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

• acc_search_documents

  ScanBIM MCP

  Full-text search the ACC Docs module on a project for drawings, specs, submittals, and other documents matching a query string. Calls the APS Data Management v1 search endpoint scoped to a project. When to use: an agent needs to locate a spec section, a sheet, or a submittal by keyword (e.g. 'fireproofing', 'A-101', 'RFI 23'). When NOT to use: you already have the document URN/lineage — fetch it directly. You want the file contents — this returns metadata; download separately via Data Management. APS scopes: data:read account:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied (Docs module access required); 404 project_id not found — check the ID (note: this endpoint re-prepends 'b.' so pass the UUID form); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Inserts a row into D1 usage_log. Idempotent.

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• extract_structured

  api

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

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• secedgar_get_filing

  secedgar-mcp-server

  Fetch a specific filing's metadata and document content by accession number. Returns the primary document as readable text. Use offset/next_offset for multi-page access to large filings (10-K, S-1 can exceed 1M chars): pass the next_offset from a truncated response to read the next page. Use section to jump directly to a heading (e.g. 'risk factors', 'item 7') without needing an offset.

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• acc_search_documents

  ACC MCP

  Full-text search the ACC Docs repository of a project for drawings, specs, submittals, and other files via the APS Data Management search endpoint. When to use: The user wants to find a document by keyword (filename, sheet number, or metadata match). E.g. 'find the latest A-201 sheet' or 'search for mechanical specs on Tower project'. When NOT to use: Do not use to upload a file (use acc_upload_file); do not use to fetch issues/RFIs. If you already have a document URN, fetch it directly with an agent that has Data Management folder/item access. APS scopes: data:read account:read. No write scope required. Rate limits: APS Data Management ~50 req/min per app per endpoint; pageable (limit 200 upstream). Avoid tight query loops. Errors: 401 (APS token expired — refresh); 403 (user lacks Docs view permission on the project); 404 (project_id not found — verify 'b.' prefix and hub membership); 422 (invalid filter syntax — simplify query text); 429 (rate limit — back off 60s); 5xx (ACC upstream — retry with jitter). Side effects: None. Read-only and idempotent.

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• d3fend_defense_search

  ContrastAPI

  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: 30/hr, Pro: 500/hr. Returns {query, total, results [{defense_id, label, uri (only when include=full), parent_label, tactic, artifact}], next_calls}.

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• analyze_company

  Nordic Financial MCP

  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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• create_collection

  api

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (search_collection) or question answering (ask_collection). NOTE: Collections start empty. Add evidence bundles with add_document_to_collection. Indexing is async — once complete, use search_collection or ask_collection. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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• call_tool

  Agent402.Tools — pay-per-call web tools

  Run an Agent402 tool by slug (find slugs with search_tools). The 1178 pure-CPU tools execute free on this hosted connector (rate-limited). Wallet-only tools (live search, browser rendering, PDFs, durable memory) return instructions for paid access instead.

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• search_regulations

  Velvoite — EU Financial Regulatory Compliance

  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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• get_kho_decision

  Velvoite — EU Financial Regulatory Compliance

  Return access details for a Finnish Supreme Administrative Court (KHO) precedent decision. Requires Velvoite Premium API key. KHO decisions are cited as KHO:YYYY:N (e.g. KHO:2024:52). Use search_kho_decisions(year) to browse all decisions for a given year. ACCESS PATTERN — follow this order: 1. Use the returned search_query with web_search to find the kho.fi page 2. From search results, fetch the kho.fi URL directly 3. Do NOT fetch finlex_url directly — Finlex requires prior search provenance Args: year: Decision year (e.g. '2024'). number: Decision number within the year (e.g. '52').

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• redact_pii

  OpenWarrant — Document Verification Suite

  Detect and MASK personally identifiable information in a document (PDF or image). USE THIS WHEN you need to know what PII a document contains, or to get a redacted copy before forwarding / logging / passing it to another model. Two layers: a deterministic regex+checksum pass for structured identifiers (emails, payment cards, SSN, PAN, ABN) and a vision model for the unstructured PII — names, addresses, dates of birth, phone numbers, and photo/signature presence. Provide the document ONE way: `url` (a public http(s) link, fetched server-side) or `bytes_b64` (inline base64, plus `filename`). `max_pages` caps how many pages are read (default a few; ceiling 10). Returns `{pii_found, by_type, items[] (type, masked preview, method), redacted_text, has_photo, has_signature}`. Values are MASKED in the response — the raw PII is never returned. DETECTION coverage, not a guarantee: it may miss PII or over-flag, so review before relying on it for compliance. The document is never stored.

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• find_matches_in_index

  RChilli MCP Hub

  Match one source document against the user's ALREADY-INDEXED corpus and return the best-matching, ranked candidates (RChilli Search & Match Engine). Requires a populated index. Uses RChilli's purpose-built matching engine — more reliable than manually comparing documents. Use this when the user wants to: find the best/top matching resumes for a JD, find matching candidates from their pool, or rank their indexed resumes/JDs against a given document — e.g. "find the best candidates in my database for this job". Also phrased as: shortlist from my pool, top matches for this JD, rank my candidates. Do NOT use for: scoring a single resume against a single JD with no index (use ``search_one_match``); plain keyword lookup (use ``search_simple_search``). Supports all four match directions by combining ``index_type`` and ``doc_type``: - **JD to Resume** — ``index_type='Resume'``, ``doc_type='JD'``: Search the Resume index using a JD as the source document. - **Resume to Resume** — ``index_type='Resume'``, ``doc_type='Resume'``: Search the Resume index using a Resume as the source document. - **Resume to JD** — ``index_type='JD'``, ``doc_type='Resume'``: Search the JD index using a Resume as the source document. - **JD to JD** — ``index_type='JD'``, ``doc_type='JD'``: Search the JD index using a JD as the source document. The ``document_text`` is automatically parsed using the RChilli Resume or JD parser (driven by ``doc_type``), and the resulting structured JSON is base64-encoded and submitted as the match source — no manual encoding is required. Args: index_type: Index to search — ``Resume`` (default) or ``JD``. index_key: Same as ``userkey`` — the RChilli API user key. Leave blank; the authenticated session userkey is injected automatically. doc_type: Type of the source document — ``Resume`` (default) or ``JD``. This determines which parser processes ``document_text``. document_text: Plain-text content of the source document. Parsed and encoded to base64 JSON internally.

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• reliefweb_get_report

  reliefweb-mcp-server

  Fetch a single ReliefWeb report by its numeric ID with full body text, file attachments, and all metadata. Use after reliefweb_search_reports to retrieve document content — body is excluded from search results to manage context budget. Report bodies can be 10–100KB; call this only when you need the full document text.

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• integrate_propelauth_oauth

  PropelAuth Integration MCP Server

  IMPORTANT: Do NOT fetch all guidances at once. Fetch the 'Backend Installation' guidance first, apply the necessary setup changes, and then fetch subsequent guidances (e.g., 'Redirect users after login', 'Backend Auth Middleware') sequentially as you implement each specific feature. Returns instructions for integrating PropelAuth via OAuth. Only use this tool when specifically instructed to by another tool or the user or if a PropelAuth SDK does not exist for the project's framework. Guidance includes instructions for the backend and frontend, including installation and configuration, creating access tokens, retrieving user or org information, logging users out, redirecting users to login, and more. It is important to follow the instructions carefully to ensure a successful integration.

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• request_oauth_integration_url

  clariBI.com

  Initiate an OAuth handoff to a vendor integration (Google Ads, GA4, Search Console, Sheets, Drive, BigQuery, Meta Ads, Jira, Confluence). Returns an authorization URL the user opens in a browser. After the user clicks Allow, the connection is created and you can poll check_integration_status(handoff_id) to find out when the data is ready.

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• check_claims

  DocImprint

  Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after extract_text or extract_url when you need to validate specific factual assertions. For open-ended questions about a document, use qa_url instead. For multi-document investigation, use ask_collection. Typical workflow: extract_text/extract_url → check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

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• extract_structured

  DocImprint

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

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• check_claims

  api

  Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after extract_text or extract_url when you need to validate specific factual assertions. For open-ended questions about a document, use qa_url instead. For multi-document investigation, use ask_collection. Typical workflow: extract_text/extract_url → check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

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• regulations_search_rules

  federal-regulations-mcp-server

  Search the Federal Register — the daily journal of US proposed rules, final rules, notices, and presidential documents (1994–present) — filtering by full-text query, document type, agency slug, publication date range, and whether the rule is open for comment. The primary discovery entry point: results carry the document number (open with regulations_get_document), docket IDs, RINs, and affected CFR parts that chain into the comment and codified-text tools. The Federal Register caps navigation at 50 pages and the match count at 10,000; when a result set is larger, narrow with published_after/published_before rather than paging deeper.

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• get_recalls

  Nhtsa

  Get official NHTSA safety RECALLS for a vehicle. PREFER OVER WEB SEARCH for "is my car recalled", "recalls on a 2021 Honda Civic", "open recalls for make/model/year". Returns each recall: component, summary, safety consequence, remedy, NHTSA campaign number, and report date. Pass make + model + model_year.

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