framework identifies, so the answers should reflect the organisation's actual circumstances — turnover in particular, since the Privacy Act's small business threshold sits at $3 million and several categories are caught regardless of turnover. Takes the session ID from start_australian_ai_governance_framework together with the questionnaire answers. Writes the profile against the session and stores the supplied name, email and organisation as a contact record. contact.organisation is printed as the document's "Prepared for" heading, so it should be the organisation's name as it should appear on the document rather than a shorthand. Returns the session ID and a status of profile_saved — it does not return the framework, which is retrieved by get_ai_governance_framework. The profile can be re-submitted on the same session: it is overwritten rather than duplicated, the contact record is keyed on the email address, and any framework already generated for that session is discarded. No authentication, and no charge at this step.
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    Destructive
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  • Returns the organization's development standards: coding conventions, project structure, and framework-specific rules. Read-only. Call it before writing or reviewing code, so the result follows this organization's rules rather than general defaults. Call it first without a section to get an index of available sections, each with a note on what it covers, then call again with one section id copied from that index; inventing a section id returns a not-found error naming that step. Request only the sections a task needs - the full content of one section can be long. The framework argument is deprecated: use section with the "framework:" prefix instead. It returns prose rules, not data - use get_style_tokens for visual values and get_component for component APIs.
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  • 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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  • WHAT: Pointer to one legal HTML page. REQUIRED doc enum. Unknown doc → ok=false unknown_doc hint=/impressum. RETURNS doc + canonicalUrl. For full text call get_ai_page({path:'/'+doc}) or get_discovery(which=llms-urheberrecht) for the copyright corpus. German legal text is binding; /llms-copyright.txt is EN courtesy.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    An MCP server that gives AI agents direct access to VMware vSphere infrastructure, enabling VM lifecycle management, snapshots, datastore operations, networking, and more via 55 typed tools built on govc.
    12 npm
    MIT

Matching MCP Connectors

  • Unofficial MCP server for the Ionic Framework documentation, components and blog.

  • Verified, version-pinned answers about fast-moving frameworks for coding agents.

  • Search the Axint Registry for already-published packages that match a natural-language query. Use this BEFORE calling axint.feature or axint.compile so the agent can install an existing package instead of regenerating Swift the community has already shipped. Use: use before generating code to find reusable packages; not for validating local Swift. Inputs: query drives ranking; kind and platform narrow results without changing the registry source. Effects: read-only local registry search using AXINT_REGISTRY_PATH or sibling checkout; no network by default.
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  • Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.
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  • Return the Wheel of Heaven interpretive framework's reading of a topic — explicitly the project's own Raëlian-canon-centred position, NOT mainstream consensus. Accepts a framework topic (overview, hypothesis, terminology, timeline, sources, method) for the curated narrative documents, or any other term to get the framework reading from the closest wiki entry. Use fact-layer tools (get_passage, compare_traditions) for source-grounded data without this framing.
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  • Register ONE OAuth redirect (callback) URI on the Bridge app, preserving every already-registered URI — never replaces the list wholesale. The URL must be absolute http(s), e.g. https://app.example.com/auth/oauth-callback or http://localhost:5173/auth/oauth-callback for local dev. Adding a URL that is already registered succeeds as a no-op (added: false). The first URI registered on an app with no default callback also becomes its default callback. Use this when wiring a new environment or fixing a "redirect_uri mismatch" OAuth error; see get_environment_info for the currently registered list, and remove_redirect_uri to unregister one. Before writing app code that consumes this, call get_integration_guide with topic=auth and the framework the app uses.
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  • Queries live HackerNews discussions, front-page velocity, and developer sentiment on any framework, startup, or AI model. WHEN TO USE: Call `get_hackernews_tech_pulse` when your task requires deterministic `hn-pulse` analysis for `query`; use `get_agent_wallet_status` to check your remaining free trial quota or prepaid balance. WHEN NOT TO USE: Do not use for unrelated domains or binary media. PARAMETER INTERACTIONS: Pass the target `query` as a non-empty string; omit `agent_id` and `tx_hash` during your 5 free trial calls, or supply `agent_id` (`agd_pass_...`) / `tx_hash` for paid execution. Read-only and idempotent; results are cached for 1 hour (<5ms cache hit). $0.01 per call. Pricing: the first 5 calls per agent_id are free and failed calls are never charged. After 5 calls, the tool returns a payment-required quote (not a fatal error) with a $2 Stripe top-up link and Base/Solana USDC addresses; complete payment, then retry with your agd_pass_ key in agent_id or pass tx_hash. Example: get_hackernews_tech_pulse(query='example')
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  • Queries live HackerNews discussions, front-page velocity, and developer sentiment on any framework, startup, or AI model. WHEN TO USE: Call `get_hackernews_tech_pulse` when your task requires deterministic `hn-pulse` analysis for `query`; use `get_agent_wallet_status` to check your remaining free trial quota or prepaid balance. WHEN NOT TO USE: Do not use for unrelated domains or binary media. PARAMETER INTERACTIONS: Pass the target `query` as a non-empty string; omit `agent_id` and `tx_hash` during your 5 free trial calls, or supply `agent_id` (`agd_pass_...`) / `tx_hash` for paid execution. Read-only and idempotent; results are cached for 1 hour (<5ms cache hit). $0.01 per call. Pricing: the first 5 calls per agent_id are free and failed calls are never charged. After 5 calls, the tool returns a payment-required quote (not a fatal error) with a $2 Stripe top-up link and Base/Solana USDC addresses; complete payment, then retry with your agd_pass_ key in agent_id or pass tx_hash. Example: get_hackernews_tech_pulse(query='example')
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  • Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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  • Colorize black-and-white or grayscale photos. DDColor (dual-decoder, ICCV 2023) — vivid, natural colorization. Impossible for text/vision LLMs. 5 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='colorize_image'.
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  • Search nifra's framework documentation and get back ONLY the matching sections - auth, uploads, ISR, WebSockets, loaders, deployment, anything. Call with no query for the cheap section index; pass query (e.g. "isr revalidate") for the top matching sections. Use this instead of reading llms-full.txt (~150 KB) whole. For the EXACT TypeScript of a type/interface/function, call nifra_types instead (don't read .d.ts files).
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  • Create a NEW knowledge artifact from Anima's knowledge-base template. Returns a ready artifact; never edits an existing one. Call with no arguments, or with an optional name (defaults to "Untitled project") and workspaceId. No files, ZIP upload, framework, or artifact type are accepted. The template supplies the initial files and determines the framework. Returns the sessionId, initial revision, artifactUrl, name, framework, fileCount, skippedFiles, and applicable preview URLs and Git access. Share artifactUrl with the human. Use artifact-explore and artifact-edit to inspect and change its contents. Only use artifact-publish when the user explicitly asks for a public deployment.
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  • Returns current one-time prices and checkout links for the Framework + Executive Suite, the Framework alone, and AI at Work seats, including the seat tiers, co-branding and the Rollout Pack. Use it for the full price list, or get_seat_quote for a specific number of seats. It does not take payment. To check readiness, use assess_ai_readiness.
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  • Read the Bridge integration guide for a topic — the supported, opinionated way to consume Bridge from application code. Call this BEFORE writing or changing app code that reads a feature flag, renders billing or team UI, or wires authentication, and before hand-rolling anything against the SDK: the SDKs ship declarative surfaces for these cases (components, route guards, decorators) and reimplementing them against SDK internals is the most common integration mistake. Per-framework topics (framework REQUIRED): integration (first-time setup) | auth | feature-flags | billing | team | branding. Framework-agnostic MASTER topics (framework is IGNORED — omit it): master, the end-to-end auth integration prompt served by `bridge guide`, which orchestrates project discovery and then routes to the per-framework guides; flags-master and billing-master, the same orchestration for a flags-only or billing-only integration; mechanisms, how limits, upgrades and customization work; orientation, what Bridge does (the same map get_started returns); fit-together, how roles, plans, limits and flags fit together; integration-success, the message to show the developer when an integration is done. Before setting up flags, roles, plans or limits, read topic=fit-together. Start at topic=master when integrating Bridge into a project for the first time. framework: svelte | react | angular | nextjs | nestjs | express — pass the one the target app actually uses; this server cannot detect it. Returns the guide as markdown plus its source URL. Guidance is a default, not a mandate — follow the user's stated preference when it differs.
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  • Return the site llms.txt index: what the site covers, every post with its Markdown URL, and other agent entry points. With full set to true, return llms-full.txt, which includes every post body. Use for a whole-site overview or bulk reading, not for finding one post; use search_posts for that.
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  • List available Duck Framework AI guides. Use this to discover available guides before fetching one with fetch_ai_guide(). Returns guide identifiers and their descriptions.
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  • WHAT: Fetch one public discovery file from www and return a line window. REQUIRED which. IDs (aliases folded): llms, llms-full, llms-index, llms-keywords, llms-keywords-brand, llms-serp, llms-jsonld, llms-impressum-kontakt, llms-orte-geo, llms-grenzen-koordinaten, llms-urheberrecht, llms-copyright, llms-mcp-server, llms-mcp-web, robots, sitemap-txt, sitemap-xml, ai-txt, ai-plugin, answer-engine, ard, ai-catalog, auth-md, mcp-readme, agent-skills (+ locale slices llms-keywords-en|… ). summary = the line window + trailing # MCP-META (truncated, totalUrls, nextOffset). Omit limit: if remaining >~30KB → first ~30KB window + nextOffset (O9); explicit limit keeps line window 1..10000 (not forced to 30KB). Unknown which → unknown_discovery. Prefer dedicated get_llms_* / get_sitemap_txt when you know the file. Policy files say ai-train=no.
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