"A system for legal document analysis and contract management" matching MCP tools:

• bls_dataframe_query

  bls-labor-mcp-server

  Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.

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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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• 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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• legal_get_context

  Lawbster

  Get adjacent norms (paragraphs/articles) before and after a target provision in document order. Use when a legal question may span consecutive provisions or when surrounding context is needed to understand a norm's scope. Requires a norm_id from a prior legal_search or legal_lookup result. Returns the target norm plus up to 10 neighbors in each direction. For a law-wide overview rather than just neighbors, use legal_get_toc.

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• asterwise_get_puja_suggestions

  Asterwise

  Returns puja (ritual worship) recommendations for planetary propitiation per graha. SECTION: WHAT THIS TOOL COVERS For each of the nine grahas, returns: puja name, presiding deity, day of week, specific offerings (flowers, grains, incense), grain associated, and beej mantra. Used by practitioners to recommend planetary remedies based on chart analysis. SECTION: WORKFLOW BEFORE: asterwise_get_natal_chart — identify afflicted planets before recommending pujas. AFTER: asterwise_get_rudraksha — complementary bead-based remedy. SECTION: INPUT CONTRACT planet (optional): One of Sun, Moon, Mars, Mercury, Jupiter, Venus, Saturn, Rahu, Ketu. Omit to get all nine planets. SECTION: OUTPUT CONTRACT Single planet: data.planet, data.puja_name, data.deity, data.day, data.offerings[], data.grain, data.mantra All planets: data.planets{} — object keyed by planet name SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (upstream): Unknown planet name → MCP INTERNAL_ERROR INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_remedies — personalised remedies from natal chart analysis. asterwise_get_rudraksha — bead recommendations, not puja rituals.

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• asterwise_get_soul_urge_number

  Asterwise

  Calculates the Soul Urge (Heart's Desire) number from vowels in the full name. Only A, E, I, O, U are treated as vowels — Y is always a consonant in this system. Reduces each name part separately before summing, preserving master numbers 11, 22, 33. SECTION: WHAT THIS TOOL COVERS The Soul Urge reveals the inner motivation — what the soul craves, what drives choices at the deepest level. It is the hidden engine beneath the Expression. This is the most private of the three core name numbers. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_personality_number — complete the name number trinity. SECTION: INPUT CONTRACT name — Full legal name as used at birth. Example: 'Arjun Mehta', 'Sofia Rossi' Y is always treated as a consonant — not a vowel. SECTION: OUTPUT CONTRACT data.number (int — Soul Urge number; 11/22/33 preserved as master) data.is_master_number (bool) data.karmic_debt_number (int or null) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — validation is upstream. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_expression_number — all letters, not vowels only. asterwise_get_personality_number — consonants only, not vowels.

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• law_citations_resolve

  UK Business Tools - Ledgerhall

  USE THIS TOOL BEFORE constructing an OSCOLA citation string from known fields, OR to confirm a citation points at a real document. Parses + resolves a single citation (neutral citation, SI, legislation section, retained EU law) and returns parsed fields plus resolved_url. For neutral citations, performs a live TNA HEAD check — non-200 sets confidence to 0.0 (document absent). Do NOT format or quote a confidence-0.0 citation. If the TNA HEAD check fails (timeout, connection error), raises ToolError with {"error_category": "transient", "is_retryable": true}. One retry is attempted — retry this call or proceed without TNA verification. Formatting a citation from "known" fields without prior resolution is the most common fabrication route. If this tool raises or returns no resolved_url, do NOT manufacture a citation — surface the failure and ask the user for the source URL. Authoritative source for UK legal-citation resolution.

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• extract_contract_from_url

  transaction-coordinator

  Extract structured transaction data from a contract at a URL. Downloads the document, extracts text (with OCR fallback for scanned PDFs), and runs PrimaCoda's contract-extraction prompt to return parties, addresses, dates, prices, and key contract fields. Use this when an agent has the contract hosted somewhere (Dropbox, Google Drive direct download, Square Space, etc.) and wants to skip the upload step. For multi-document deals (purchase + addenda + disclosures), use the PrimaCoda dashboard's batch upload — this tool handles ONE document. Args: pdf_url: Direct download URL for the contract (PDF, DOCX, TXT, or image). Must be reachable from the PrimaCoda server. Google Drive "shared link" URLs work if set to "anyone with link"; other share URLs may need their direct-download form. api_key: Your PrimaCoda MCP API key (starts 'pck_').

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• sieve_dataroom_add

  Sieve

  Add a document to a deal's data room. Creates the deal if needed. This is the primary way to get documents into Sieve for screening. Upload a pitch deck, financials, or any document -- then call sieve_screen to analyze everything in the data room. Provide company_name to create a new deal (or find existing), or deal_id to add to an existing deal. Provide exactly one content source: file_path (local file), text (raw text/markdown), or url (fetch from URL). Args: title: Document title (e.g. "Pitch Deck Q1 2026"). company_name: Company name -- creates deal if new, finds existing if not. deal_id: Add to an existing deal (from sieve_deals or previous sieve_dataroom_add). website_url: Company website URL (used when creating a new deal). document_type: Type: 'pitch_deck', 'financials', 'legal', or 'other'. file_path: Path to a local file (PDF, DOCX, XLSX). The tool reads and uploads it. text: Raw text or markdown content (alternative to file). url: URL to fetch document from (alternative to file).

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• GetLEIDetail

  Fintel Discovery — Financial Intelligence for AI Agents

  Retrieve the full GLEIF LEI record for one legal entity using its 20-character LEI code. Returns legal name, registration status, legal address, headquarters address, managing LOU, and renewal dates. Use this tool when: - You have a LEI (from SearchLEI) and need full entity details - You want to verify the registration status and renewal date - You need the exact legal address and jurisdiction of an entity Source: GLEIF API (api.gleif.org). No API key required.

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• get_legal_documents

  Molt2Meet

  Get all active legal documents an agent must accept on registration. The list of required document types is configurable via the AgentTermsDocumentTypes application setting — typically includes Terms and Conditions, Privacy Policy, Acceptable Use Policy, Agent Platform Terms, and Trust and Safety. Each document includes its type reference, name, version, effective date, and full markdown content. Call this before register_agent so you know what the agent is accepting when setting acceptedTerms=true. No authentication required.

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• ambr_create_contract

  Ambr

  Generate a Ricardian Contract from a template. Creates a dual-format contract (human-readable legal text + machine-parsable JSON) using AI, linked by SHA-256 hash. The contract is stored on Ambr and accessible via the Reader Portal. Requires a valid API key (X-API-Key header on the HTTP request) with available credits. Use ambr_list_templates first to discover templates and their required parameters. Args: - template (string, required): Template slug (e.g. "c1-agent-delegation") - parameters (object, required): Template-specific parameters matching the schema - principal_declaration (object, required): { agent_id, principal_name, principal_type } - parent_contract_hash (string, optional): SHA-256 hash of parent contract for amendments - amendment_type (string, optional): "original" | "amendment" | "extension" Returns: - contract_id: Unique ID (e.g. "amb-2026-0042") - sha256_hash: SHA-256 hash for verification - status: Contract status - reader_url: URL to view in Reader Portal - credits_remaining: Remaining API credits Legibility: Output is dual-format by construction and replayable to the original SHA-256 hash — the basis of Ambr's legibility guarantee.

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• asterwise_get_balance_number

  Asterwise

  Calculates the Balance number from the first letter of each name part, using Pythagorean values. A three-part name yields three initials summed and reduced. The Balance number describes how a person handles emotional crises and unresolved inner conflict. SECTION: WHAT THIS TOOL COVERS The Balance number is consulted specifically in times of stress. It does not describe everyday personality but rather the instinctive crisis-management style. A person with Balance 1 instinctively becomes self-reliant under pressure; Balance 2 seeks partnership; Balance 8 attempts to assert control. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: None. SECTION: INPUT CONTRACT name — Full legal name as used at birth. The first letter of each space-separated part contributes one value. Example: 'Arjun Mehta' → A(1) + M(4) = 5 Example: 'James Earl Carter' → J(1) + E(5) + C(3) = 9 SECTION: OUTPUT CONTRACT data.number (int — Balance number 1–9, or master 11/22) data.is_master_number (bool) SECTION: RESPONSE FORMAT response_format=json — structured JSON. response_format=markdown — human-readable. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: upstream failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_expression_number — uses all letters, not just initials. asterwise_get_karmic_lessons — identifies absent digits across all letters.

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• hires_list_application_stage_history

  100Hires - AI ATS & Recruitment Software

  Get the full chronological stage transition history for an application, including the initial assignment. Each entry has from_stage_id/name, to_stage_id/name, moved_at (Unix seconds), moved_by_type (system, user, automation), moved_by_user_id, and source (what caused the transition, e.g. 'apply:indeed', 'form_watcher', 'user'; null for historical records). Use this for funnel analysis, attribution reports, and time-in-stage reports instead of paginating through /candidates/{id}/activities when only stage data is needed.

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• dd_gazette_notice

  UK Business Tools - Ledgerhall

  Fetch the full legal wording of a Gazette notice by numeric notice ID. Returns the complete JSON-LD linked-data record for the notice: parties, legal basis, court, and full text. Use gazette_insolvency first to find notice_numeric_id values.

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• get_nda_report

  NDASentry

  Retrieve the full NDA / contract risk report after the user pays $9. Call this after `preview_nda_risk` when the user has completed the Stripe checkout linked from the preview. Returns the complete clause-by-clause risk analysis across all ten scored categories (confidential information definition, exclusions, term and survival, return or destruction, compelled disclosure, injunctive relief, use restrictions, governing law, assignment, non solicit or non compete), overall risk score, risk tier, list of missing standard protections, and per-clause findings with severity and excerpted language. Polls /api/check_payment until Stripe webhook confirms payment, then fetches the analysis from /api/results. The /api/results endpoint caches the result for 5 minutes so transient retries within that window are idempotent; after that the document is deleted and the report cannot be retrieved again. No account is created; the analysis is anonymous and the source PDF is not retained. Polling: 2s interval, 5 minute total cap (150 attempts). Args: session_token: The token returned by `preview_nda_risk`. Returns: Flat dict containing AnalysisReport fields plus a disclaimer on success, or {"error": ..., "message": ..., "disclaimer": ...} on failure. Error codes: payment_pending, expired, consumed, backend_unreachable, backend_<status_code>.

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• legal_clause_extractor

  gapup-mcp

  Structured extraction of clauses, obligations and deadlines from legal documents (SaaS contracts, NDAs, employment agreements, loan agreements, leases, M&A deals, IP licences). Complements contract_risk_scanner with granular per-clause output. ICP: legal ops, M&A lawyers, paralegals, contract managers, compliance officers. Capabilities: • Auto-detects document type (7 types) and language (EN/FR/DE/ES/PT) • Extracts parties with roles (buyer, seller, licensor, employee, etc.) • Splits document into sections and classifies 16+ clause types • Per-clause: 20 obligation patterns (EN/FR/DE), 10 deadline patterns, 18 risk detectors • Document-level: red flags (liability cap, auto-renewal, IP overreach, etc.), missing clauses per doc type • Global deadline calendar with P0/P1/P2 severity • Cross-reference map between sections • Cache: 7 days (legal docs stable once provided) 100% pure compute — no external fetch required. Accepts 10k–100k char documents.

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• get_intention

  ManifestYOU

  Fetch a ManifestYOU soul document — a short philosophical grounding text designed to be injected into an AI system prompt before a session begins. Call this at the start of a session to orient the model toward stillness, precision, or creative expansion before work. Paste the returned soul_document into your system prompt or before the first user message.

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• extract_text

  DocImprint

  Extract plain text from a PDF or image (base64-encoded). Use when you need raw text for downstream AI analysis (summarization, claim checking, structured extraction). For documents at a public URL, use extract_url instead (no base64 encoding needed). Returns: { pages: number, text: string } Example prompts: - "Extract the text from this scanned contract so I can search it." - "Give me the raw text from this PDF document." - "OCR this image and return the text content."

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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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