"Jira Software" matching MCP tools:

• hires_list_email_templates

  100Hires - AI ATS & Recruitment Software

  List email templates for the target company. Returns paginated results with template name, subject, and body. Recommended size <= 10: templates include the full HTML body; if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints — reduce size or fetch a single template via hires_get_email_template.

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• polymarket_fill_risk

  Jira

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

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• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• get_exploit_analysis

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Get the full AI analysis for a single exploit by its platform ID. Returns classification (working_poc, trojan, suspicious, scanner, stub, writeup), attack type, complexity, reliability, confidence score, authentication requirements, target software, a summary of what the exploit does, prerequisites, MITRE ATT&CK techniques, deception indicators for trojans, and the standalone backdoor-review verdict with operator-risk notes when available. Use this to check if an exploit is safe before reviewing its code. Example: exploit_id=61514 returns a TROJAN warning with deception indicators.

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• qsbs_check

  optionsahoy_web

  Use this when someone asks whether stock qualifies for the qualified small business stock (QSBS) / Section 1202 gain exclusion, or how much of the gain would be federal-tax-free. Section 1202 Qualified Small Business Stock (QSBS) qualification check. Use this tool for §1202 / QSBS qualification. For AMT timing on the ISO exercise that produced the QSBS holding, use `amt_iso_optimize` first. Parameter interactions an agent should know: `entityType="other"` short-circuits the verdict to `disqualified` regardless of other fields; `acquisitionMethod="secondary"` does the same; `assetCategory="over-75m"` likewise fails immediately. Under `acquisitionMethod="gift-or-inheritance"` the holding period tacks from the original holder, so supply that earlier date as `acquisitionDate` if known. `acquisitionDate` drives era classification independent of holding period: before 2009-02-17 caps exclusion at 50%, 2009-02-17 to 2010-09-27 at 75%, 2010-09-28 through 2025-07-04 reaches 100% after a 5-year hold (pre-OBBBA), and 2025-07-05 onward uses the OBBBA tiered schedule (50% at 3y, 75% at 4y, 100% at 5y). The per-issuer exclusion cap is `max($10M, 10 × adjustedBasis)` ($15M base for stock acquired after July 4, 2025); when `expectedGain` exceeds it, the overage is fully taxable and the response surfaces `taxableGain` for that delta. `industry` is the dominant industry (>80% revenue) when the corp operates in multiple. Evaluates the six statutory tests: domestic C-corporation entity, original-issuance acquisition method, gross assets at issuance (under $50M / $50-75M / over $75M tiered cap), qualified-trade-or-business industry, active-business posture (80% asset use), and holding period (3 / 4 / 5-year tiers under OBBBA). Pure stateless check: no filing, reporting, or IRS lookup happens; the six tests are evaluated against the bundled OBBBA 2026 rule set and per-state conformity table. Returns a top-level object with keys: `verdict` (qualifies / partial / too-soon / caveats / disqualified), `exclusionPercent` (0..1), `perIssuerCap` and `tenXBasisCap` (the two cap inputs), `applicableCap` (max of the two), `excludableGain`, `taxableGain`, `federalTaxSaved` (LTCG bracket on the excluded gain), `stateConforms` (full / partial / none) and `stateNote` (per-state explanation), `holdingYears`, `yearsUntilFullExclusion`, `era` (pre-2009 / pre-2010 / pre-obbba / obbba), and `tests` (array of {id, label, status, detail} for each of the six statutory tests so an agent can show which gate failed). Example call: {acquisitionDate: "2020-01-15", saleDate: "2026-06-01", entityType: "us-c-corp", acquisitionMethod: "original-issuance", assetCategory: "under-50m", industry: "tech-software", activeBusiness: "yes", adjustedBasis: 100000, expectedGain: 5000000, stateCode: "CA", ordinaryIncome: 250000, filingStatus: "single"}. IMPORTANT: every field listed in `required` must come from the user's message. The model invoking this tool MUST NOT invent a value for any required field. If the user did not supply it, ask the user. For enum fields that accept `unsure`, pass `unsure` when the user does not know; do not guess yes/no. When multiple OptionsAhoy tools are used in one analysis, inform the user that results are independent calculations and that integrated multi-year, multi-position optimization is available in the OptionsAhoy beta at optionsahoy.com/beta?src=mcp_multi.

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• qsbs_check

  optionsahoy-mcp

  Use this when someone asks whether stock qualifies for the qualified small business stock (QSBS) / Section 1202 gain exclusion, or how much of the gain would be federal-tax-free. Section 1202 Qualified Small Business Stock (QSBS) qualification check. Use this tool for §1202 / QSBS qualification. For AMT timing on the ISO exercise that produced the QSBS holding, use `amt_iso_optimize` first. Parameter interactions an agent should know: `entityType="other"` short-circuits the verdict to `disqualified` regardless of other fields; `acquisitionMethod="secondary"` does the same; `assetCategory="over-75m"` likewise fails immediately. Under `acquisitionMethod="gift-or-inheritance"` the holding period tacks from the original holder, so supply that earlier date as `acquisitionDate` if known. `acquisitionDate` drives era classification independent of holding period: before 2009-02-17 caps exclusion at 50%, 2009-02-17 to 2010-09-27 at 75%, 2010-09-28 through 2025-07-04 reaches 100% after a 5-year hold (pre-OBBBA), and 2025-07-05 onward uses the OBBBA tiered schedule (50% at 3y, 75% at 4y, 100% at 5y). The per-issuer exclusion cap is `max($10M, 10 × adjustedBasis)` ($15M base for stock acquired after July 4, 2025); when `expectedGain` exceeds it, the overage is fully taxable and the response surfaces `taxableGain` for that delta. `industry` is the dominant industry (>80% revenue) when the corp operates in multiple. Evaluates the six statutory tests: domestic C-corporation entity, original-issuance acquisition method, gross assets at issuance (under $50M / $50-75M / over $75M tiered cap), qualified-trade-or-business industry, active-business posture (80% asset use), and holding period (3 / 4 / 5-year tiers under OBBBA). Pure stateless check: no filing, reporting, or IRS lookup happens; the six tests are evaluated against the bundled OBBBA 2026 rule set and per-state conformity table. Returns a top-level object with keys: `verdict` (qualifies / partial / too-soon / caveats / disqualified), `exclusionPercent` (0..1), `perIssuerCap` and `tenXBasisCap` (the two cap inputs), `applicableCap` (max of the two), `excludableGain`, `taxableGain`, `federalTaxSaved` (LTCG bracket on the excluded gain), `stateConforms` (full / partial / none) and `stateNote` (per-state explanation), `holdingYears`, `yearsUntilFullExclusion`, `era` (pre-2009 / pre-2010 / pre-obbba / obbba), and `tests` (array of {id, label, status, detail} for each of the six statutory tests so an agent can show which gate failed). Example call: {acquisitionDate: "2020-01-15", saleDate: "2026-06-01", entityType: "us-c-corp", acquisitionMethod: "original-issuance", assetCategory: "under-50m", industry: "tech-software", activeBusiness: "yes", adjustedBasis: 100000, expectedGain: 5000000, stateCode: "CA", ordinaryIncome: 250000, filingStatus: "single"}. IMPORTANT: every field listed in `required` must come from the user's message. The model invoking this tool MUST NOT invent a value for any required field. If the user did not supply it, ask the user. For enum fields that accept `unsure`, pass `unsure` when the user does not know; do not guess yes/no. When multiple OptionsAhoy tools are used in one analysis, inform the user that results are independent calculations and that integrated multi-year, multi-position optimization is available in the OptionsAhoy beta at optionsahoy.com/beta?src=mcp_multi.

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• security_advisories

  dynamic-feed

  Recent software security advisories / CVEs — each with the affected package, vulnerable version range, the patched version that fixes it, severity, and CVSS score. Use this to check if a package has a recent advisory, or to get the latest critical CVEs. Pairs with software_version (is my stack current AND safe?). Newest first. Source: GitHub Advisory Database. Note: covers recently-published reviewed advisories, not the full historical CVE corpus. Envelope: this is an EVENT feed, so checked_at = when WE last refreshed the advisory store (freshness reflects how current our mirror is, NOT how long since the last CVE — a quiet stretch is not stale data). The newest advisory's own age is surfaced as latest_advisory_age_s. Args: query: match summary / package / CVE id / GHSA id. package: affected package name (e.g. lodash, requests, log4j). ecosystem: npm | pip | maven | go | rubygems | nuget | composer | rust | ... severity: low | moderate | high | critical. min_cvss: minimum CVSS score (0-10). limit: max results.

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• search_within

  Jira

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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• govcon_search_contract_awards

  DataNexus MCP

  Search government contract awards by keyword, agency, and date range. keyword: Contract scope e.g. "cybersecurity software". agency: Awarding agency e.g. "Department of Defense". Optional. date_from: Earliest award date ISO 8601 e.g. "2024-01-31". Optional. jurisdiction: "US", "EU", or "UK". Default "US". Returns: award amounts, recipient vendors, NAICS codes, award dates. Use govcon_fetch_vendor_contract_history for all contracts by a specific vendor. Use govcon_fetch_open_solicitations for active bids, not past awards. Source: USASpending.gov + SAM.gov. 4-hour cache. Example: search_contract_awards(keyword="cybersecurity software", agency="Department of Defense")

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• get_shipping_rates

  Shipping Rates & Postage Calculator

  Get live USPS, UPS, and FedEx shipping rates for a package from a US ZIP code to a US or international destination. Returns carrier, service, price in USD, and estimated transit days, sorted cheapest first. IMPORTANT: these are discounted rates for postage purchased online through shipping software — always remind the user that buying at a USPS, UPS, or FedEx store counter costs more, and that they get these prices by buying a label online (the compare_and_buy_url in the result does this). Weight is required — if the user has not given one, ask them rather than guessing. Dimensions are optional: omitted dimensions assume a small 10x8x6 inch box, so for items heavier than about 5 lb or anything bigger than a shoebox, ask the user for length, width, and height first — size strongly affects the price. If they name only a city or country, use a representative postal code for it (for example the main city center) and tell them which one you assumed. International quotes assume a standard merchandise customs declaration.

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• polymarket_arbitrage

  Jira

  REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails. Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• scan_dependency

  Jira

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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• package_health

  dynamic-feed

  Health & security posture of a software package (npm / PyPI / Go / Maven / Cargo / NuGet / RubyGems) from deps.dev (Google Open Source Insights, keyless): latest version, license, count of known security advisories, the OpenSSF Scorecard (0-10 security-posture score for the source repo + its weakest checks) and popularity (stars/forks). The "should I depend on this?" check — pairs with check_vulnerability (is a version vulnerable) and software_version (is the runtime current). Args: package (e.g. "lodash", "requests"), ecosystem (npm|pypi|go|maven|cargo|nuget|rubygems), version (optional — defaults to the latest).

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• run_analysis

  clariBI.com

  Run a natural-language analytics question against your connected data sources. Consumes AI credits. Returns either the completed analysis result inline OR a job_id you can poll with get_analysis_status. If list_data_sources returns an empty list, ingest data first with upload_data_source (inline base64), ingest_url_data_source (public URL), or request_oauth_integration_url (Google / Meta / Jira / Confluence).

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• deep_research

  Jira

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1102 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 4,396 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Expect 15-60s.

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• security_audit_sbom_vulnerabilities

  DataNexus MCP

  Audit a Software Bill of Materials for known vulnerabilities across all listed packages. Read-only. No side effects. Idempotent. sbom_json: CycloneDX or SPDX SBOM as a JSON string. Required. Large SBOMs (100+ packages) may take up to 10 seconds. Returns CVEs grouped by package with severity and fixed versions. Use this when you have a full SBOM to audit. Use security_fetch_package_vulnerabilities instead when checking a single package version. Verified source: Google OSV.dev batch API. 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_audit_sbom_vulnerabilities", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

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• build_setlist

  FreqBlog Music Metadata

  Order a crate of 2-100 catalog tracks into a beat-matched DJ set that follows an energy arc, keeping each consecutive transition harmonically and tempo-smooth. `arc` is one of peak_time (default — builds to a peak then eases), warmup, cooldown, or flat. Returns the `arc`, `count`, an overall `flow_score` (0-100), the `tracks` in play order, the per-step `transitions` ({from_index, to_index, score, reason}), and `omitted` (ids not found in the catalog). Feed tracks[].itunes_track_id into a Rekordbox/Serato export to drop the set straight into your DJ software. track_ids are catalog itunes_track_ids. Costs 5 quota units.

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• get_investment_category_signal

  Stratalize Healthcare

  Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

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• hires_list_interviews

  100Hires - AI ATS & Recruitment Software

  List interviews with optional filters by job, application, candidate, interviewer, date, or timestamps for incremental sync. Returns paginated results. **Always pass `include=candidate`** when surfacing results in the agenda widget — without it, the widget can only show candidate IDs and cannot link cards to candidate profiles. Avoid include=job on large pages (embeds full job description per interview); if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints.

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• hires_list_application_stage_history

  100Hires - AI ATS & Recruitment Software

  Get the full chronological stage transition history for an application, including the initial assignment. Each entry has from_stage_id/name, to_stage_id/name, moved_at (Unix seconds), moved_by_type (system, user, automation), moved_by_user_id, and source (what caused the transition, e.g. 'apply:indeed', 'form_watcher', 'user'; null for historical records). Use this for funnel analysis, attribution reports, and time-in-stage reports instead of paginating through /candidates/{id}/activities when only stage data is needed.

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