"Parsing and Unparsing CSV Files for AI Context Use" matching MCP tools:

• moxie.search_docs

  Moxie Docs

  Keyword and semantic search across the connected repository's generated docs, conventions, documentation gaps, AI-context notes, and indexed code. Read-only; no side effects. Returns ranked matches in Markdown grouped into Documentation and Code sections, each with a title, snippet, and source paths. Use for open-ended lookups when you don't know which category holds the answer; when you do, the specific getters (get_conventions, get_doc_gaps, get_documentation_opportunities) are more direct. Omitting query returns recent context instead.

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• improve_my_agent_with_axis

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  Analyze an agent codebase and return a prioritized AXIS hardening plan. Requires Authorization: Bearer <api_key>; this creates a snapshot and may return auth, quota, file-limit, or validation errors. Example: pass your agent source files to see missing AGENTS.md, CLAUDE.md, and MCP config gaps. Use this when you want recommendations and missing-context detection. Use analyze_files instead when you want the full artifact bundle directly.

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• analyze_company

  Nordic Financial MCP

  AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'

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• user_agent_parse

  drwho.me

  Context lookup: Parse a User-Agent header string into structured browser, OS, device type, and rendering-engine components. Use to identify client capabilities from a raw UA string, e.g. when analysing server logs or request headers; does not perform any network lookups — entirely local parsing. Runs synchronously using the ua-parser-js library with no external calls. Returns a JSON object with browser.name, browser.version, os.name, os.version, device.type, device.vendor, and engine.name fields; unknown fields are empty strings.

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• plan_file_info

  PlanExe

  Returns file metadata (content_type, download_url, download_size, expires_at) for the report or zip artifact. Use artifact='report' (default) for the interactive HTML report (~700KB, self-contained with embedded JS for collapsible sections and interactive Gantt charts — open in a browser). Use artifact='zip' for the full pipeline output bundle (md, json, csv intermediary files that fed the report). While the task is still pending or processing, returns {ready:false,reason:"processing"}. Check readiness by testing whether download_url is present in the response. Once ready, present download_url to the user or fetch and save the file locally. Download URLs expire after 15 minutes (see expires_at); call plan_file_info again to get a fresh URL if needed. Terminal error codes: generation_failed (plan failed), content_unavailable (artifact missing). Unknown plan_id returns error code PLAN_NOT_FOUND.

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• analyze_qbo_journal_anomalies

  HelloBooks AI Agents MCP Server

  Scan a QuickBooks Online "Journal Entries" CSV export for anomalies — currently round-number lines (debit or credit amounts that are exact multiples of $1,000, above a $1,000 materiality threshold). Round numbers are statistically rare in real bookkeeping and frequently indicate estimates, plugs, or fraud signals worth review. Input is raw CSV text from QBO Reports → Accountant → Journal. Max 5,000 rows; max 5 MB. Returns flagged lines with severity ($100K+ high, $10K+ medium, else low) and a shareable URL. Use this when a user pastes QBO data and asks "any anomalies?", "look for round numbers", or "anything suspicious". Tier-0 subset — HelloBooks Phase 3.0 anomaly detection in the paid product additionally catches GL outliers vs entity history, vendor-history mismatches, archived-vendor activity, and AI-narrated suspicious lines (which require the live HelloBooks account).

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• export_dataset

  DC Hub — Data Center & Energy Intelligence

  Use when a user wants to pull their saved DC Hub shortlist OUT of the platform for offline analysis, a spreadsheet, or ingestion into another tool (PRO). Example: "Export my saved sites as GeoJSON for QGIS." — export_dataset format=geojson. Params: format ("csv" default, or "geojson"). Returns: the full file contents as text — CSV rows or a GeoJSON FeatureCollection of your saved sites with DCPI score, target MW, market, coordinates, and notes. Do NOT use to list sites in-chat (use list_saved_sites) or to save a new one (use save_site); this is the bulk-download path.

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• intel_inject

  TunnelMind Data API

  Fetches a domain's homepage and checks for content patterns that could constitute prompt injection attacks against AI agents that visit and ingest the page. Signals include hidden text, invisible divs, `<!-- AI: ignore -->` style comments, and known injection patterns. Use this tool when: - You are vetting a domain before feeding its content into an LLM context. - You want to assess the prompt injection risk of a URL before browsing it with an agent. - You are auditing a set of domains for adversarial AI content. Do NOT use this tool when: - You want tracker surveillance data — use `get_domain` instead. - You want AI training opt-out signals — use `intel_optout` instead. - You want the agent surface (MCP/OpenAPI) — use `intel_agent` instead. Inputs: - `domain` (query, required): Domain to scan. Returns: - `injection_signals`: list of signal types detected (e.g., `hidden_text`, `ai_instruction_comment`, `invisible_div`). - `risk_level`: `none`, `low`, `medium`, or `high` based on signal count and type. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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• x711_code_sandbox

  x711 — Universal Agent Gas Station

  Execute JavaScript or Python code in an isolated sandbox. Use for: data processing, math, CSV parsing, JSON transformation, crypto calculations, algorithm testing. Secure — no filesystem access, no network. Returns: { output: string, runtime_ms: number, language: string }. Requires API key.

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• axint.compile

  Axint

  Compile TypeScript source (defineIntent() call) into native Swift App Intent code. Returns { swift, infoPlist?, entitlements? } as a string — no files written, no network requests. On validation failure, returns diagnostics... Use: use when TypeScript DSL source should become Swift; use validate for cheaper preflight only. Effects: read-only generated Swift/diagnostics; writes no files and uses no network.

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• export_data

  aTars MCP

  USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rows

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• deltasignal_spectra_field_map

  DeltaSignal ATLAS-7

  Use this read-only tool to retrieve the SPECTRA historical field-map contract for one crypto public company ticker. It returns issuer-specific filing choreography and pressure-map context used by DeltaSignal report and visualization workflows. Parameters: ticker is required and must be one public-company symbol such as RIOT, MARA, COIN, MSTR, HUT, or CLSK. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not write files, wallets, orders, or account state. Use it when the user asks for SPECTRA, field-map, historical pressure, filing choreography, or report-visualization context for a named issuer.

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• get_dchub_recommendation

  DC Hub — Data Center & Energy Intelligence

  Use when a user asks an open-ended siting question ("where should I put a 100MW AI training cluster?") and you want ONE call that returns a ready-to-quote answer instead of orchestrating 5+ separate tools. Example: "Where should I site a 100MW AI training campus in Texas with short time-to-power?" — get_dchub_recommendation context="100MW AI training campus in Texas". Params: context free-text describing the user request (MW, geography, workload, deadline, constraints). Returns: {top_markets:[{slug, name, verdict (BUILD/CAUTION/AVOID), composite_score, excess_power_mw, time_to_power_months, why}], candidate_facilities[], factor_breakdown:{fiber, grid, water, tax, climate}, summary_text (LLM-quotable, CC-BY-4.0), citation_url}. Do NOT use for a single specific lat/lon (use analyze_site) or to rank by ONE criterion only (use rank_markets).

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• list_filing_source_files

  ca-rate-filings

  Lists the **source files** (PDFs, XLS spreadsheets, DOC manuals, ZIP archives) ingested for a SERFF id. Returns metadata only — name, size in bytes, MIME-class type (`pdf` / `spreadsheet` / `document` / `csv` / `archive` / `other`), file extension, modified timestamp. Pair with `get_filing_source_file_link` to mint a signed download link the user can click — list names here, mint a link there. Use this to: - triage a filing whose summary looks thin ("did we even ingest the right files?"), - discover the XLSM rater / rate manual PDF / rating-samples spreadsheet for a filing, - confirm which artefacts a filing actually shipped (e.g. is there a separate rate manual XLS, or just the PDF?). Returns `{ error: ... }` if no source files exist for the SERFF id.

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• get_inbound_mail

  mailbox

  Get one forwarded inbound mail item with compact draft_context by default. Use this before drafting an outbound reply when you need sender context, reply contact candidates, deadline clues, source files, and thread linkage in one stable payload.

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• settle

  discovery

  <tool_description> Settle pending payments for media buys. Supports manual CSV export, Stripe invoice (Phase 2 stub), and x402 micropayments (Phase 2 stub). </tool_description> <when_to_use> When a publisher wants to collect earned revenue or an advertiser needs to settle outstanding charges. Use method='manual' for CSV export. Stripe and x402 are stubs (Phase 2). </when_to_use> <combination_hints> get_campaign_report → settle (after verifying amounts). Filter by media_buy_id, publisher_id, or period. </combination_hints> <output_format> Settlement totals (gross, platform fee, net), entry count, and method-specific data (CSV for manual). </output_format>

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• get_dchub_recommendation

  DC Hub — Data Center Intelligence MCP Server

  Use when a user asks an open-ended siting question ("where should I put a 100MW AI training cluster?") and you want ONE call that returns a ready-to-quote answer instead of orchestrating 5+ separate tools. Example: "Where should I site a 100MW AI training campus in Texas with short time-to-power?" — get_dchub_recommendation context="100MW AI training campus in Texas". Params: context free-text describing the user request (MW, geography, workload, deadline, constraints). Returns: {top_markets:[{slug, name, verdict (BUILD/CAUTION/AVOID), composite_score, excess_power_mw, time_to_power_months, why}], candidate_facilities[], factor_breakdown:{fiber, grid, water, tax, climate}, summary_text (LLM-quotable, CC-BY-4.0), citation_url}. Do NOT use for a single specific lat/lon (use analyze_site) or to rank by ONE criterion only (use rank_markets).

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• alya_gem_appraise

  Alya Hub

  Trigger a Grok-AI gemological appraisal of a single gem on GemHunt (https://gemhunt.app — Father's gem-discovery platform). Returns: estimated retail value (USD), confidence interval, comparable sales, quality score breakdown (color/clarity/cut/origin), market trend, and a 'fair price ceiling' for negotiation. Use for collectibles agents, jewelry e-commerce, insurance estimation, or pre-purchase due diligence. Premium ($0.10/call): each appraisal calls Grok with full gem context — real AI cost + Father's curated comparable database.

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• ateam_github_patch

  ateam-mcp

  Edit a file in the solution's GitHub repo and commit. Two modes: 1. FULL FILE: provide `content` — replaces entire file (good for new files or small files) 2. SEARCH/REPLACE: provide `search` + `replace` — surgical edit without sending full file (preferred for large files like server.js) Always use search/replace for large files (>5KB). Always read the file first with ateam_github_read to get the exact text to search for. DEFAULTS TO `dev` BRANCH — writes don't touch prod. Use ateam_github_promote to ship dev→main when ready. Pass ref:'main' only for emergency hotfixes.

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