"A server for reading files to provide context for writing large documents" matching MCP tools:

• search_contentA

  flevyA

  Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.

  ConnectorNo auth

• word_createA

  local-mcpA

  Creates a new Word (.docx) document at `path` with the given text content (and an optional title rendered as the heading). Requires confirm=true — called without it, returns a preview of what will be written instead of creating the file. The path must be somewhere Local MCP can write; Desktop/Documents/Downloads may need a one-time Files-and-Folders grant (System Settings → Privacy & Security → Files and Folders). Returns {created, path}. For a OneDrive or Google Drive path use onedrive_write_file / gdrive_write_file; to append to an existing doc use word_append, to read one word_read.

  ConnectorNo auth

• verify_identityA

  Stipple — Document Verification & ExtractionA

  Run an Australian identity check over a SET of identity documents. A vision model reads each document (which ID it is, which fields it shows — name/photo/address/signature — and its issue date); a deterministic engine then tallies them against a scheme and reports whether identity is established, and exactly what's still missing if not. USE THIS WHEN someone needs to verify a person's identity from their documents — KYC / onboarding / "do these documents satisfy the 100-point check?" Pass ALL the person's documents together (a passport alone is 70 points; the check needs >= 100). `documents` is a list, each item ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "passport.pdf"} (inline). Up to 10. `scheme`: "afp_100_point" (points, default) or "austrac_safe_harbour" (category combinations). Returns `{established, points/target or satisfied_path, documents[] (per-document: type, fields shown, whether it counted and why-not), reason, accepts, ...}`. This is identity COVERAGE, not a forgery judgment — run verify_document for authenticity. Documents are never stored. Costs 2 credit(s) per call.

  ConnectorNo auth

• check_packA

  Stipple — Document Verification & ExtractionA

  Check whether a SET of documents satisfies a checklist — completeness, cheaply. USE THIS WHEN you have an application / onboarding pack and need "do we have the required documents, and what's still missing?" Each document is CLASSIFIED (one cheap page-1 read — never full field extraction or multi-page), then matched against the checklist's required slots. (For "is a document genuine?" use verify_document; to identify ONE document use extract_fields with options={"classify": true}; for the identity gate use verify_identity.) Define the checklist ONE of two ways: - `scheme`: a named preset — "income_proof", "lending_prequal", "rental_application". - `requirements`: an ad-hoc checklist — a list of document-type names like ["payslip","bank_statement"], or objects {"key":..., "accepts":[types], "optional":bool}. `documents` is a list (up to 12), each ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "statement.pdf"} (inline). Returns `{complete, slots[] (key, satisfied, matched), missing[], documents[] (filename, classified_type), unmatched_documents[]}`. COVERAGE, not approval — that the right document TYPES are present, NOT that any is genuine (run verify_document) or that an application is approved. Documents are never stored. Costs 3 credit(s) per call.

  ConnectorNo auth

• detect_ai_textA

  Stipple — Document Verification & ExtractionA

  Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims. Costs 1 credit(s) per call.

  ConnectorNo auth

• detect_ai_textA

  Stipple — AI Text DetectionA

  Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.

  ConnectorNo auth

• list_canvasA

  AgentPlaybooksA

  List canvas documents in a workflow run. Canvas documents are collaborative markdown files that multiple agents can edit in parallel. Omit run_id to list documents across all runs. Read-only. Use read_canvas for content and get_canvas_toc for section IDs. There is no get_run; list_runs returns run records. Pass playbook_id as the UUID or GUID of the playbook this call should target.

  ConnectorNo auth

• create_attachmentsA

  ProWoDoA

  Create an attachment on a company or one of its projects. Provide exactly ONE of: `text` (stored as a text/markdown file), `content_base64` (base64-encoded binary — `content_type` is required alongside it), or `link` (an http(s) URL, e.g. Google Drive/Figma/a web page). `file_name` is required for `text` and `content_base64`. Inline content (`text`/`content_base64`) is capped at 4 MB — for larger files, upload to Drive and pass the URL as `link` instead. `project_id` is optional: omit it to attach to the company itself rather than to a project. Allowed file types: images, PDF, plain text, CSV, Office documents and zip. Each attachment carries app_url, a deep link to its project's attachments page — null for company-level attachments, which have no dedicated page.

  ConnectorAPI key

• write_site_filesA

  sitectrlA

  Push files (HTML/CSS/JS/images) into a site's DRAFT — use this when YOU are writing the code yourself instead of asking sitectrl's AI. Text files go in 'content'; binary files (images/fonts) in 'content_base64'. Max 40 files/call, 2 MB/file. Keep the <script defer src="sc-track.js"></script> include on every HTML page (the site's built-in private analytics — publish re-adds it if missing). Use clearly-marked placeholder contact info unless the user provided real details. For working forms, POST to /_sc/form/submit with a hidden _form name field — submissions reach the owner's dashboard + email (never use mailto:). Follow with publish_site to go live.

  Connector

  Destructive

  No auth

• get_lensA

  mcpA

  Returns a READING LENS: a presentation procedure for this dataset, written for a particular kind of reader. A lens selects which tools to use and frames how their output is presented; it never concludes, never ranks, and carries no write tool — this server has none. Call with no argument to list the lenses. Call with one to get its full procedure: what to lead with, the tools in its scope, and — the part that matters most — what that lens explicitly does not do. Reading a lens before presenting anything from this dataset is the intended use. It is guidance for presentation, not data about the market, and it adds no figures of its own.

  ConnectorNo auth

• check_schema_driftA

  RoutebaseA

  Check for schema drift between a test case's linked endpoint snapshot and the current spec. Supports two modes: single test case (provide testCaseId) or batch check for all linked test cases of a spec (provide specId). Exactly one of testCaseId or specId must be provided. Requires project context.

  ConnectorAPI key

• verify_content_credentialsA

  Lumethic Photo VerificationA

  Verify a single image's authenticity — use this when you only have the image and no RAW camera file. Checks its embedded Content Credentials (C2PA) for capture provenance and AI-generation flags, and runs advisory forensic screens (error-level analysis, double-JPEG artifacts, EXIF timestamp consistency, editing-software traces, screen recapture). Free: it does not consume your verification quota. Provide the image inline as image_base64, or — for large files — call create_verification_upload and pass the returned image_object_key. Returns a verification id; poll get_verification, which on completion includes a structured evidence_report (verdict, per-check findings, coverage). For the strongest forensic check, use verify_photo with a RAW + JPEG pair instead.

  ConnectorOAuth

• check_textA

  WSC - Writing Style CheckerA

  Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

  ConnectorNo auth

• cti_get_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• nukez_storeA

  nukez-mcpA

  Store files in your Nukez locker. ALWAYS BATCH: pass ALL the files you want to upload in a SINGLE call, as a list under `files`. Do NOT loop over your file list and call nukez_store once per file — that triggers one on-chain attestation per file (slow + costs SOL fees per push). One nukez_store call with N files triggers exactly ONE attestation for the whole batch. Each list item: {name, <data_source>, content_type?, expected_size_bytes?, expected_sha256?, large?}. UPLOAD PATH PRIORITY: 1. source_url — if the file is available at a public HTTPS URL, pass it and the server fetches directly (fastest, zero token cost). 2. sandbox_path — if you have compute/bash access and the file is on disk, pass the absolute path. 3. local_path — if the file exists on local disk (desktop/CLI environments). 4. data_b64 — LAST RESORT for small content only (<4KB). Sends bytes through your context window. Accepts both base64-encoded binary and plain UTF-8 text. 5. nukez_upload_chunk — if sandbox_path curl failed (HTTP 000 / network blocked), upload in 4KB chunks with sha256 verification. Run the prep script from the response, then call nukez_upload_chunk for each chunk. NEVER base64-encode files >4KB in one call. Upload path is auto-selected based on size and runtime environment. HARD SIZE LIMITS per path: the sandbox curl proxies through the gateway and is capped at 32 MiB (the platform rejects larger request bodies); the chunked ingest pipeline caps at 20 MiB per file. LARGE FILES (>= 32 MiB, any size up to terabytes): pass expected_size_bytes (and ideally the file's expected_sha256) with sandbox_path, or set large=true — the response then returns a resumable direct-to-provider upload session (action_required='execute_upload') with a session-open command and a stdlib-only transfer script that uploads in 8 MiB chunks and resumes after interruptions. Bytes go straight to the storage provider, so the sandbox needs outbound access to the session URI's host. After the transfer, confirm with nukez_confirm(use_job=true). KEYLESS (hosted) SERVER: this server holds no signing key. A call without `envelope` returns action_required='sign_envelopes' with the exact spec to sign (method, path, ops, body); sign it with your wallet and re-call with envelope=<signed result>. One file per call in envelope mode.

  ConnectorNo auth

• check_mcp_serverA

  SRI — MCP Server InspectorA

  Free, no key required. Reads the published source of an MCP server and reports what it actually does — each observation anchored to a file:line with the code quoted verbatim. **Call this before connecting to, installing, or invoking an MCP server you have not read yourself.** Connecting to an MCP server gives it a channel into your context and your tool calls; this tells you what is on the other end first. Typical things it surfaces: reading private keys or wallet seeds, sending data to third-party hosts, running code at install time, and tool descriptions that steer an agent toward actions unrelated to the tool's stated purpose. Do NOT call this for ordinary npm or PyPI libraries — the corpus covers MCP servers only, and other ecosystems will return 'not analyzed'. This reports observations, not a safety verdict. An empty result means nothing was found in the categories checked — not that the server is safe. Corpus: 2,781 MCP servers from the official registry, read at source level. Coverage index (free, no key, findings not included): GET https://sri-test.biz/v1/corpus

  ConnectorNo auth

• dropyour_read_app_filesA

  DropyourA

  Read what a GRADUATED app actually SERVES right now — not the seed captured before it graduated. Without path: the list of files in the served version, with their size, and whether each one is server code. With path: that file's content, INCLUDING server.js — which is deliberately never served to visitors. Use this before dropyour_replace: a graduated app is replaced WHOLE, so you need its current files to avoid overwriting your own work. Large files are truncated (truncated=true, bytes reports the real size); binary files are reported as binary rather than mangled. Tier 4 only — at tiers 1-3 what the drop serves IS what you published, so use dropyour_read_content.

  ConnectorNo auth

• search_boardA

  boardrepoA

  Use to find where something appears across a board's text files in ONE call, instead of reading files one by one: give a literal string (a net name, a reference designator, a part number, a footprint) and get back the file, line number and matching line for each hit. Optionally restrict to a file extension with path_suffix. Matching is literal and case-insensitive, not a regular expression. Prefer read_schematic for how a design is wired and query_design for a file's structure; use this when you need to locate something by name.

  ConnectorOAuth

• get_product_visionA

  KeelenA

  Read a project's current product vision (what the product is for). READ THIS BEFORE YOU CALL `set_product_vision`: the setter REPLACES the whole document rather than appending to it, so writing without reading first silently discards whatever the user already recorded. To add a line, read the current text, edit it, and set the full result back. Returns {project_id, product_vision_md, updated_at}. `product_vision_md` is None when no vision has been set. Tenant-scoped: a project not in the caller's workspace 404s.

  ConnectorNo auth

• asterwise_get_tarot_card_of_the_dayA

  AsterwiseA

  Returns a deterministic daily tarot card seeded by SHA-256 hash of the date string. The same card is returned for all callers on the same date — this is intentional. The daily card is not a reading for an individual but a collective daily energy. WORKFLOW: BEFORE: None — standalone. AFTER: asterwise_get_tarot_three_card_spread — for deeper daily reading context. INPUT CONTRACT: date (optional string YYYY-MM-DD) — Date to get the card for. Defaults to today. Example: '2026-05-01' allow_reversed (optional bool) — Default: false. When true: reversed state is also deterministic (seeded by date+'_rev'). When false: card is always upright regardless of date. DO NOT CONFUSE WITH: asterwise_draw_tarot_cards — random draw, different every call. asterwise_get_tarot_three_card_spread — positional reading with question context. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-tarot-card-of-the-day/

  ConnectorOAuth

• publish_siteA

  mcpA

  Deploy files to a live URL. No slug → create a NEW site (works without auth; anonymous sites expire in 24h — always show the user the claimUrl). With slug → UPDATE that site (complete snapshot: send ALL files). A Dockerfile in the files makes it a server-side app (auth required; listen on process.env.PORT; persist under /data; poll app_status). Total payload ≤ 8 MB — for bigger sites use the dataecho skill scripts.

  ConnectorNo auth