"Software or tools developed using Java" matching MCP tools:

• purl_auditA

  ZEN SecDBA

  Perform a software package vulnerability audit using SecDB. ## What this tool does Analyzes a list of software packages identified by PURL (Package URL) and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided. ## When to use this tool Use this tool when the user wants to determine: - whether application dependencies contain known vulnerabilities - whether a project is affected by security advisories - which packages require patching or upgrading ## Supported ecosystems - **npm** - Node.js packages (e.g. pkg:npm/lodash@4.17.21) - **maven** - Java/JVM packages (e.g. pkg:maven/org.apache.logging.log4j/log4j-core@2.14.1) - **pypi** - Python packages (e.g. pkg:pypi/django@4.2.0) - **gem** - Ruby gems (e.g. pkg:gem/rails@7.0.0) - **cargo** - Rust crates (e.g. pkg:cargo/openssl-src@111.10) - **nuget** - .NET packages (e.g. pkg:nuget/Newtonsoft.Json@13.0.1) - **golang** - Go modules (e.g. pkg:golang/github.com/gin-gonic/gin@1.9.1) - **composer** - PHP packages (e.g. pkg:composer/symfony/symfony@6.4.0) ## Inputs - **purls**: list of Package URLs, one per entry. Generate them from your project manifest files: - Node.js: package.json / package-lock.json - Python: requirements.txt / Pipfile.lock / pyproject.toml - Ruby: Gemfile.lock - Go: go.mod / go.sum - Rust: Cargo.lock - PHP: composer.lock - Java: pom.xml / build.gradle - .NET: *.csproj / packages.lock.json ## Outputs - **report**: structured JSON objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable — always call this tool. - Only submit PURLs from the supported ecosystems listed above; others will be ignored. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.

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• how_munimji_helpsA

  HelloBooks AI AgentsA

  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_situation_timelineA

  clstrA

  Get the full chronological timeline of one situation. A situation is an ongoing storyline (story arc) that groups related news events over time; it carries a maintained summary plus every event in order, so you see not just what happened but how it developed. Each event is a cluster (one happening assembled from many outlets and deduplicated) with its date, a significance score (1 to 10, where 8 and above is exceptional), a source count, and a canonical clstr.news link. Use this to brief on a story's history or answer 'how did this develop'. Get a situation id from search_situations or get_top_situations, or from a clstr.news URL. Cite the returned URLs.

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• post_vastu_ayadiA

  Vastu Shastra MCP Server by RoxyAPIA

  Ayadi shadvarga calculator for a building or a room: send the dimensions and the API returns all six proportional formulas with the multiplier, the divisor and the remainder shown, the member each remainder names, and the two verdict rules the texts state. Choose between three text families, which genuinely disagree, and choose your own cubit length, because every remainder is unit sensitive. Built for practitioner tools, plan checking software and anyone who has to justify a dimension rather than assert it.

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• find_examplesA

  SenzingA

  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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• depwatch_previewA

  Signal OracleA

  Free preview of breaking changes / new releases for a software dependency. Pass an npm/PyPI `package` (resolved and fetched live if not already tracked) or a GitHub `repo` (owner/repo). Returns up to 5 recent changes plus the package's current version. Full history, significance filtering, and the LLM brief are paid via x402.

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• check_injectionA

  ContrastAPIA

  Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.

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• search_cpt_codesA

  Loa Healthcare PricingA

  Canonical code-lookup tool for this server. Search Loa's CPT/HCPCS index using exact codes, clinical terms, or consumer phrases. Use this first when the user does not already know the CPT code, before calling pricing tools.

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• get_solution_detailA

  Tuki — Travel Marketplace (Chile y Latinoamérica)A

  Get full detail for a Tuki solution: description, who it is for, capabilities, status and contact / CTA. Use after `list_solutions` or when the user asks about a specific Tuki product (WhatsApp Booking OS, boutique ticketing, rental inventory software, event post-sale, tailor-made tourism software).

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• list_makes_modelsA

  mcpA

  List the exact canonical car makes (brands) TransparentCars can search and price — or, given a make, that brand's models — using the exact strings the other tools expect. Call this FIRST (or whenever unsure of spelling) and pass the returned values verbatim into search_inventory / check_fair_price. No make = the list of brands; with a make = that brand's models.

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• findagent_list_tech_facetsA

  FindAgentA

  List the controlled Tech Domain and Language/Framework vocabularies (the software-development discipline facets). Pass chosen slugs as create_draft tech_domains[] / languages[]. Read-only; these are enrichment facets (unknown slugs are dropped). Prefer findagent_submission_wizard to walk the user through the tech step-by-step.

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• g2_reviewsA

  review-intelligence-mcp-serverA

  Get G2 software reviews. Returns ratings, pros, cons, use cases. Args: product: Software product name (e.g. 'Salesforce') max_results: Max reviews (default 20)

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• check_secretsA

  ContrastAPIA

  Scan source code (or snippet) for hardcoded secrets — cloud provider keys, API tokens, connection strings, private keys, passwords. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect leaked credentials before commit; for injection detection use check_injection. Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored. The generic password-assignment rule is suppressed when a more-specific credential rule fires on the same line — one targeted finding per leaked secret, not two.

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• search_occupationsA

  O*NET Occupational DataA

  Search O*NET occupations by keyword. Returns a list of occupations matching the keyword with their SOC codes, titles, and relevance scores. Use the SOC code from results with other O*NET tools to get detailed information. Args: keyword: Search term (e.g. 'software developer', 'nurse', 'electrician'). limit: Maximum number of results to return (default 25).

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• search_doisA

  DataciteA

  Search DataCite-registered DOIs (research datasets, software, etc.). Filter by free-text query, resource type, year, publisher, or affiliation, and SORT by relevance, recency, citations, downloads, or views — e.g. "most-downloaded climate datasets" (sort=downloads), "newest genomics datasets" (sort=recent), "most-cited datasets on X" (sort=citations). Returns DOI, title, creators, publisher, type, year, and citation/download/view counts.

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• workflows.trigger_invoiceA

  Admin SubstituteA

  Generate and send an invoice for a completed job. Auto-pushes to connected accounting software (Xero/QuickBooks/MYOB/FreshBooks), generates Stripe payment link, and notifies the customer via SMS. Full pipeline: invoice → accounting sync → payment link → customer notification → team alert.

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• how_munimji_helpsA

  HelloBooks AI Agents MCP ServerA

  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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• search_contentA

  Fluentive MCPA

  Searches across ALL Fluentive content — features, pricing, FAQ, comparisons, and live blog posts — for topics relevant to a query. Use for generic questions like 'does Fluentive support X?', 'is it good for Y type of business?', or 'I need software that does Z'. Returns the top 5 most relevant content excerpts.

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• get_ratio_pairA

  closelook-intelligenceA

  Closelook’s Ratio Chart presets: relative-performance facts for ten pairs — India vs US (Nifty 50 / S&P 500, INDA / SPY), EM vs US (EEM / SPY), developed ex-US vs US (EFA / SPY), Japan vs US (Nikkei / S&P), growth without tech (QQXT / QQQ), health care vs tech (XLV / XLK), low-vol vs momentum (SPLV / SPMO), gold vs US equities (GLD / SPY), bitcoin vs S&P 500. Per pair: current ratio, 1y change, all-time low/high with dates, percentile in its own history, distance from the low, lowest-since date. Weekly since each pair’s first common date. Mirrors closelook.net/lab/ratio/ (where any pair can be charted).

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• find_companiesA

  YouSpotA

  Search for companies by describing them in plain English — the same engine behind the ProspectFinder app. Handles industry, size, location, funding stage and investors in one query: 'CRM software companies with more than 1,000 employees', 'Series A AI startups in Boston', 'YC-funded fintechs'. Returns name, domain, employee count and description per company. Use this for discovering companies out in the world; for companies/people the user already knows, use their graph and connection tools instead. Results can then be added to the user's graph with the graph tools if they ask. The result includes a result_id that chat surfaces can use to render the list as a live prospect_list card.

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• get_category_disruption_signalA

  Stratalize IntelligenceB

  Use when assessing whether a software category faces near-term displacement risk, or timing a market entry or exit decision. Returns disruption risk score from 0 to 1 with evidence strings from citation volume patterns. Example: ERP category — disruption risk score 0.71, evidence: 34 citations referencing AI-native alternatives, 12 referencing no-code replacements — HIGH disruption risk for legacy on-premise vendors. Source: Stratalize citation volume heuristics.

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