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619,814 tools. Updated 2026-09-28 18:28

"Robot Framework" matching MCP tools:

  • Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
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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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  • 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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  • Revoke this authenticated robot connection, including its access and refresh credentials and room memberships. Use only when the human explicitly requests logout, disconnection, or reconnection. Call BEFORE clearing local credentials or running MCP logout/login. It affects only the current connection and accepts no account or robot selector. After login, restart or reload the MCP client, then match connection_info owner.id and handle to the approved account and robot before sending.
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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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Matching MCP Servers

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    Enables agents to work with Robot Framework by discovering tests and resources, drafting tests from requirements, dry-running and executing suites, and parsing output.xml results.
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Matching MCP Connectors

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

  • SEO automation MCP with 39 tools. 12 free tools (no auth): audit pages, check Core Web Vitals, validate schema, score AI visibility, generate keywords. 27 pro tools with account: manage content calendar, track GSC traffic, monitor AI bot visits (ChatGPT/Perplexity), analyze backlinks, and publish directly to WordPress, Webflow, or Wix.

  • Verify the current connection: owner.id is the human public User ID, handle identifies the robot, and home_room is its delivery room. After login or reconnect, match owner.id and handle to the approved account and robot before sending. A mismatch means stop and reload or restart the MCP client. Includes a credential-protected latest-pings feed and token-free install URL; never send credentials to the install URL.
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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.
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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.
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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.
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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.
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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.
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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.
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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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  • List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.
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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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  • Get the questions for one EQIQs framework so the user can take it inside the chat. Ask them one at a time, word for word, accepting only A or B. Call with no framework to list what the user has not completed yet. Personal frameworks (attachment, love language) need the user's explicit consent. Read-only; costs 1 unit.
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  • Get one outfits preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.
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  • Multi-robot joint trajectories (NPZ) for sim_to_real_retargeting or imitation_learning. Platforms: Unitree G1 (43 DOF: 29 body + 14 hand), Unitree H1 (19 DOF), Apptronik Apollo (32 DOF) — specify via robot= param. MoveIt/ROS and Isaac Sim compatible, 60 fps, latency <500ms. Pay $15 USDC on Base (chain 8453, USDC 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913) to RTK wallet 0x6C11F8a21f7ca922F483Ed21C3b6c2d9B305B10C; pass tx hash as payment_tx. Eth/Solana: /.well-known/x402. Cheaper $0.25 trial sample: call get_trial_robotarget.
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  • Captures the user's project architecture to inform i18n implementation strategy. ## When to Use **Called during i18n_checklist Step 1.** The checklist tool will tell you when to call this. If you're implementing i18n: 1. Call i18n_checklist(step_number=1, done=false) FIRST 2. The checklist will instruct you to call THIS tool 3. Then use the results for subsequent steps Do NOT call this before calling the checklist tool ## Why This Matters Frameworks handle i18n through completely different mechanisms. The same outcome (locale-aware routing) requires different code for Next.js vs TanStack Start vs React Router. Without accurate detection, you'll implement patterns that don't work. ## How to Use 1. Examine the user's project files (package.json, directories, config files) 2. Identify framework markers and version 3. Construct a detectionResults object matching the schema 4. Call this tool with your findings 5. Store the returned framework identifier for get_framework_docs calls The schema requires: - framework: Exact variant (nextjs-app-router, nextjs-pages-router, tanstack-start, react-router) - majorVersion: Specific version number (13-16 for Next.js, 1 for TanStack Start, 7 for React Router) - sourceDirectory, hasTypeScript, packageManager - Any detected locale configuration - Any detected i18n library (currently only react-intl supported) ## What You Get Returns the framework identifier needed for documentation fetching. The 'framework' field in the response is the exact string you'll use with get_framework_docs.
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