"Shell" matching MCP tools:

• query_docs_filesystem_n_topA

  docsA

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the nTop documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

  ConnectorNo auth

• send_faxA

  SendThisFaxA

  Submit an uploaded PDF for faxing. Step 1 (before this tool): upload the PDF over plain HTTP multipart, using any HTTP client you have — shell, JavaScript fetch with FormData, Python, etc.: curl -F "file=@document.pdf" https://www.sendthisfax.com/api/upload fetch("https://www.sendthisfax.com/api/upload", {method: "POST", body: formDataWithFile}) The response contains fax_public_id and page_count. PDFs must be unencrypted, at most 50 MB and 1000 pages. Step 2: call this tool with the fax_public_id and the recipient fax number. Two modes: - With an API key (Authorization: Bearer stf_live_... on this MCP connection): the fax price is debited from the prepaid credit balance and sending starts immediately — no checkout, no browser. sender_email and billing_country are optional (they default to the key's records). Buy credits at https://www.sendthisfax.com/en/credits. - Without an API key: sender_email and billing_country are REQUIRED and the tool returns a checkout_url the USER must pay in a browser; the fax is sent automatically once paid. In both modes, poll get_fax_status until status reaches "delivered" or "failed" (failures after payment are auto-refunded). For integration testing, +19898989898 is the designated test recipient number.

  ConnectorNo auth

• create_quote_draftA

  Taokeh MCP serverA

  File a quotation DRAFT into Taokeh from a request you've read or been told. A quote is an ESTIMATE — it does NOT post to the books or move stock; it creates a pending draft the user reviews and approves in Taokeh, which posts a real quotation they can then convert to an invoice or delivery order. Shape the fields with resolve_customer + resolve_product first. The server re-computes every line quantity from the tally and the grand total (with SST) — so present your working, but the server's figures are authoritative. A quote may have no customer (a walk-in estimate). Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check — not lengthy reasoning) for any doubt. If you read the request off a document, attach the original document you extracted from — the owner sees it beside the draft at review (s.82 record-keeping) and it lands on the posted quote automatically on approval. Small files: attachmentBase64 + attachmentMediaType inline. Send attachmentBytes (the original file’s decoded size) with it so a truncated base64 is rejected instead of filed. Anything bigger: request_attachment_upload → PUT the bytes → pass the returned attachmentToken — unless your shell cannot reach taokeh.my (a sandboxed client behind a network allowlist), in which case inline it anyway, with attachmentBytes; never both.

  ConnectorOAuth

• query_docs_filesystem_site_gpt_docsA

  SiteGPT DocsA

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the SiteGPT Docs documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

  ConnectorNo auth

• query_docs_filesystem_cerebriumA

  Cerebrium DocsA

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the Cerebrium documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

  ConnectorNo auth

• query_docs_filesystem_dark_funnelsA

  DarkFunnels documentationA

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the DarkFunnels documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

  ConnectorNo auth

• query_docs_filesystem_honeydew_documentationA

  Honeydew AI DocumentationA

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the Honeydew Documentation documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

  ConnectorNo auth

• squish_videoA

  SquishA

  Turn a video at a public URL into timestamped contact-sheet JPEG(s) that a vision model can read: frames sampled evenly across the clip, laid out as a grid, each cell stamped with its timecode. Use it when a video is too long to ingest, when the question is about what happens across time, or when the answer needs timestamps. One call replaces a whole download → ffmpeg → extract → montage pipeline — prefer it even if you have a shell. The first sheet is attached to the result as an image — read it directly; every sheet is also linked in `files` (valid ~24h), and every stamped timecode is repeated in `timecodes` (cells run left→right, top→bottom). Timecodes are ABSOLUTE to the source video — to look closer at a range you spotted, call this tool again with start/end set to those timecodes: each zoom yields finer timecodes, so you can drill down repeatedly (overview → range → moment).

  ConnectorNo auth

• niche_attach_imageA

  Niche — Editorial IntelligenceA

  Upload or attach a user-supplied or externally-designed image (bring-your-own asset) to a post: the creator's own visual (a product shot, their actual work, a card designed elsewhere) instead of an AI-generated image (niche_render_image_card photo, paid) or a flat brand card. Free, with no image-generation spend. For a visual-product maker the real piece is the sale. Input modes, in order of preference: (1) `upload_ref`, the FAST path for an agent that built the asset itself and can run a shell: POST the raw file to `/asset/upload` (multipart/form-data, your bearer token) to get back an `upload_ref`, then pass it here. The bytes travel over HTTP and never round-trip through the model as base64, so it's effectively instant for a real graphic. (2) `image_url`, a fetchable https URL (the server fetches + stores it; for an asset that already lives on the web). (3) `image: {mime_type, data_base64}`, inline base64, fine for small images only. (4) `image_chunk`, the no-shell FALLBACK: upload bounded chunks of base64. It still re-types the bytes through the model (slow), so use it only when the agent has no shell to curl with. Split the file's bytes into ~32-48KB pieces, base64 EACH independently, send in order, each with a `sha256` of that piece's raw bytes so the server catches a mis-transcribed chunk and has you resend just that one (this is what makes the slow path reliable). Omit upload_id on the first chunk; the response returns one to pass on the rest. Set `final:true` on the last chunk (optionally with `total_sha256`); that call assembles, validates, and attaches. The cell's output must already exist (use niche_add_output first if needed). Sets it as the post's image; publishes with the caption. A dimension_note warns if the image's aspect won't fit the cell. Undo-able (the prior image is kept in history).

  ConnectorNo auth

• query_ai_infrastructure_equipment_trade_v1A

  osintA

  Query verified U.S. monthly IMPORTS of semiconductor-manufacturing EQUIPMENT (HS-8486) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data. Use this for "is the fab buildout actually tooling up, and who supplies the machines" questions — the equipment leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, this measures the tools flowing in, and chip imports (ai_infrastructure.trade) measure the output side. HS-8486 covers machines and apparatus used solely or principally to MANUFACTURE semiconductor boules/wafers, devices, and integrated circuits — AND flat-panel displays (Census does not split them at this level); it is NOT the chips themselves (those are HS-8542). Filter by `country` (the verbatim Census name, e.g. "JAPAN", "NETHERLANDS", "KOREA, SOUTH"), `cty_code` (the Census country code), `country_level` ("total" = the all-countries TOTAL, "country" = an individual country, "grouping" = a Census bloc/continent like ASIA / APEC / EU), `year`, `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range. Group by any of `country`, `cty_code`, `country_level`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"country_level": "country", "group_by": ["country"], "order_by": "general_value_usd", "top_n": 5}` for the top tool-supplying countries; `{"country_level": "total", "group_by": ["data_month"]}` for the national trend. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census response row, re-verifiable via get_source_evidence_v1. Measures: `general_value_usd` (general imports value) and `consumption_value_usd` (imports for consumption) — value only; no tool counts, and no tool-type or vendor breakdown (one HS4 heading: no lithography-vs-deposition-vs-etch split, no per-manufacturer series such as ASML). NEVER SUM across country rows: Census's groupings (ASIA, APEC, EU, OECD, ASEAN, the continents) OVERLAP each other and the individual countries, and the all-countries TOTAL contains everything — so adding rows double-counts; a cross-row sum returns a country_aggregation note and nulls the metric in ranking remainders. Filter `country_level=total` for the U.S. national figure. Country is the country of ORIGIN (Census attribution), not which U.S. fab, state, or operator receives the equipment — there is no U.S. place breakdown. Imports only (not exports), customs value (not landed/CIF/duty), and recent months are preliminary and revised in later Census releases.

  ConnectorNo auth

• query_ai_infrastructure_production_v1A

  osintA

  Query verified U.S. semiconductor & electronic-component PRODUCTION and CAPACITY UTILIZATION — the Federal Reserve's monthly G.17 industrial-production index (2017=100) and capacity-utilization rate (percent) for NAICS 3344 — from the Board's own release, history to 1972. Use this for "are the domestic fabs actually producing / how hot are they running" questions — the OUTPUT leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, equipment imports (ai_infrastructure.equipment_trade) the tools flowing in, chip imports (ai_infrastructure.trade) what crosses the border; this measures domestic production and how much of the installed capacity is in use. NAICS 3344 is "semiconductor and OTHER electronic component" manufacturing — the finest split the Fed publishes here (broader than semiconductors alone, and NOT the same slice as QCEW's 334413). Filter by `series_kind` ("ip" = the production index, on both bases; "capacity_utilization" = percent of capacity in use, seasonally adjusted only; "capacity" = the capacity index behind the rate), `series_name` (the verbatim Fed series, e.g. "IP.G3344.S", "CAPUTL.G3344.S"), `basis` ("seasonally_adjusted" / "not_seasonally_adjusted" — IP only), `year`, `data_month` (ISO first-of-month, e.g. "2026-05-01") or the `data_month_from`/`data_month_to` range. Group by any of `series_name`, `series_kind`, `basis`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"series_kind": "capacity_utilization", "group_by": ["data_month"], "data_month_from": "2024-01-01"}` for the utilization trend; `{"series_kind": "ip", "basis": "seasonally_adjusted", "group_by": ["year"]}` for the production index by year (an average per year). Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Fed SDMX observation, re-verifiable via get_source_evidence_v1. Measures are avg/min/max per reading — `avg_ip_index`, `avg_capacity_utilization_pct`, `avg_capacity_index` (+ min/max variants): an index or a rate is INTENSIVE, so multi-month figures are AVERAGES, never sums (the Fed publishes its own quarterly/annual aggregations, which this block does not serve — monthly grain only). An index is not dollars and not unit counts (2017=100). Capacity and utilization exist seasonally adjusted only — their not-seasonally-adjusted cells are structurally absent, never zero. Averaging the IP index across both bases returns a production_aggregation note — filter or group by basis instead. National industry aggregate: no state, county, fab, or company breakdown. Every monthly release revises history (as_of carries the vintage).

  ConnectorNo auth

• pscale_playA

  bsp-mcpA

  Inhabit a handle in a world, in one call — the no-fiddle entry that makes 'play anya on thornwood' just work. Resolves the world to its beach (a sub-domain <world>.beach.<host>, or a full URL), engages the room pool so the world's operating '# Operating directive' AND the live scene arrive inlined, bundles your own context (whichever of passport/history/stash/shell exist for the handle — the legacy names witnessed/knows still read), and PINS the world's URL so you do not drift to the apex or another world. Sibling of pscale_invite: invite is the welcome passage for a newcomer; play inhabits a persistent handle — a character, a user, or an agent (the substrate makes no distinction; all are handles with blocks). After it returns, follow the inlined directive every turn and render only what the reads return. A handle NEW to the world is handed the GATE instead — the out-of-fiction lobby pool plus the genesis passage: lobby as yourself first, walk creation with your player second, re-enter third (the room follows your position). Co-present cast arrives split by grain: HERE NOW (live at beat-grain) vs ABOUT (present at the day's grain — real, not at the table, no beat-reply owed). RPG: pscale_play(world='thornwood', handle='anya') → you are Anya in the Beaten Drum, directive and scene in hand.

  ConnectorNo auth

• generate_adscriptA

  HeistaA

  Generate direct-response video ad scripts by fusing a proven structural source (decoded ad or formula) with a brand's PowerSource. Output is feed-native ad copy for paid social (Meta, TikTok, Reels) in the brand's voice — hook, beat-by-beat body, CTA close, plus visual direction per beat. Takes source_id (from adformula_intelligence, decoder_intelligence, or decode_ad), source_type ("formula" or "decode"), powersource_id (from any create_powersource_*), and tunable params: count (1-5 variants, tensions and selling points auto-rotated across variants), script_mode ("blueprint" preserves source structure exactly, "remix" preserves psychology but writes original copy), duration (target seconds), audience, tension override, selling_points override, voice_mode ("creator" for UGC default, "brand" for owned channels), and idempotency_key. Use this when the user says "write me a script", "I need a TikTok script", "write an ad based on this", or wants shell-faithful replication of a proven winner in their own brand voice. REQUIRES both a structural source AND a powersource — guide the user through creating either if missing. Metered pricing — typically 2-5 credits per script (~2 credits for 15s, ~5 credits for 60s). Pre-flight reserves a 17-credit ceiling and refunds the difference after measurement. Do NOT use to discover sources — use decoder_intelligence or adformula_intelligence first. Do NOT use to extract brand intel — use create_powersource_url first.

  ConnectorNo auth

• screen_universeA

  Valuein — SEC EDGAR Fundamentals & Smart-Money DataA

  Rank companies by cross-sectional factor scores from factor_scores.parquet. Returns the underlying factors (roe, gross_margin, operating_margin, net_profit_margin, revenue_growth_yoy, fcf_to_assets, debt_to_equity, asset_turnover, current_ratio, piotroski_f_score) plus their percentile ranks (1.0 = best in universe, 0.0 = worst). `composite_rank` (the default sort) is a one-number multi-factor shortcut; sort by a specific *_rank column for a single factor. Two modes: full-universe (omit ticker) or single-entity (ticker set — spot-check ONE company's factor profile). Sector filter is SIC-derived (GICS-aligned, not licensed GICS — see `get_pit_universe`). Use this *instead of* `get_financial_ratios` when you want CROSS-SECTIONAL comparison (rank vs peers); use `get_financial_ratios` when you want one company's ratios over time. Supports survivorship-free POINT-IN-TIME screening via `as_of_date` (see the param). Full-universe screens omit rows that don't join to a company (null symbol); pass `exclude_outliers=true` to also drop shell-company rows with implausible factors. Available on every plan — sample returns the subset covered by the sample bucket.

  ConnectorNo auth

• finalize_certificateA

  TLS RadarA

  Finalize and issue a certificate order in one call: validates the DNS challenges, waits for Let's Encrypt, and returns the issued cert. Step 3 of issuance - call after check_certificate_propagation reports all_found. STRONGLY PREFER passing csr_pem (generate the key + CSR locally with openssl so the private key never leaves the machine). Returns leaf_pem/chain_pem/fullchain_pem. If you must, pass a passphrase instead to get a PKCS#12 bundle - but a CSR is safer. If it replies "still validating", DNS hasn't fully propagated: re-check check_certificate_propagation and call again. Needs a locally-generated CSR (csr_pem) - requires a local shell with openssl. On a surface without one (e.g. a Claude.ai custom connector) this can't complete; it returns guidance to finish in Claude Code/Cowork or the web form. Scanning and monitoring work everywhere. On success the structuredContent carries a `handoff` object - relay `handoff.message` to the user and do NOT separately call add_monitor; the cert→monitoring handoff is automatic and server-side.

  ConnectorNo auth

• add_filesA

  vibedeployA

  Append files to an open staging session. Call as many times as needed; commit_deploy applies them all at once. Validates path/extension/encoding on every call so a bad file fails fast. Same 500 MB cap as single-call deploys, but cumulative across the session. LARGE TEXT FILES: a file that looks too big to inline (100-250 KB of HTML/CSS/JS) usually still fits in ONE call — gzip it locally, base64 the result, send with encoding:'gzip+base64' (text compresses 3-5×, so ~250 KB of source ≈ ~70 KB on the wire). Prefer that over add_file_chunk: one call, no ordering hazards. Only chunk when a single file exceeds ~250 KB of source even after gzip, or when you have no way to gzip locally. If your environment can run shell but can't reach this host, gzip+base64 via add_files is the fastest path; if it CAN reach this host, begin_deploy's uploadUrl (tarball POST, 100 MB) beats everything.

  ConnectorNo auth

• create_referenceA

  LogospellA

  Create an upload slot for a reference image. Returns an upload URL and a ref_ token: upload the image file with one shell command (curl -T <file> '<uploadURL>'), then pass the ref_ token to the reference-image parameter you are filling - every parameter that takes reference images names this tool in its description. This is the ONLY way to supply reference images, and those parameters accept ref_ tokens and nothing else. Image data never goes inside a tool call: a call is JSON, so an embedded image would have to be base64 text that you, the caller, must emit character by character - slow, error-prone, and enough to exhaust your context window. The upload moves the bytes out-of-band instead: a plain HTTP PUT of the raw file, so any HTTP client works; if your environment has no way to send one, install curl. And when the image you want is from one of your OWN recent Logospell generations, skip the upload entirely: pass sourceGeneration and sourceImage and the server copies it directly - the shortcut for extending an existing set in its own style. Accepts PNG, JPEG, or WebP, each at most 500KB, each side between 64px and 768px - resize before uploading if needed; larger reference images do not improve results. A reference is private to your API key and can be used in any number of later calls; it expires 7 days after its last use or re-upload (each use restarts the window), so uploading a few references once can serve a whole session of work. Costs no credits.

  ConnectorAPI key

• create_expense_draftA

  Taokeh MCP serverA

  File a paid-expense DRAFT into Taokeh from a receipt you've read. This does NOT post to the books — it creates a pending draft the user reviews and approves in Taokeh; only then does it hit the ledger. Shape the fields with intake_contract + expense_accounts first. Set amountUncertain when any digit of the printed total is uncertain; set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what the human should double-check — not lengthy reasoning) for any other doubt. If you have the ORIGINAL receipt image/PDF, attach it — it rides the draft and lands on the posted entry automatically on approval, so the user never has to re-upload it. Small files: pass attachmentBase64 + attachmentMediaType inline. Send attachmentBytes (the original file’s decoded size) with it so a truncated base64 is rejected instead of filed. Anything bigger: request_attachment_upload → PUT the bytes → pass the returned attachmentToken — unless your shell cannot reach taokeh.my (a sandboxed client behind a network allowlist), in which case inline it anyway, with attachmentBytes; never both.

  ConnectorOAuth

• create_receipt_draftA

  Taokeh MCP serverA

  File a customer-payment DRAFT into Taokeh ("customer paid X"). This does NOT post — it creates a pending draft the user reviews and approves in Taokeh; only then does it write a real bank receipt and settle the invoices. A receipt settles an EXISTING customer's open invoices: resolve the customer (resolve_customer) and see what they owe (open_invoices) first, then give EITHER a lump `total` (auto-allocated oldest-first) OR explicit per-invoice `allocations`. MYR only — a foreign-currency invoice is refused and routes to Banking → Payments. The server re-derives the allocation against live outstanding, so its figures are authoritative. Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check — not lengthy reasoning) for any doubt. If you have the payment proof (bank-in slip / remittance advice), attach the original document you extracted from — the owner sees it beside the draft at review (s.82 record-keeping) and it lands on the settlement entry automatically on approval. Small files: attachmentBase64 + attachmentMediaType inline. Send attachmentBytes (the original file’s decoded size) with it so a truncated base64 is rejected instead of filed. Anything bigger: request_attachment_upload → PUT the bytes → pass the returned attachmentToken — unless your shell cannot reach taokeh.my (a sandboxed client behind a network allowlist), in which case inline it anyway, with attachmentBytes; never both.

  ConnectorOAuth

• governance_policyA

  Moltline Agent Governance & MCP AuditorA

  Generate an audit-ready agent-governance policy for a fleet. PREMIUM (license). Covers inventory cadence, ownership rules, least-privilege approval gates, injection defense, logging/retention, and decommissioning triggers. Typical input {"fleet_context": "20 agents, 3 with shell access, one finance bot"} returns {"policy": ..., "sections": {...}, "context_note": ..., "audit_checklist": ["...", ...]}. Use when a fleet needs a written policy document. Not for assessing what the fleet currently does (inventory_report, audit_mcp_config). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• governance_policyA

  Moltline Agent GovernanceA

  Generate an audit-ready agent-governance policy for a fleet. PREMIUM (license). Covers inventory cadence, ownership rules, least-privilege approval gates, injection defense, logging/retention, and decommissioning triggers. Typical input {"fleet_context": "20 agents, 3 with shell access, one finance bot"} returns {"policy": ..., "sections": {...}, "context_note": ..., "audit_checklist": ["...", ...]}. Use when a fleet needs a written policy document. Not for assessing what the fleet currently does (inventory_report, audit_mcp_config). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth