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649,985 tools. Updated 2026-10-11 16:33

"Guide to Deploying a Large Language Model Framework" matching MCP tools:

  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
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  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
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  • List the framework ids this server covers (langchain, llamaindex, ollama, xrpl) with display names, aliases, homepages, and catalog topics. Use when you do not know which framework string to pass. Free tools/call (no x402). Not a docs search (search_ai_framework_docs) and not a deprecation dump (list_known_deprecations). Catalog-backed.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
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Matching MCP Servers

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

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

  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Scaffold a flags-only Bridge integration into an existing project — the MCP equivalent of `bridge flag init`. Returns everything that command produces, for YOU to write to disk: the contents of bridge-flags.config.json (app id, app name, base URL, suggested provider config, read live from this Bridge app), the install command, and the framework-specific setup guide fetched from the plugin repo that owns it. Writes nothing — neither to disk nor to the Bridge app. Call this when adding feature flags to a project for the first time; for auth or billing use get_integration_guide with topic=master or topic=billing-master instead. framework is REQUIRED: this server cannot see the caller's disk, so detect it yourself from package.json (next → nextjs, @sveltejs/kit or svelte → svelte, @nestjs/core → nestjs, @angular/core → angular, react → react, express → express; check in that order) and pass it. The returned guide is the same document get_integration_guide returns for topic=feature-flags, so there is no need to call both. Do not invent import specifiers — they differ per plugin and only the guide has the current ones.
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  • A package version's TYPED API: exported symbols and their signatures from its shipped .d.ts (or DefinitelyTyped), exact to the version, none of it in the model's training data. Use before writing code against a package whose API may have moved. For whether a name exists at RUNTIME (what decides if an import throws) use resolve_surface; for is-it-safe-to-install use verify. Pass knownVersion (e.g. the version you were trained on) to get the precise delta: added, removed, renamed, changed. Also returns the version's declared engines (Node/runtime floor). Large surfaces are paged, 80 symbols per call: `totalSymbols` is the size, `query` filters by name, `offset` pages. `shallow: true` means the API lives on an interface's members that are not listed, and the note says where to read them. For framework file/convention changes (not exported symbols), consult the official migration guide / Context7 instead.
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  • Poll a translation job started by translate_files and, once it is "completed", pull back the translated files. While the job is still running, returns its status and progress with no file content — call again until status is "completed". A "completed" response carries each translated file (one per namespace × target language) plus a manifest; a "failed" response carries the failure reason. Optionally filter by target language to bound the payload of a large job.
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  • Search for solutions other agents already discovered. STOP and search before acting on any error — even mid-task. Do not chain fixes without searching each one. When: Any error, compile failure, stack trace, or unexpected output. Before config, integration, or setup tasks. When a fix attempt just failed — search before trying the next one. Unfamiliar library or framework combination. Do not skip because the fix seems obvious — obvious fixes are the most common and most likely to already be solved. How: Paste the exact error message, not your goal. Include framework or language name. Read failedApproaches first to skip dead ends. Feedback: Include previousSearchFeedback to rate a result from your last search — this refunds your search credit and costs nothing extra.
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  • Deploy a reusable image generator that skills reference to produce images from a chosen model: creates it or appends a version. An image generator is a named, versioned configuration that routes image generation calls to a specific model. Generators are private and owner-scoped. Skills reference them by UUID or ``uuid@version``. You cannot deploy a new generator whose ``name`` matches an active platform ``scope=system`` generator (those are tier-level configs that are run-only and not listed or fetched). Versioning: the first deploy with a given ``name`` creates the generator at version 1. Re-deploying the same ``name`` appends a new version and requires ``expected_version_token`` from the latest known version (returned by deploy/list/get). A new generator must omit the token; an existing one without a token returns Conflict. Deploy-time validation: the ``model`` is checked against the pricing layer. A model that does not resolve to a known image endpoint with an authoritative price is rejected before any row is written. Returns: ``{generator_id, name, description, current_version, version, version_token, status, scope, provider, model, generation_contract, config_hash, created_at}``. Persist ``version_token`` for the next re-deploy.
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  • Returns an official GuruWalk support guide for a specific traveler-support topic. GuruWalk is a platform for free walking tours and paid activities; these guides are GuruWalk's own source of truth on how bookings, cancellations, account settings and contacting guides actually work, including current policies and the exact URLs travelers should use. These guides apply only to bookings and accounts on guruwalk.com. Available topics: - account_settings: The traveler wants to manage their GuruWalk account: edit their details (name, surname, phone, city, password), change their email, stop receiving emails / unsubscribe, or delete their account; or they can't access their account. These are concrete steps you shouldn't improvise: consult this before answering. - contact_guru: The traveler wants to contact or coordinate something with the guide of their GuruWalk booking, or thinks they are talking directly to the guide: they can't find them at the meeting point, the guide didn't show up, they're running late, they treat you as if you were the guide, ask for the tour photos, or ask about bringing a pet or paying the guide, or have a question only the guide can answer. - free_tour_modification: The traveler wants to modify or reschedule their GuruWalk free tour — change the day, time, language or number of people — or asks how to do it. - group_booking: The traveler wants to book or extend a GuruWalk booking for a group (they usually say how many; treat it as a large group from around 6 people), asks how to book for many people, can't book for the whole group, sees a large-group notice or is asked for a card or payment for the group, or had a booking cancelled as "group or duplicate". The rules aren't intuitive; consult this before advising. - paid_cancellation: The traveler wants to cancel or change a paid activity booked on GuruWalk, asks about a refund, or can't cancel from their account. Call this when the traveler raises a support topic covered above. Pass the exact topic; the guide content is returned.
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  • Fetch a blob's bytes by id, base64-encoded in the result. Pass 'modelId' to fetch a blob referenced by a specific model (access-scoped); omit it for a direct store fetch. Large blobs may exceed the result-size limit — this channel suits small binaries only.
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  • Mamanida's editorial buying guides for one storefront, in that storefront's own language. Called without `guide` it lists the published guides (metadata only: title, deck, meta description, pillar, topics, dates and the canonical Mamanida URL), optionally filtered by `topic`. Called with `guide` (the guide slug from the listing) it returns that one localized edition plus its structured body: paragraphs, headings, lists, comparison tables, callouts, links to other guides and category calls-to-action. A `category_cta` gives a `category_slug` you can pass straight to search_products, which is the intended guide → category → product path. Retailer and affiliate URLs are never returned.
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  • List the controlled Tech Domain and Language/Framework vocabularies (the software-development discipline facets). Pass chosen slugs as create_draft tech_domains[] / languages[]. Read-only; these are enrichment facets (unknown slugs are dropped). Prefer findagent_submission_wizard to walk the user through the tech step-by-step.
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  • Promotes a model the caller owns into a reusable template in place: no copy, the model keeps its content and stays editable, and it appears in `layerz_list_templates`. Its usage guide is the model's FINANCE.md. `scope: 'user'` (default) is private and Pro-gated; `scope: 'system'` publishes into the curated catalog visible to every account and is admin-only. Not advertised to model-scoped or read-only keys.
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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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  • Find exact tokens or patterns in raw documentation, including large Lua tables and community-native notes. Use to find every source reference, walkstyle/clipset names such as MP_Style_Casual, scenario keys or framework API symbols. For complete structured data (including propsets, controls, bones, drawable/material components, entity extensions and other named catalogs) prefer discovery_lookup; for model assets use asset_lookup; for callable native hashes/names use lookup_native; for behaviors use semantic_search. grep_docs remains the fallback for unsupported formats or a catalog miss. Optional contextBefore/contextAfter includes surrounding lines; filesOnly returns paths; multiline permits cross-line patterns. Patterns use Rust regex syntax. Prefer targeted patterns over large alternations. Returns path and 1-based line numbers; long lines are windowed around the match. Read surrounding raw content with read_lines({path,start}), including content beyond a document preview. If retrying a failed pattern, populate prior_attempt.
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