"Search for Unreal Engine or unreal-related content" matching MCP tools:

• list_model_channels

  Peec AI MCP Server

  List the AI engine channels tracked by Peec. A model channel is a stable identifier for an AI engine (e.g. "openai-0" = ChatGPT UI) that persists even as the underlying model is upgraded — use it to filter or break down reports by engine without worrying about model version changes. Use this tool to resolve channel descriptions (e.g. "ChatGPT UI", "Perplexity") to channel IDs before filtering reports (model_channel_id filter), and to label channel IDs from report output before presenting results. The current_model_id column gives the model ID currently active in the channel — pass this as model_id where reports require it. is_active indicates whether the channel is enabled for this project — inactive channels return empty data. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, description, current_model_id, is_active.

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• search_notes

  anamnese

  Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.

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• get_compression_candidates

  Push Realm

  Find clusters of related learnings that are ripe for compression. When many similar solutions get linked together (e.g., 10+ 'relates_to' entries about the same issue), they clutter search results and waste agent time. Use this tool to discover clusters that could be compressed into a single consolidated learning. WORKFLOW: 1. Call get_compression_candidates with min_cluster_size=3 (or higher) 2. Review the returned clusters - each has full content for every learning 3. Synthesize a compressed version: one clear (Issue) section plus agent-specific nuances (grok adds X, claude adds Y) 4. Call compress_learnings with the learning_ids, new title, and synthesized content 5. Show preview to user, then confirm_compression on approval Only use when you've seen or been asked about compressing duplicate/similar solutions.

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• fda_import_refusals

  FDA Data MCP

  Search FDA import refusals (Compliance Dashboard data, not available in openFDA API). Import refusals indicate products detained at the US border. Filter by company name, FEI number, country code (e.g., CN, IN for major API source countries), or date range. Critical for evaluating international manufacturing sites and supply chain risk. Related: fda_get_facility (facility details by FEI), fda_inspections (inspection history by FEI).

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• get_doc

  Dock

  Read a workspace's doc (TipTap rich-text) body. Returns three forms of the same content: `content` (TipTap JSON, round-trippable into update_doc for structural edits), `markdown` (CommonMark + GFM, ready to feed to an LLM or render in a non-ProseMirror surface), and `text` (plain text, best for search, summarisation, word-count heuristics). A workspace can hold any combination of doc and table surfaces, one or many of either kind; omit `surface_slug` to read the primary doc surface, or pass it to target a specific doc tab (use `list_surfaces` to enumerate). An unwritten or absent doc returns content={}/markdown=""/text=""; a `surface_slug` that doesn't match any live doc surface 404s.

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• knowledge_query

  DialogBrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use workspace.search for that.

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• congressgov_bill_lookup

  congressgov-mcp-server

  Browse and retrieve U.S. legislative bill data from Congress.gov. Discover bills by filtering on congress, bill type, and date range — there is no keyword search. Use 'list' to browse (requires congress), 'get' for full bill detail (sponsor, policy area, CBO estimates, law info), or drill into a specific bill with 'actions', 'amendments', 'cosponsors', 'committees', 'subjects', 'summaries', 'text', 'titles', or 'related' (each requires congress + billType + billNumber).

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• ateam_github_patch

  ateam-mcp

  Edit a file in the solution's GitHub repo and commit. Two modes: 1. FULL FILE: provide `content` — replaces entire file (good for new files or small files) 2. SEARCH/REPLACE: provide `search` + `replace` — surgical edit without sending full file (preferred for large files like server.js) Always use search/replace for large files (>5KB). Always read the file first with ateam_github_read to get the exact text to search for.

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• fda_search_recall_text

  FDA Data MCP

  Full-text search across recall reasons and product descriptions using PostgreSQL text search. Finds recalls mentioning specific terms (e.g. 'salmonella contamination', 'mislabeled', 'sterility'). Supports multi-word queries ranked by relevance. Filter by classification, product_type, or date range. Related: fda_search_enforcement (search by company name, classification, status), fda_recall_facility_trace (trace a recall to its manufacturing facility).

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• compress_learnings

  Push Realm

  Propose compressing multiple related learnings into one consolidated learning. Call this AFTER get_compression_candidates and synthesizing the compressed content. Same approval flow as submit_learning: show preview to user, then confirm_compression on approval or reject_compression on decline. The compressed content should follow the format: (Issue) summary, then agent-specific nuances (e.g. grok adds X, claude adds Y).

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• search_email

  gmail

  Search for emails in Gmail to find specific messages or filter the inbox. Use this when the user wants to find emails by sender, subject, date, content, or other criteria. Returns email summaries suitable for listing and overview - to read full email content, attachments, or HTML body, use get_email with the returned email ID. This tool searches across all folders unless specified otherwise in the query.

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• echosaw_search_media

  mcp-server

  Semantic search across your analyzed media library. Returns ranked results with titles, summaries, transcript snippets, labels, and relevance scores. Use this to find previously analyzed media by topic, content, or keyword.

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• get_chat

  Peec AI MCP Server

  Get the full content of a single chat (one AI engine's response to one prompt on one date). Returns: - messages: the user prompt and assistant response(s) - brands_mentioned: brands detected in the response with their position - sources: URLs the model retrieved, with citation counts and position - queries: search queries the model issued - products: product gallery entries extracted from the response - prompt: { id } - model: { id } — deprecated, prefer model_channel - model_channel: { id } — stable engine channel id (e.g. "openai-0") Use list_chats to discover chat IDs for a project.

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• skincare_report_issue

  SearchShop AI

  Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.

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• get_sdk_reference

  Senzing

  Get authoritative Senzing SDK reference data for flags, migration, and API details. Use this instead of search_docs when you need precise SDK method signatures, flag definitions, or V3→V4 migration mappings. Topics: 'migration' (V3→V4 breaking changes, function renames/removals, flag changes), 'flags' (all V4 engine flags with which methods they apply to), 'response_schemas' (JSON response structure for each SDK method), 'functions' / 'methods' / 'classes' / 'api' (search SDK documentation for method signatures, parameters, and examples — use filter for method or class name), 'all' (everything). Use 'filter' to narrow by method name, module name, or flag name

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• get_url_content

  Peec AI MCP Server

  Get the scraped markdown content of a source URL Peec has indexed. Use this after get_url_report to inspect the actual content an AI engine read — useful for content gap analysis and competitive content comparison. Input notes: - url is the full URL. Copy it verbatim from get_url_report output. Trailing slashes and scheme variations change the resolved source ID. - Returns 404 if Peec has no record of the URL (it hasn't been scraped from any project). - max_length caps the returned content (default 100000 characters). If the stored content is longer, truncated=true and you can re-request with a higher max_length. Returned fields: - url, title, domain, channel_title: page metadata - classification: domain-level classification - url_classification: page-level classification (HOMEPAGE, LISTICLE, COMPARISON, ...) - content: markdown content, already extracted via Mozilla Readability and converted with Turndown GFM. null when the URL is tracked but scraping hasn't completed yet (can take up to 24h). - content_length: original character length before truncation (0 when content is null) - truncated: true if content was truncated to max_length - content_updated_at: ISO timestamp of last scrape, or null if not yet scraped

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• web_search_exa

  exa

  Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.

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• notes_search

  DialogBrain

  Full-text search in your notebook. By default searches only your own notes. Pass filter_agent_id=<int> to search another agent's notebook, or "all" (or "*") for workspace-wide. Or list all notes for a person/thread by scope_ref_id.

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• skincare_report_issue

  La Luer — AI Skincare Commerce

  Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.

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• browse

  redm-mcp

  Enumerate doc paths in a category/namespace. Use to discover what exists before calling `get_document` or a targeted `grep_docs`. NOT a content search — use `semantic_search` for behavior/concept lookups or `grep_docs` for token lookups. Returns `{path, title, chunks}[]`.

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