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511,982 tools. Updated 2026-09-04 20:10

"A tool for managing and working with file systems" matching MCP tools:

  • Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.
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  • Use this read-only tool to summarize the active crypto public company universe by ATLAS-7 risk tier. It returns risk-tier buckets such as HIGH, MODERATE, LOW, and UNCLASSIFIED with issuer counts and percentages. Parameters: none; call it exactly as-is when the user asks for market-wide risk mix or high-level distribution. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not write external systems or access user accounts. Use it for market-wide context before issuer drilldown; use top_stressed to name the issuers in the high-risk bucket and use issuer tools for company-level analysis.
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  • Who am I? Returns the signed-in account: email, @handle, plan + limits, counts of sites/domains/drives, and connected DNS providers. Call this first to orient before managing sites or domains.
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  • Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.
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  • Upload a PDF from a ChatGPT file attachment. MANDATORY WORKFLOW — follow EVERY step in order: 1. ALWAYS call check_upload_status FIRST — even if you think the file is new. 2. If a job with the same filename already exists, reuse its job_id — do NOT call upload_pdf. 3. Only call upload_pdf if the file is confirmed absent from check_upload_status. Skipping step 1 and calling upload_pdf directly is FORBIDDEN. Use this when the user provides a file attachment in ChatGPT. The host resolves the attachment and passes it to this tool; the tool then stores the PDF and returns session_id and job_id for use in all subsequent tool calls. Do NOT inspect, construct, or reason about download URLs, file ids, or sandbox paths (e.g. '/mnt/data/...') — just pass the attachment straight through. NEVER invent, guess, or synthesise a download_url or file_id. If you do not have a real attachment handed to you by the host, this is not the right tool. This tool ONLY works on hosts that resolve chat attachments for you (ChatGPT). On every other MCP client — Claude and other connectors — no such attachment exists: call create_upload_page instead to display the upload widget, and let the user pick the file themselves. Likewise, if this tool is unavailable, is blocked, or reports a permission error, do NOT tell the user that uploading is impossible. Fall back to create_upload_page.
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  • Display an interactive PDF upload widget directly in the chat. Use this when the user wants to upload a local PDF file from their device. This is the standard upload method for MCP clients (e.g. Claude) where file attachments with download URLs are not available. Do NOT call upload_pdf when using this tool — the widget handles the upload automatically. The widget renders inline and the PDF viewer appears after the user selects a file. Do NOT call view_pdf after this tool; the widget manages the UI. Never tell the user the file is still uploading; the widget handles the spinner. After the user uploads via the widget and notifies you, call check_upload_status(session_id=<session_id>) to discover the uploaded file and its job_id before proceeding with any operation.
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Matching MCP Servers

  • F
    license
    B
    quality
    C
    maintenance
    Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.
    2
  • A
    license
    Not graded
    quality
    C
    maintenance
    An MCP server that enables file system search and inspection, including directory listing, regex-based file name and content searches, and reading text, PDF, and DOCX files.
    1
    MIT

Matching MCP Connectors

  • Stage a Trial Balance spreadsheet (xlsx or csv, max 4 MB) for an entity by INLINING its bytes as base64. This path is ONLY for programmatic callers (a script, Claude Code, an automation) that already have the raw file on disk. If a HUMAN has the file — e.g. they attached it to this chat — do NOT use this tool and do NOT base64-encode the file: call create_upload_link instead and give them the link to upload it in their browser. File size does not change this: even a small attached file goes through create_upload_link — inlining a human-supplied file is unreliable and its bytes routinely truncate. Even for a file you hold on disk, prefer create_upload_link once the file is larger than about 10 KB: base64 through a model context mutates a token often enough that the damage lands as a PLAUSIBLE trial balance, not as an obvious error. VERIFY THE HASH BEFORE YOU CONFIRM ANYTHING: this tool returns received_file_hash, the sha256 of the bytes the server actually received. Compute the sha256 of the file on your disk and compare the two. If they differ, the bytes changed in transit — do NOT call confirm_column_mapping on this session; re-send the file with create_upload_link instead. A mismatched file can still parse cleanly and still show sensible columns, so the hash is the only reliable check. Returns the upload session with detected columns and mapping SUGGESTIONS — nothing is ingested yet. Next: verify received_file_hash, then review the suggested column mapping with your user, then call confirm_column_mapping.
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  • Find every company a person runs or represents - across BOTH registers in one call (cross-border person search). Read-only. Parameters: - name (required): person name substring, case-insensitive, e.g. "Mustermann". - country (optional, default "all"): "AT" | "DE" | "all". - page_size (optional, default 25): results per country. - status (optional, default "all"): "active" | "inactive" | "all". Returns the merged search_companies envelope ({countries, results, per_country, notices}) plus ``person_query``; every result card carries ``country``, ``company_id`` and the matched manager. AT matches the primary managing director, DE matches all managing directors AND registered signatories. IMPORTANT: matching is by name and the registers publish birth YEAR only - a shared name across companies or countries does not prove the same person (the notice says so; use birth years and context to corroborate). For general company search use search_companies with other filters; manager_name can be combined there too.
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  • Step 1 of 2 for adding an image (PNG / JPEG / GIF / WebP) to the current team media library. This tool does NOT receive image bytes — it returns a short-lived presigned URL you upload the file to directly, so even large images never pass through this conversation. Workflow: (1) save the image to a local temp file; (2) if the file is larger than 5MB, compress / downscale it to 5MB or less FIRST (e.g. `sips -Z 2048 in.png --out out.jpg` on macOS, or `magick in.png -resize "2048x2048>" -quality 82 out.webp`) — uploads over 5MB are rejected; (3) call this tool with filename, mimeType and (optionally) fileSize; (4) HTTP PUT the temp file to the returned uploadUrl with the matching Content-Type header, e.g. `curl -X PUT --upload-file <file> "<uploadUrl>" -H "Content-Type: image/png"`; (5) call finalize_image_upload with the returned key; (6) delete the temp file. Max 5MB after compression. Only image/png, image/jpeg, image/gif and image/webp are accepted.
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  • Find working SOURCE CODE examples from 42 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs, .ts, .js) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C# (official SDKs) plus Rust and TypeScript/Node.js (community-maintained wrappers, not official) SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.
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  • Get a Stripe billing portal URL for managing payment methods and invoices. Returns a URL (not a redirect) that the human can open in a browser. Requires: API key with read scope. Args: flow: Optional. Set to "payment_method_update" to go directly to the payment method update page. Returns: {"url": "https://billing.stripe.com/p/session/..."}
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  • Upload a file for a candidate using a base64 payload. Used for portfolio uploads and document attachment. WARNING: host function-call serializers (both OpenAI and Anthropic) truncate tool arguments above ~20KB, so binary files larger than that will arrive corrupted. For resumes specifically, prefer hires_create_candidate / hires_update_candidate with resume_text — the model parses the file from chat context and passes extracted text, avoiding the size limit entirely.
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  • Return a dasha (planetary period) timeline as a nested tree anchored on a date — past, present and future in ONE call. Works for BOTH planetary (graha) and sign (rasi) dasha systems; name the 'system' and the tool routes it automatically (default: vimsottari, anchored today, depth 3). Every period node has the same shape: 'level' (maha/antar/pratyantar/sookshma), 'ruler' (planet for graha, sign for rasi), 'start', 'end' (YYYY-MM-DD) and 'relation' (past/current/future). The result carries 'dasha_type' ('graha'/'rasi'), 'system', 'as_of', 'depth', a ready-made 'current' summary (with a 'path' and 'current_period_ends'), the full 'maha_timeline', and the expanded 'current_maha' → 'current_antar' → 'current_pratyantar' branches (rasi systems have two levels, no pratyantar; level 4 'sookshma' is graha-only). To drill into a SPECIFIC period regardless of date, pass 'maha' (and optionally 'antar') as a ruler name — it returns under 'selected_maha'/'selected_antar'. The full list of supported graha and rasi systems is the 'system' enum below. Set 'as_of_date' (YYYY-MM-DD, separate from birth 'date') to anchor on another time. Data only — no interpretation.
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  • Submit a photo or PDF of a receipt for processing. Covers requests phrased as 'log this', 'log this receipt', 'save this receipt', 'expense this', or 'add this to my expenses', including when the user simply shares a photo of a receipt or invoice. The receipt image is validated, uploaded to cloud storage, and processed by AI to extract vendor, amount, date, tax, and category. The expense appears in the user's spreadsheet in about 1-3 minutes, and longer for PDFs or large batches. Handles images and PDFs, mixed together in one batch. TO SEND FILES (preferred, and required for PDFs): call this tool with filesToUpload listing every file the user gave you. It returns one signed upload URL per file. Upload them ONE AT A TIME with an HTTP PUT, telling the user which file you just finished and how many remain, then call this tool ONCE with uploadRefs for all of them — that processes the whole set as a single batch, like the ExpenseBot web app. Do not call this tool once per file. Use the photo parameter for one image or PDF attached in ChatGPT. MCP clients that cannot supply file references may use photoBase64 for one small image; use the upload flow for large files or batches. Optional note and tag values use the same receipt metadata path as ExpenseBot's camera, file uploader, and forwarded-email intake. The note is stored in the Notes column (L); the tag is stored in the Tag column (K). Batch defaults apply to every file, and each uploadRefs item may override either value for that file.
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  • Step 1 of 2 for adding an image (PNG / JPEG / GIF / WebP) to the current team media library. This tool does NOT receive image bytes — it returns a short-lived presigned URL you upload the file to directly, so even large images never pass through this conversation. Workflow: (1) save the image to a local temp file; (2) if the file is larger than 5MB, compress / downscale it to 5MB or less FIRST (e.g. `sips -Z 2048 in.png --out out.jpg` on macOS, or `magick in.png -resize "2048x2048>" -quality 82 out.webp`) — uploads over 5MB are rejected; (3) call this tool with filename, mimeType and (optionally) fileSize; (4) HTTP PUT the temp file to the returned uploadUrl with the matching Content-Type header, e.g. `curl -X PUT --upload-file <file> "<uploadUrl>" -H "Content-Type: image/png"`; (5) call finalize_image_upload with the returned key; (6) delete the temp file. Max 5MB after compression. Only image/png, image/jpeg, image/gif and image/webp are accepted.
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  • Overwrite one existing file (Liquid/CSS/JS/JSON) on the LIVE (MAIN) Shopify theme — buyers render the change immediately. Approval-tier with expected_updated_at lock from get_shopify_theme_asset: refuses if the file changed since review, refuses unpublished themes (those use upsert_shopify_theme_file), and refuses creating a new live file. Use when the operator approves a single-file live-theme fix. Swapping the entire storefront is publish_shopify_theme. Routing: Shopify: overwrite one LIVE theme source file — approval-tier, lock-checked [outbound-tier — EVERY call needs a manager's approval (per-send human rail): each request queues its own approval card and sends exactly once on approve. There is no standing grant for this tool.]
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  • Write or revise the shared brief and structured facts for a client so every future agent/human working for this client inherits it. Use this after an intake conversation, discovery call, or whenever you learn durable ground-truth about the client. For one-off decisions/learnings, prefer log_client_decision. Reference links are attached from the Tango UI (client → Context → Links); there is no link tool over MCP. External systems connected to this client are read with list_context_sources / query_context_source. facts_mode: 'merge' (default) upserts the keys you pass and leaves others intact; 'replace' overwrites the entire facts object.
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  • Canonical GlobalGov coverage numbers: total and open solicitation counts, countries covered, source systems, languages, vendor counts. Pre-computed nightly and served from cache; if the cache is cold the tool returns a retryable 'computing' error and the refresh is dispatched automatically. No arguments.
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  • Search NVD for CVE vulnerabilities by product or component name. Returns CVE ID, description, severity, and CVSS score. Search terms are matched against CVE description text and EVERY word must appear, so pass the product name ("OpenSSL", "log4j", "nginx") optionally with a technical term ("buffer overflow") — not a plain-English question. Use when researching security threats or checking if a known vulnerability affects your systems.
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  • FEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use this tool to report issues it encounters during operation. REQUIRES: session_id, category, message OPTIONAL: user_email (for follow-up), user_name, source (default: 'mcp'), initiator ('user' or 'agent') Categories: bug_report, feature_request, general_feedback, question, security The 'initiator' field tracks who triggered the report: - 'user' — the user explicitly reported the issue or requested feedback submission - 'agent' — Riley detected an issue and initiated the feedback flow Examples: - User says 'the deploy button is broken' → submit_feedback(category='bug_report', message='...', initiator='user') - User says 'I wish it had dark mode' → submit_feedback(category='feature_request', message='...', initiator='user') - Deployment failed with Terraform error → submit_feedback(category='bug_report', message='Deployment failed: Terraform apply error on aws_alb resource — timeout waiting for ALB provisioning', initiator='agent')
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