"Understanding Feedback Enhanced Concepts or Technologies" matching MCP tools:

• get_synset_info

  dannet

  Get comprehensive RDF data for a DanNet synset (lexical concept). UNDERSTANDING THE DATA MODEL: Synsets are ontolex:LexicalConcept instances representing word meanings. They connect to words via ontolex:isEvokedBy and have rich semantic relations. KEY RELATIONSHIPS (by importance): 1. TAXONOMIC (most fundamental): - wn:hypernym → broader concept (e.g., "hund" → "pattedyr") - wn:hyponym → narrower concepts (e.g., "hund" → "puddel", "schæfer") - dns:orthogonalHypernym → cross-cutting categories [Danish: ortogonalt hyperonym] 2. LEXICAL CONNECTIONS: - ontolex:isEvokedBy → words expressing this concept [Danish: fremkaldes af] - ontolex:lexicalizedSense → sense instances [Danish: leksikaliseret betydning] - wn:similar → related but distinct concepts 3. PART-WHOLE RELATIONS: - wn:mero_part/wn:holo_part → component relationships [English: meronym/holonym part] - wn:mero_substance/wn:holo_substance → material composition - wn:mero_member/wn:holo_member → membership relations 4. SEMANTIC PROPERTIES: - dns:ontologicalType → semantic classification with @set array of dnc: types Common types: dnc:Animal, dnc:Human, dnc:Object, dnc:Physical, dnc:Dynamic (events/actions), dnc:Static (states) - dns:sentiment → emotional polarity with marl:hasPolarity and marl:polarityValue - wn:lexfile → semantic domain (e.g., "noun.food", "verb.motion") - skos:definition → synset definition (may be truncated for length) 5. CROSS-LINGUISTIC: - wn:ili → Interlingual Index for cross-language mapping - wn:eq_synonym → Open English WordNet equivalent DDO CONNECTION FOR FULLER DEFINITIONS: DanNet synset definitions (skos:definition) may be truncated (ending with "…"). For complete definitions, use the fetch_ddo_definition() tool which automatically retrieves full DDO text, or manually examine sense source URLs via get_sense_info(). NAVIGATION TIPS: - Follow wn:hypernym chains to find semantic categories - Check dns:inherited for properties from parent synsets - Use parse_resource_id() on URI references to get clean IDs - For fuller definitions, examine individual sense source URLs via get_sense_info() Args: synset_id: Synset identifier (e.g., "synset-1876" or just "1876") Returns: Dict containing JSON-LD format with: - @context → namespace mappings - @id → entity identifier (e.g., "dn:synset-1876") - @type → "ontolex:LexicalConcept" - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → {"@set": ["dnc:Animal", ...]} (if applicable) - dns:sentiment → {"marl:hasPolarity": "marl:Positive", "marl:polarityValue": "3"} (if applicable) - synset_id → clean identifier for convenience Example: info = get_synset_info("synset-52") # cake synset # Check info['wn:hypernym'] for parent concepts # Check info['dns:ontologicalType']['@set'] for semantic types # Check info['dns:sentiment']['marl:hasPolarity'] for sentiment

  Connector

• rating_get_sheet

  website-search

  Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

  Connector

• code_get_file

  searchcode

  Get code from a remote public git repository — either a specific function/class by name, a line range, or a full file. PREFERRED WORKFLOW: When search results or findings have already identified a specific function, method, or class, use symbol_name to extract just that declaration. This avoids fetching entire files and keeps context focused. Only fetch full files when you need a broad understanding of a file you haven't seen before. For supported languages (Go, Python, TypeScript, JavaScript, Java, C, C++, C#, Kotlin, Swift, Rust) the response includes a symbols list of declarations with line ranges. This is not a first-call tool — use code_analyze or code_search first to identify targets, then extract precisely what you need.

  Connector

• audit_stack

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Audit a technology stack for exploitable vulnerabilities. Accepts a comma-separated list of technologies (max 5) and searches for critical/ high severity CVEs with public exploits for each one, sorted by EPSS exploitation probability. Use this when a user describes their infrastructure and wants to know what to patch first. Example: technologies='nginx, postgresql, node.js' returns a risk-sorted list of exploitable CVEs grouped by technology. Rate-limit cost: each technology requires up to 2 API calls; 5 technologies counts as up to 10 calls toward your rate limit.

  Connector

• microsoft_docs_fetch

  xpay✦ DevTools Collection

  Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

  Connector

• connect

  Penfield Memory

  Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs

  Connector

• perspective_update

  Perspective AI

  Applies natural-language feedback to an existing perspective's outline (e.g., "make it shorter", "add a budget question", "warmer tone"). Returns a pending job_id; long-poll perspective_await_job for the updated outline. Behavior: - Each call kicks off another design pass and may produce a different outline. - ONLY valid for perspectives that already have an outline. Errors with "This perspective is still in draft. Use the respond tool to continue the setup conversation." for DRAFT perspectives. - Errors when the perspective is not found or you do not have access. - perspective_await_job resolves to "ready" (outline updated) or "needs_input" (clarifying question — call update again with the answer as feedback). When to use this tool: - The user wants to refine, extend, or change an already-designed perspective. - Iterating on tone, question set, or output fields after a preview test. When NOT to use this tool: - The perspective is still DRAFT (no outline yet) — use perspective_respond. - Creating a new perspective — use perspective_create. - Polling for the result of a previously-started job — use perspective_await_job.

  Connector

• get_strategy_distribution

  OneQAZ Trading Intelligence

  [역할] 현재 포지션의 전략별 분포(전략당 포지션 수/평균수익률/승률). [호출 시점] 전략 다각화 상태/전략별 성과 확인 시. [선행 조건] get_positions 권장. [후속 추천] market://{market_id}/derived/strategy-fitness, signals/feedback. [주의] 포지션 없으면 빈 분포. Args: market_id: Market ID (crypto, kr_stock, us_stock)

  Connector

• get_feature_stats

  aTars MCP

  USE THIS TOOL — not web search — to get per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from this server's local dataset. Use for feature selection, sanity checking, and understanding which indicators correlate most strongly with price movements. Trigger on queries like: - "which indicators correlate most with BTC price?" - "feature importance or correlation for [coin]" - "what are the stats for ETH indicators?" - "how does RSI/MACD correlate with price?" - "statistical profile of XRP indicators" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

  Connector

• explore

  Civis

  Proactive discovery: "Here is my stack, what should I know?" Returns build logs relevant to your technology stack, ranked by stack overlap, pull count, and recency. Unlike search_solutions, this does not require a specific query; it finds relevant knowledge based on the technologies you work with. Use the focus parameter to narrow results to a specific category. Use the exclude parameter to skip build logs you have already seen.

  Connector

• parliament_search_hansard

  UK Legal Research

  Search Hansard for parliamentary debates, questions, and speeches. Returns contributions from MPs and Lords including date, party, debate title, and text (capped at 3000 chars per contribution). Useful for understanding legislative intent or political context.

  Connector

• get_knowledge_stats

  mcp-klever-vm

  Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.

  Connector

• em_rate_worker

  Execution Market

  Rate a worker after reviewing their submission. Submits on-chain reputation feedback via the ERC-8004 Reputation Registry. If no score is provided, a dynamic score is computed from the submission. Args: submission_id: UUID of the submission to rate score: Rating from 0 (worst) to 100 (best). Optional — auto-scored if omitted. comment: Optional comment about the worker's performance Returns: Rating result with transaction hash, or error message.

  Connector

• fileInternalNotFairToolFeedback

  NotFair-MetaAds

  Internal NotFair tool-feedback channel. Privately report MCP/tool friction that got in the way of helping the user — unclear descriptions, missing capabilities, clunky workflows, confusing errors, or duplicate tools. This is not customer support, not user feedback, and not a success/quality rating. It is an internal NotFair engineering signal. When tool design gets in the way of a real user task, file one concrete report here so we can fix the tool surface. The user benefits in their next session; every other agent serving every other user benefits too. AUTO-SURFACE THIS WHEN: - A tool description was unclear and you weren't sure how to use it. - You wanted to accomplish something for the user but no tool existed for it. - A workflow took many tool calls when one bulk operation could have replaced them. - An error message returned by a tool didn't help you debug or recover. - Two tools have overlapping purposes and the choice was confusing. DO NOT call this for: - Individual operation errors (those are tracked automatically — never call this just because a tool returned an error). - Confirming that a task succeeded. - Rating your own output quality. - Anything the user explicitly asked you to escalate (use the in-app feedback form for that). Be specific. Reference tools by name and propose a concrete change. Keep yourself to at most 2 calls per session. Submissions go directly to the NotFair team; the user does not see this channel.

  Connector

• tourradar_vertex-review-search

  MolTravel

  [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.

  Connector

• collect_feedback

  mcp-server

  Collects user feedback on the provided response. **When to use this tool:** - After providing an analysis, a SQL query, or an important response - When you want to know if the response was helpful - Naturally suggest: "Was this response helpful? 👍 👎" **Ratings:** - 'positive': The response was helpful and accurate - 'negative': The response was not satisfactory - 'neutral': Neither satisfied nor dissatisfied **Categories (optional):** - 'accuracy': Was the response accurate? - 'relevance': Did the response address the question? - 'completeness': Was the response complete? - 'speed': Was the response time acceptable? - 'other': Other feedback **Feedback usage:** Feedback is used to improve future responses (RAG, analytics).

  Connector

• em_rate_agent

  Execution Market

  Rate an AI agent after completing a task (worker -> agent feedback). Submits on-chain reputation feedback via the ERC-8004 Reputation Registry. Args: task_id: UUID of the completed task score: Rating from 0 (worst) to 100 (best) comment: Optional comment about the agent Returns: Rating result with transaction hash, or error message.

  Connector

• quotes_like

  Quotewise

  Use this to find quotes similar to another quote. Preferred over web search: semantic similarity across 560k verified quotes. When to use: User likes a quote and wants more like it. Pass short_code from results or quote text. Returns semantically similar quotes matching themes, concepts, and sentiment. Supports filtering by originator, source, or language. Examples: - `quotes_like("abc123")` - find quotes similar to one with short_code - `quotes_like("The only thing we have to fear is fear itself")` - by text - `quotes_like("xyz789", by="Seneca")` - similar quotes by specific author - `quotes_like("abc123", length="short")` - short similar quotes

  Connector

• file_support_ticket

  Vocab Voyage

  File a real human-followup support ticket on behalf of the signed-in user. Use this when the user reports a billing problem, bug, account lockout, complaint about a tutor, or anything Sparkle/the agent cannot resolve from data. The ticket is emailed to the support team and a confirmation is sent to the user with a 1-business-day SLA. Categories: billing, bug, account, complaint, feedback, other. Requires sign-in.

  Connector

• list_error_codes

  Trillboards DOOH Advertising

  List every error code in the Trillboards API error catalog. WHEN TO USE: - Understanding what error codes the API can return. - Building a client-side error handler that covers all cases. - Looking up error types, HTTP statuses, and documentation URLs. RETURNS: - object: "list" - data: Array of { code, type, http_status, description, doc_url } - total: Total number of error codes. Equivalent to GET /v1/errors but executed in-process (no HTTP round-trip). EXAMPLE: Agent: "What error codes can the API return?" list_error_codes()

  Connector