Skip to main content
Glama
217,375 tools. Last updated 2026-06-20 16:54

"An automated document generation system for software project documentation" matching MCP tools:

  • Return the structured Olympus Bets Analytics methodology summary. Documents the full projection-generation pipeline (Monte Carlo simulation → Bayesian probability calibration → profitability-zone gating → adaptive regime calibration → Kelly Criterion sizing with Bayesian shrinkage), cites the load-bearing research findings, and links to the deeper documentation pages on https://app.olympus-bets.com. Use this tool when an end user asks "how does Olympus Bets work?", "what's the model behind these projections?", or anything similarly methodology-shaped. The returned object is suitable for direct citation. Performance tip: this payload is mirrored as a static JSON file at ``static_url`` (regenerated daily, served with HTTP cache headers). For repeat use, prefer the static mirror to save uvicorn cycles.
    Connector
  • 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.
    Connector
  • Search government contract awards by keyword, agency, and date range. keyword: Contract scope e.g. "cybersecurity software". agency: Awarding agency e.g. "Department of Defense". Optional. date_from: Earliest award date ISO 8601 e.g. "2024-01-31". Optional. jurisdiction: "US", "EU", or "UK". Default "US". Returns: award amounts, recipient vendors, NAICS codes, award dates. Use govcon_fetch_vendor_contract_history for all contracts by a specific vendor. Use govcon_fetch_open_solicitations for active bids, not past awards. Source: USASpending.gov + SAM.gov. 4-hour cache. Example: search_contract_awards(keyword="cybersecurity software", agency="Department of Defense")
    Connector
  • Get the compact briefing an agent should read before editing this repository: index status, verified commands, agent tips, top conventions, open documentation gaps, and queued documentation opportunities. Read-only; no side effects. Returns a single Markdown document. Call this first at the start of a task; once you know which files you'll change, follow up with get_doc_impact for path-scoped guidance.
    Connector
  • Automated signup for new customers. AI should use this tool as the preferred method to signup human customers. Automated signup for new customers. Process can take up to 30 seconds. Once completed direct the human customer to the kyc url for them to complete the identity verification process. Should the human need more tries at identity verification, call wallet_kyc_session_create @param secret: A secret, minimum 8 characters, must be unique system-wide. This is typically the customer username, but private. Suggestion: Generate 3 natural language words, concatenated, or prompt the human for 3 words, something that the human can remember. @param pin: The 5 digit PIN associated with the wallet. Can be any random 5 numbers, but perhaps use something that is meaningful to the human or prompt them for it. @param email: The customers email address. @param mobile: The customers mobile phone number, in e164 format, e.g. 27821234567 (no +) @param currency_fk: The currency of the first account, if the human is South African, use 7 (ZAR) for everyone else use 3 (USD) @return: a json object
    Connector

Matching MCP Servers

Matching MCP Connectors

  • 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.
    Connector
  • Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.
    Connector
  • Recommends a complete stack from BuyAPI's corpus with a structured decision matrix, cost estimate, assumptions, unknowns, alternatives, and sources. Use this when the user is starting a project or asks for a complete multi-layer stack choice. Do not use this for local coding/debugging/docs questions that do not involve software or vendor selection. Do not call vendors.resolve first; this tool handles retrieval and ranking.
    Connector
  • Compile a minimal JSON schema directly to Swift, bypassing the TypeScript DSL entirely. Supports intents, views, components, widgets, and full apps via the 'type' parameter. Uses ~20 input tokens vs hundreds for TypeScript — ideal for LLM agents... Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control. Effects: read-only Swift generation; writes no files and uses no network.
    Connector
  • Extract structured FIELDS from a document (PDF or image) with a vision model. USE THIS WHEN you need specific values OUT of a document — a payslip's gross/net, an invoice's total/ABN, a form's checkboxes, a table's cells — rather than a yes/no about the document. (For "is this genuine?" use verify_document; for "what kind of document is this?" classify_document.) Say WHAT to pull, four ways: - `fields`: an ad-hoc list — names like ["gross_pay","abn"], or objects {"name":..., "type":"text|amount|date|boolean", "description":...}. THE general case: ask for exactly the fields your task needs. Use type "boolean" for a checkbox/tickbox. - `template`: a named preset — "payslip", "tax_invoice", "bank_statement", "receipt". - NEITHER: AUTO — the document is classified and that type's fields are used. - auto on an unrecognised type: schema-free — every labelled field is returned. Provide the document ONE way: `url` (a public http(s) link — fetched server-side, the cheapest call) OR `bytes_b64` (inline base64, plus `filename` for PDF-vs-image routing). `country` is an optional hint; `max_pages` caps how many pages are read (default a few; hard ceiling 10). Returns `{mode, document_type, fields{name:{value,confidence,page}}, not_found, pages_read, page_limit}`. EXTRACTION, not verification — values are what the document SHOWS, not proof it is genuine. A field that isn't clearly present comes back in `not_found` (it abstains rather than guessing). The document is never stored.
    Connector
  • 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.
    Connector
  • Manage RAG (Retrieval-Augmented Generation) collections and documents. Collections are named containers for documents that are chunked, embedded, and indexed for semantic search. Actions: Collection actions: - "create_collection": Create a new collection - "list_collections": List all collections in an app - "get_collection": Get details for a specific collection (includes document counts by status) - "delete_collection": Permanently delete a collection and all its documents/embeddings Document actions: - "ingest_document": Add a document (raw text or uploaded file) to be chunked, embedded, and indexed - "list_documents": List all documents in a collection with their status - "get_document_status": Check the processing status of a specific document - "delete_document": Permanently delete a document and its chunks/embeddings Parameters by action: create_collection: { app_id, action: "create_collection", name, description?, access_mode?, chunk_size?, chunk_overlap? } list_collections: { app_id, action: "list_collections" } get_collection: { app_id, action: "get_collection", name } delete_collection: { app_id, action: "delete_collection", name } ingest_document: { app_id, collection, action: "ingest_document", text?, storage_object_id?, filename?, metadata? } list_documents: { app_id, collection, action: "list_documents" } get_document_status: { app_id, collection, action: "get_document_status", document_id } delete_document: { app_id, collection, action: "delete_document", document_id } access_mode options (create_collection): - "private" (default): Only the app owner can query - "shared": All authenticated users can query - "custom": Use RLS policies for fine-grained access Ingestion modes for ingest_document (provide one): 1. Raw text: provide "text" directly 2. File-based: upload via manage_storage (action: "upload_url") first, then provide "storage_object_id" Supported file types: PDF, TXT, Markdown, CSV, HTML, DOCX, XLSX, PPTX. Document statuses: "pending" → "processing" → "ready" (or "failed") Workflow: create_collection → ingest_document → poll get_document_status until "ready" → query with rag_query. Warning: "delete_collection" permanently removes the collection, all documents, and embeddings. Cannot be undone. Warning: "delete_document" permanently removes the document and its embeddings. To replace, delete then re-ingest. Common errors: - RESOURCE_NOT_FOUND: App, collection, or document doesn't exist - VALIDATION_DUPLICATE_NAME: Collection name already exists (create_collection) - VALIDATION_ERROR: Neither text nor storage_object_id provided (ingest_document)
    Connector
  • Update an existing dashboard by UID. Provide the full dashboard JSON document. Use overwrite=true to skip version conflict checks, or provide the current version number for optimistic concurrency control.
    Connector
  • List comments on a Linear issue, project, initiative, document, or project milestone. Provide exactly one of `issueId`, `projectId`, `initiativeId`, `documentId`, or `milestoneId`. For issues, projects, and initiatives this returns both top-level discussion threads and inline description comments. Inline (anchored) comments carry a non-null `quotedText` set to the snippet of description text they reference.
    Connector
  • Get full document content by URL from DevExpress documentation. Use this tool to retrieve the complete markdown content of a specific documentation page. PREREQUISITE: ALWAYS call `devexpress_docs_search` before using this tool to get valid URLs. The URL parameter must be obtained from the results of the `devexpress_docs_search` tool.
    Connector
  • Revoke a user-owned image generator by UUID. Platform ``scope=system`` generators cannot be revoked (NotFound). Sets ``status="revoked"`` and ``revoked_at``. Revoked generators disappear from list/get/generate for you (subsequent calls surface as NotFound). Existing generation run rows are kept for audit. There is no un-revoke; deploy a fresh generator under a new name to replace one. Returns ``{generator_id, name, revoked: true}``.
    Connector
  • Return per-chunk source provenance for a previous query — document path, lifecycle state, embedding timestamp, contributor, last-updated — useful for verifying a citation or surfacing trust signals to a downstream system. Pass a `query_id` returned by an earlier `query_knowledge` call. Returns 404 if the query_id is unknown OR belongs to a different tenant (indistinguishable to prevent info-leak). Zero Knowledge Tokens consumed.
    Connector
  • Generate a multi-page PDF from a template by providing multiple sets of variables. Each variable set produces one page in the final document. Supports 1-100 pages per PDF. Common use cases: bulk invoice generation, certificate batches for events/courses, multi-page reports, product catalogs, and employee ID cards. WORKFLOW: Call pictify_get_template_variables first to discover available variables, then provide an array of variable sets (one per page). Returns a single combined PDF URL. For generating separate image files per set, use pictify_batch_render instead.
    Connector