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127,531 tools. Last updated 2026-05-05 21:04

"Integrating corporate Confluence with model access for search and document navigation" matching MCP tools:

  • Search the MITRE D3FEND catalog of defensive techniques by keyword, tactic, or targeted artifact. Default response is SLIM (drops `uri` from each row — saves ~60 chars/row, ~30% on popular drills); pass include='full' for the verbose record. Pass exclude_id when chaining from d3fend_defense_lookup to skip self in sibling-artifact searches. Use to discover defenses applicable to a given threat model — e.g. 'what defenses harden access tokens?' (tactic=Harden + artifact='Access Token'). Drill into d3fend_defense_lookup with any returned defense_id for the ATT&CK technique mappings. Free: 100/hr, Pro: 1000/hr. Returns {query, total, results [{defense_id, label, uri (only when include=full), parent_label, tactic, artifact}], next_calls}.
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  • Convert a single photo into a textured 3D GLB model. Uses Seed3D — generates accurate geometry and materials from one image. Async — returns requestId, poll with check_job_status. 350 sats per model. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_3d_model'.
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  • Search the regulatory corpus using keyword / trigram matching. Uses PostgreSQL trigram similarity on document titles and summaries. Returns documents ranked by relevance with summaries and classification tags. Prefer list_documents with filters (regulation, entity_type, source) first. Only use this for free-text keyword search when structured filters aren't sufficient. Args: query: Search terms (e.g. 'strong customer authentication', 'ICT risk', 'AML reporting'). per_page: Number of results (default 20, max 100).
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  • Primary tool for reading a filing's content. Pass a `document_id` from `list_filings` / `get_financials`. MANDATORY for any substantive answer - filing metadata (dates, form codes, descriptions) alone doesn't answer the user; the numbers and text live inside the document. ── RESPONSE SHAPES ── • `kind='embedded'` (PDF up to ~20 MB; structured text up to `max_bytes`): returns `bytes_base64` with the full document, `source_url_official` (evergreen registry URL for citation, auto-resolved), and `source_url_direct` (short-TTL signed proxy URL). For PDFs the host converts bytes into a document content block - you read it natively including scans. • `kind='resource_link'` (document exceeds `max_bytes`): NO `bytes_base64`. Returns `reason`, `next_steps`, the two source URLs, plus `index_preview` for PDFs (`{page_count, text_layer, outline_present, index_status}`). Use the navigation tools below. ── WORKFLOW FOR kind='resource_link' ── 1. Read `index_preview.text_layer`. Values: `full` (every page has real text), `partial` (mixed), `none` (scanned / image-only), `oversized_skipped` (indexing skipped), `encrypted` / `failed`. 2. If `full` / `partial`: call `get_document_navigation` (outline + previews + landmarks) and/or `search_document` to locate pages. If `none` / `oversized_skipped`: skip search. 3. Call `fetch_document_pages(pages='N-M', format='pdf'|'text'|'png')` to get actual content. Prefer `pdf` for citations, `text` for skim, `png` for scanned or oversized. ── CRITICAL RULES ── • **Navigation-aids-only**: previews, snippets, landmark matches, and outline titles returned by the navigation tools are for LOCATING pages. NEVER cite them as source material - quote only from `fetch_document_pages` output or this tool's inline bytes. • **No fallback to memory**: if this tool fails (rate limit, 5xx, disconnect), do NOT fill in names / numbers / dates from training data. Tell the user what failed and offer retry or `source_url_official`. • Don't reflexively retry with a larger `max_bytes` - for big PDFs the bytes are unreadable to you anyway. Use the navigation tools instead. `source_url_official` is auto-resolved from a session-side cache populated by the most recent `list_filings` call. The optional `company_id` / `transaction_id` / `filing_type` / `filing_description` inputs are OVERRIDES for the rare case where `document_id` didn't come through `list_filings`. Per-country document availability, format, and pricing - call `list_jurisdictions({jurisdiction:"<code>"})`.
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  • Get county-level food access risk profiles using Census ACS data. Constructs food access risk profiles by combining vehicle access (B25044), poverty status (B17001), and SNAP participation (B22001). Limited vehicle access combined with high poverty indicates food desert risk. Useful for identifying areas with barriers to food access in grant applications. Args: state: Two-letter state abbreviation (e.g. 'WA', 'MS') or 2-digit FIPS code. county_fips: Three-digit county FIPS code (e.g. '033' for King County, WA). Omit to get all counties in the state.
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  • <tool_description> Search and discover products, recipes AND services in the Nexbid marketplace. Nexbid Agent Discovery — search and discover advertiser products through an open marketplace. Returns ranked results matching the query — products with prices/availability/links, recipes with ingredients/targeting signals/nutrition, and services with provider/location/pricing details. </tool_description> <when_to_use> Primary discovery tool. Use for any product, recipe or service query. Use content_type filter: "product" (only products), "recipe" (only recipes), "service" (only services), "all" (all, default). For known product IDs use nexbid_product instead. For category overview use nexbid_categories first. </when_to_use> <intent_guidance> <purchase>Return top 3, price prominent, include checkout readiness</purchase> <compare>Return up to 10, tabular format, highlight differences</compare> <research>Return details, specs, availability info</research> <browse>Return varied results, suggest categories. For recipes: show cuisine, difficulty, time.</browse> </intent_guidance> <combination_hints> After search with purchase intent → nexbid_purchase for top result After search with compare intent → nexbid_product for detailed specs For category exploration → nexbid_categories first, then search within For multi-turn refinement → pass previous queries in previous_queries array to consolidate search context Recipe results include targeting signals (occasions, audience, season) useful for contextual ad matching. </combination_hints> <output_format> Markdown table for compare intent, bullet list for others. Products: product name, price with currency, availability status. Recipes: recipe name, cuisine, difficulty, time, key ingredients, dietary tags. Services: service name, provider, location, price model, duration. </output_format>
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Matching MCP Servers

Matching MCP Connectors

  • Confluence MCP — wraps the Confluence Cloud REST API v2 (OAuth)

  • corporate-apology MCP — wraps StupidAPIs (requires X-API-Key)

  • Get the list of legal document templates available for generation on the platform (e.g. NDA, employment agreement, stock purchase agreement). For corporate services like 83(b) filing or registered agent, use get_available_corporate_services instead.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Search for a data model by approximate or misspelled name using fuzzy matching. Use this as the recovery step whenever get_data_model returns MODEL_NOT_FOUND — it finds the closest real model names even when the spelling is off. Returns ranked candidates with similarity scores. Example: fuzzy_find_model({"model_name": "WeatherFora", "threshold": 80})
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  • Search 20,000+ free icons across 10 libraries by meaning, label, visual description, tags, and synonyms. Use this when the user describes an icon concept such as "database", "user profile", "chill", "security", or "AI model". Returns matching icons with SVG code and public semantic guidance.
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  • Retrieve a shipment document (commercial invoice) as binary PDF. **IMPORTANT:** This tool returns only metadata (content type and size) because MCP cannot transmit binary data. For usable document links, prefer calling `get_shipment` with `format="URL"` instead — it returns clickable download URLs. Only use this tool if you specifically need to confirm a document exists or check its file size. Required authorization scope: `public.shipment_document:read` Args: easyship_shipment_id: The Easyship shipment ID, e.g. "ESSG10006001". document_type: The type of document to retrieve. Must be "commercial_invoice". page_size: Page size for the document: "4x6" or "A4". Default: "A4". Returns: Metadata only (content type and size). For downloadable URLs, use `get_shipment` with format="URL".
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  • Get AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns news events with dates and summaries for industry context.
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  • Get metadata and navigable section index for a GOV.UK page. Returns the page title, document type, publication dates, and a list of sections with their anchor IDs and headings. Use govuk_get_section to read the body of a specific section, or govuk_grep_content to search within the page body.
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  • 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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  • WHEN: you need ALL objects of a given type or in a given model. Triggers: 'list all tables in ALM', 'show all classes', 'quels objets dans le modèle', 'give me all forms'. Full index scan -- returns EVERY matching object, not just top search results. Use to discover what tables, classes, forms, enums, etc. exist in a specific model. When no filters are given and a custom model is configured, defaults to listing that model. NOT for a single object -- use get_object_details. NOT for natural language search -- use search_d365_code.
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  • Locate pages containing a phrase. Returns matching page numbers + short context snippets for navigation. Useful when the outline/landmarks don't list your target (e.g. you want 'directors' remuneration' but only 'Directors Report' is a landmark). Up to `max_hits` pages (default 20) are returned; `total_hits` counts raw matches across the document. CRITICAL - snippets are NAVIGATION AIDS ONLY and may contain OCR errors. Once you've identified target pages, call fetch_document_pages(pages=<n>) to read the authoritative text / bytes before citing anything. Requires get_document_navigation (or fetch_document on a PDF) to have run first so the per-page text index exists in R2.
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  • Get the complete structure of the Catalunya 2022: RESET policy document — 3 spheres, 12 goals, 91 actions created by a 30-expert Catalonia Task Force. Returns the hierarchy with canonical slugs for navigation via get_section.
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  • List all custom evaluation models for the authenticated user. Returns an array of model objects with id, name, description, and status. Use model id in artifact, rubric, and evaluation tools. Free.
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  • Delete a custom evaluation model. This removes the model and all associated artifacts and rubrics. model_id from atlas_create_custom_eval_model or atlas_list_custom_eval_models. Free.
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