"Understanding Batch Processing in Computing or Operations" matching MCP tools:

• security_fetch_package_vulnerabilities

  DataNexus MCP

  Fetch all known CVEs for an open source package version or a batch of packages. Read-only. No side effects. Idempotent. Single-package mode: package (e.g. requests), version (e.g. 2.28.0), ecosystem (PyPI/npm/Maven/Go/Cargo/NuGet/RubyGems). Batch mode: packages array of {name, version, ecosystem} objects — max 50 per call. If packages array is provided and non-empty, batch mode is used and package/version/ecosystem are ignored. Batch returns {results: [...], partial: bool, failed_count: int}. Each result has vuln_count and vulnerabilities list. Returns CVE ID, severity, CVSS score, affected range, and fixed version. Use security_fetch_cve_detail for full detail by CVE ID. Use security_audit_sbom_vulnerabilities for SBOM files. Verified source: Google OSV.dev. 1-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="security_fetch_package_vulnerabilities", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

  Connector

• x_read

  twitr.sh

  Read live X/Twitter data: look up a single tweet or user profile, batch-read up to 100 tweets or users at once, check whether one account follows another, fetch trending topics by region, extract a long-form article, or download tweet media. Pay per call in USDC — no API key, no signup; failed calls are never charged. Guidance: resource=get-tweet|get-user (id), batch-tweets|batch-users (ids array, ≤100), check-follower (source+target usernames), trends (optional woeid/count), article (tweet id), followers-you-know (id + mandatory resultsLimit), download-media (id or ids ≤50 — returns media file URLs on THIS origin; links live 7 days). Fixed-price per call/id except followers-you-know which is per-result.

  Connector

• save_template

  Uwear

  Create a canonical company template, or replace one by passing template_id with expected_revision. Send typed GenerationCommand objects; updates are complete replacements. This persists the template and does not generate images. If a generation_preset create fails structurally, or legacy keys appear (camera_quantities, step2_operations, operations, fixed_context, variable_context, input_policy, cameraQuantities, step2Operations, fixedContext, variableContext, inputPolicy, or their camelCase forms), these describe a batch workflow — use `template_kind=batch_workflow`. front + back is one batch workflow: Step 1 front generation, Step 2 back camera edit.

  Connector

• submit_receipt

  ExpenseBot

  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. Only use the photo parameter for a single small image whose base64 you can reliably include in full — large base64 payloads are frequently dropped in transit, which silently loses the receipt. 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.

  Connector

• resolve_names

  Google Maps

  Resolves a batch list of specific location queries (landmark names or exact addresses) into canonical Google Maps Place IDs. **Input Requirements (CRITICAL):** 1. **`queries` (array of objects - MANDATORY):** A list of location queries to resolve. You may specify up to 20 queries. * **Each query object must have:** * **`text` (string - MANDATORY):** The text query representing a specific place name or address to resolve. * **Examples:** `'Googleplex, Mountain View, CA'`, `'1600 Amphitheatre Pkwy, Mountain View, CA'`, `'Eiffel Tower, Paris'`. 2. **`location_bias` (object - OPTIONAL):** Use this to prioritize results near a specific geographic area. * **Format:** `{"viewport": {"low": {"latitude": [value], "longitude": [value]}, "high": {"latitude": [value], "longitude": [value]}}}` 3. **`region_code` (string - OPTIONAL):** The Unicode CLDR region code (two-letter country code, e.g., `US`, `CA`) of the user to bias the results. **Instructions for Tool Call:** * Specificity (CRITICAL): Queries must represent a specific place name or address. General searches like `'restaurants'` or chain names like `'Starbucks'` are not supported. * Do NOT call this tool if the downstream tools you plan to invoke already accept raw address or place name strings directly. **Error Handling (CRITICAL):** * This is a batch processing tool. A request might return "mixed results" (e.g. some queries resolve successfully while others fail). * The output list of `results` is guaranteed to map 1:1 with the input `queries` indices. A failed query will result in an empty `Result` message (no `entity` is set) at its corresponding index in the `results` list. * You **MUST** check the `failed_requests` map field in the response to identify which specific query index failed. The key of `failed_requests` represents the 0-based index of the failed query in the request. Do not assume the entire batch call failed because of a partial failure.

  Connector

• 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.

  Connector

• searchPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

  Connector

• searchCollaborators

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".

  Connector

• relm_batch

  Relm CRM

  Run up to 100 record writes in ONE call (contact/company/deal/activity) - the fast path for imports/migrations. operations: [{object, method:create|update|delete, data|patch, id?}]. Returns a per-op result array (partial success). Every op still counts against your quota (batch saves round-trips, not quota).

  Connector

• searchJobs

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".

  Connector

• list_api_reference_pages

  docs-mcp

  List pages in Redpanda API reference documentation. Returns endpoints, schemas, and topic pages with URL, title, type, and description. SCOPING (important for accurate results): - api="all" or omit: Lists all available APIs - api="admin": Cluster management operations (brokers, partitions, configs, users) - api="cloud-controlplane": Redpanda Cloud resource management (clusters, networks, namespaces) - api="cloud-dataplane": Cloud cluster data operations (topics, ACLs, connectors) - api="http-proxy": Kafka operations over HTTP (produce, consume, offsets) - api="schema-registry": Schema management (register, retrieve, compatibility) Use this to browse API structure. For general Redpanda docs, use ask_redpanda_question instead.

  Connector

• get_analysis_status

  hypathesis

  Check the processing status of an uploaded paper. Poll this tool after uploading a PDF until status is 'Ready' before calling get_variable_relationships. Args: file_id: The file_id returned by the /upload endpoint. authorization: Optional. API key as 'Bearer hk_...' or 'hk_...'. Returns: { "status": "Processing" | "Ready" | "Empty" | "Ineligible" | "Pending", "edges_count": int, "variables_count": int }

  Connector

• apply_actions

  FlowCastle

  Validate and apply a batch of flow-builder actions — the single write path for editing flows, blocks, variables, broadcasts, sequences, and folders. Call this directly; a separate validate_actions call beforehand is unnecessary. Broadcasts have no dedicated tool and are managed here: create_broadcast makes a DRAFT (it owns its flow via data.flowId — add the message blocks in the same batch, no separate create_flow), optionally with create_recurrence_schedule + attach_recurrence_to_broadcast for recurring; a later update_broadcast with status SCHEDULED (and scheduledAt for one-shots) is what actually schedules/sends it. The full recipe is in get_action_schema under `broadcasts`. DESTRUCTIVE: the batch may include delete_block, delete_link, delete_flow, and delete_variable. Confirm with the user before applying deletions. IRREVERSIBLE SIDE EFFECTS: run_operation starts a real operation run, which may send broadcasts to real contacts and write application variables. It cannot be undone or recalled, is not idempotent, and is available only through this tool — confirm with the user before applying a batch containing one, and never blindly retry a timed-out call that did. Validation always runs first and an invalid batch applies nothing. Execution is NOT atomic, however: if an action fails mid-batch, the actions before it stay applied and execution stops — re-read state with get_flow_context before retrying rather than blindly resending the batch. Not idempotent — resending a batch of create_* actions creates duplicates. Read get_action_schema for the action contract and get_design_guidelines before any structural edit. Returns { success, changes, errors, warnings, actionId } plus an idRemap mapping placeholder ids to the real ids that were created.

  Connector

• get_platform_stats

  GlobalGov — Government Procurement Intelligence

  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.

  Connector

• getMarketOverview

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Get a snapshot of the quantum computing landscape — no parameters needed. Use when the user asks broad questions like "how's the quantum job market?", "what are trending topics?", or wants an overview of the quantum computing industry. Returns: total active jobs, top hiring companies, jobs by role type, papers published this week, total researchers tracked, and trending technology tags. For specific job/paper/researcher searches, use the dedicated search tools instead.

  Connector

• generate_batch

  machfive

  Submit multiple leads for batch email sequence generation (ASYNC). Returns IMMEDIATELY with a list_id. Processing runs in the background. After calling this, poll get_list_status every 15-30 seconds until processing_status is 'completed' or 'failed', then call export_list. You must have a campaign_id first. Call list_campaigns if you don't have one.

  Connector

• get_list_status

  machfive

  Check the processing status of a lead list. Use this to POLL after calling generate_batch. Call every 15-30 seconds until processing_status is 'completed' or 'failed'. When completed, call export_list. When failed, submit a new batch.

  Connector

• check_urls

  Unphurl

  Check multiple URLs in a single batch. Returns results for all URLs, handling async processing automatically. Each URL is analysed across seven dimensions: redirect behaviour, brand impersonation, domain intelligence (age, registrar, expiration, status codes, nameservers via RDAP), SSL/TLS validity, parked domain detection, URL structural analysis, and DNS enrichment. Known and cached URLs return results immediately. Unknown URLs are queued for pipeline processing. This tool automatically polls for results until all URLs are complete or the 5-minute timeout is reached. You don't need to manage polling or job tracking. If the timeout is reached before all results are complete, returns whatever is available with a clear message indicating which URLs are still processing. The user can check results later via check_history. Maximum 500 URLs per call. For larger datasets, call this tool multiple times with chunks of up to 500 URLs. Billing: Same as check_url. Known and cached domains are free. Only unknown domains running through the full pipeline cost 1 credit each. The summary shows pipeline_checks_charged (the actual number of credits consumed). If you don't have enough credits for the unknowns in the batch, the entire batch is rejected with a 402 error telling you exactly how many credits are needed. Duplicate URLs in the list are automatically deduplicated (processed once, charged once). Invalid URLs get individual error status without rejecting the batch. Use the "profile" parameter to score all results with custom weights.

  Connector

• format_batch

  Caliper

  Analyze multiple geometry files in a single batch request. Submit up to 10 files, receive a single quote, pay once, and get structured metadata for all files. Supports mixed formats. Read-only analysis — does not modify, convert, or repair files. Payment is required via x402 (USDC on Base) or card via MPP (Stripe). If no payment is provided, the response includes the total price and per-file breakdown. Retry with the payment argument containing "transaction", "network", and "priceToken". Partial success: if some files fail processing, you still receive results for the files that succeeded. Privacy policy: https://caliper.fit/privacy

  Connector

• validate_data_safety_lite

  Data Compliance Classifier MCP

  Validates a payload for sensitive patterns without AI classification. Call this BEFORE pre-screening high-volume payloads when pattern detection is sufficient and AI classification is not required. Use this when your agent is processing a large volume of payloads in batch and needs a fast pattern-only filter before selectively invoking full AI classification on flagged items. Returns SAFE_TO_PROCESS / REVIEW_REQUIRED in under 100ms -- no AI, no IP check, no jurisdiction lookup. Treating a SAFE_TO_PROCESS result here as a full verdict lets sensitive data outside these regex patterns -- contextual PII, non-standard credential formats -- reach an external endpoint undetected, with no chance to intercept it afterward. Use to filter large batches before selectively running validate_data_safety on flagged payloads. Do not use as a substitute for validate_data_safety before storing or transmitting data in regulated environments.

  Connector