"A debugger for reverse engineering and software analysis" matching MCP tools:

• verify_content_credentials

  Lumethic Photo Verification

  Verify a single image's authenticity — use this when you only have the image and no RAW camera file. Checks its embedded Content Credentials (C2PA) for capture provenance and AI-generation flags, and runs advisory forensic screens (error-level analysis, double-JPEG artifacts, EXIF timestamp consistency, editing-software traces, screen recapture). Free: it does not consume your verification quota. Provide the image inline as image_base64, or — for large files — call create_verification_upload and pass the returned image_object_key. Returns a verification id; poll get_verification, which on completion includes a structured evidence_report (verdict, per-check findings, coverage). Works without an API key on the keyless anonymous tier (rate-limited; returns an anonymous_user_id to reuse). For the strongest forensic check, use verify_photo with a RAW + JPEG pair instead.

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• how_munimji_helps

  HelloBooks AI Agents

  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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• submit_rfq

  Commonlands Optics: M12 Lens and C-Mount Lens Finder + Field-of-View Calculator

  Forward a buyer request-for-quote or engineering question to the Commonlands engineering team. Two-step, buyer-confirmed: the first call returns a preview and sends nothing; show the buyer the preview (including their reply-to email) and, only after they explicitly approve, call again with confirm: true to send. The recipient is fixed to the Commonlands inbox (the agent cannot choose it); this only sends an inquiry and never creates an order, charges a card, or writes Shopify/customer data. Include part numbers, sensor, quantity, and application when known so the team can reply with a quote. Commonlands replies by email.

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• pentest_lookup_group

  pentest-mcp-server

  Look up a MITRE ATT&CK threat group (intrusion set) or software entry by name or ID for authorized penetration testing and threat intelligence. Returns the group or software record: ATT&CK ID, display name, known aliases, type (group vs. software), description, and the techniques it uses with procedure-level context from public ATT&CK reporting. Accepts exact ATT&CK IDs (G0007 for threat groups, S0002 for software) or keyword/name search (e.g., "APT28", "Mimikatz", "Lazarus Group"). Equally useful for defenders building detection coverage around specific adversary tradecraft.

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• malware_get_template

  website-search

  Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.

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• nausika_geocode

  Nausika

  Resolve a place 'query' to coordinates (forward) or find nearest places to 'latitude'+'longitude' (reverse). Mediterranean-focused curated DB; forward falls back to OSM/Nominatim globally. Each result carries a 'source' discriminator ('local' for the curated marine DB, 'osm' for the global fallback). Returns name, type, coords, source, plus similarity (forward) or distance_m (reverse). Example forward: query="Portofino". Example reverse: latitude=44.3, longitude=9.21, radius_m=50000. Chain into nausika_marine_forecast, nausika_tides, nausika_search_places, or nausika_sea_route using the returned coords.

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• lookup_lei

  nordic-data

  Look up a Legal Entity Identifier (LEI) via GLEIF — the global standard for entity identification. Returns legal name, registered address, status, parent + ultimate parent relationships, and child entities (subsidiaries). Also supports reverse lookup from a national company number to LEI across 15 countries (DK, NO, SE, FI, IE, UK, FR, DE, CZ, PL, LV, EE, NL, BE, LU). Tier note (reverse mode only): NL and DE use paid upstream registries — free-tier API keys receive HTTP 402 'upgrade_required'; do NOT retry on 402.

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• recipes_search

  mcp

  Find a step-by-step setup recipe matching a user's intent — installing, configuring, or deploying anything on a Linux VM (web servers, databases, container runtimes, dev environments, networking, security tooling, ML stacks — any software setup an LLM agent might be asked to perform). ALWAYS call this FIRST when a user asks to set up, install, deploy, run, host, or spin up any service or software on a Massed Compute VM, BEFORE calling instances_launch. The recipe returned will include the correct VM size, image, and step-by-step instructions the user expects you to follow. Examples that should trigger this tool: 'I want an nginx reverse proxy', 'spin up a jupyter notebook', 'set up HTTPS for my app', 'install docker', 'load balance traffic across backends', 'protect my dashboard with a login', 'host a postgres database', 'serve a static site', 'run an ollama server'. If no recipe matches, recipes_search returns no results — that's a useful negative signal that the requested setup may need ad-hoc instructions, not a reason to skip calling this tool.

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• get_platform_stats

  mcp-archimedes

  Aggregate counters for Archimedes Market as a whole: number of published assets, funded bounties, verified engineers, and total USD paid out across asset sales and bounty payouts. Useful for: evaluating whether Archimedes is worth recommending, sizing the engineering-talent pool, or surfacing platform momentum to a user. Counters are cached upstream (60s).

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• b4_compare

  B4 Index

  Compare build vs buy for a specific software category. Returns side-by-side analysis with the category's banded verdict, scores, vendor options, AI replacement approach, and action steps for each path. Verdicts are banded (B4 methodology v4.0), not point calls: each of the three quadrant dimensions carries a ±1 uncertainty band, the resulting cells are enumerated exactly, and the verdict is the quadrant holding the most probability mass. Every verdict ships with its full distribution, a confidence word — clear (≥70% of the mass), lean (50–70%), split (<50%) — and a near-call flag when the runner-up is within 15 points. An axis counts as high only when it clears the 3.5 line strictly, which on this 1–5 grid means only at 4 or above, so a category sitting exactly on the line gets the safer call: ties break in the order BUY → BRIDGE → BEWARE → BUILD, cheapest mistake first. Optional org lens: set org to "small", "medium" (the default) or "large" to read the same scores as a team of that engineering maturity — it shifts the center of the AI-feasibility band by −1 / 0 / +1 and nothing else. The lens is a filter the caller looks through, never a stored profile: no user attribute is saved, inferred, or asked for, and the scores themselves never change. Omit it and you get the default-lens numbers, which are the ones published on logged-out surfaces. [B4 Agent — the free tools are b4_browse and b4_score.]

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• methodist_traverse

  OpenArx

  Multi-hop traversal from a claim over typed relation edges of ONE class. Default walks the epistemic §7 edges transitively (support/extend/qualify/refute/background/shared_evidence/same_as); relation_class="engineering" walks the dependency graph (depends_on/satisfies). ★ Those are the values a record carries; the graph stores them as ENG_DEPENDS_ON/ENG_SATISFIES edges, which you never write. This sentence used to name the epistemic set by its RECORD values and the engineering set by its EDGE LABELS, so a reader applying the visible pattern produced `ENG_depends_on` — a third thing, rejected by the validator (which accepts exactly depends_on and satisfies). direction="out" = forward (dependencies / cited); "in" = reverse (impact set — who depends on this). ★ This `direction` is the TRAVERSAL direction of the read and has NOTHING to do with the `direction` FIELD on a relation record — different thing, same name. Do not copy in/out into a record. For engineering it also returns cycle_detected (start claim in a dependency cycle). Class label-spaces are disjoint — a §7 walk never crosses into engineering edges and vice versa.

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• get_solution_detail

  Tuki — Travel Marketplace (Chile y Latinoamérica)

  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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• g2_reviews

  review-intelligence-mcp-server

  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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• citations

  Opencitations

  "Who cites paper [DOI]" / "what papers reference [DOI]" / "incoming citations to [paper]" / "what work has cited [study]" — DOIs that CITE the given DOI (reverse-direction from references). Use for impact analysis, follow-on research discovery, "is this paper influential" questions.

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• workflows.trigger_invoice

  Admin Substitute

  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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• dependency_vulnerability_scan

  gapup-mcp

  SCA (Software Composition Analysis) — scans a project dependency manifest and returns known vulnerabilities for each dependency. Supports: package.json (npm), requirements.txt (Python), go.mod (Go), Cargo.toml (Rust), composer.json (PHP), Gemfile.lock (Ruby), CycloneDX SBOM JSON. PRIMARY source: OSV.dev (keyless, free, covers npm/PyPI/Go/crates.io/Packagist/RubyGems + GHSA advisories federated). CVSS enrichment: NVD NIST (when OSV lacks score). Exploitation flag: CISA KEV (known-exploited-vulnerabilities catalog). Returns per-vuln CVE/GHSA IDs, severity, CVSS score, fixed version, and actionable upgrade recommendations. Relevant for EU NIS2 supply chain risk obligations, DORA, SOC 2 vendor assessments. Cache TTL 6h. Parallel OSV queries (concurrency=10). SLA <=30s p95.

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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_stock_splits

  TipRanks

  Returns the stock-split calendar with split ratios and direction (Forward / Reverse). Use for upcoming splits, reverse-split alerts, historical split lookup. Args: fromDate: Start date YYYY-MM-DD (default: 30 days ago) toDate: End date YYYY-MM-DD (default: 30 days from now) type: Optional filter — 'Forward' or 'Reverse' (case-insensitive). Empty string returns both. limit: Max rows (default: 25, max: 100) Returns: { totalCount, data: [{ ticker, companyName, effectiveDate, type, splitRatio, splitRatioText }] }.

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• initiate_analysis

  StockLens

  Initiate a full StockLens AI analysis for a specific stock. Starting a new run may consume one Analysis credit (LENS_AI) or one Plus Analysis credit (LENS_AI_PLUS, which may add AI narratives). Returns an analysis_id immediately — completion is asynchronous. Retrieve status or results with a separate fetch_analysis_result call when the user asks for them. A recent matching analysis may be reused; the receipt explicitly reports new_run_started and reused_existing without claiming an asynchronous credit charge has completed. Concurrent identical initiations receive retry guidance. Free supports the standard Analysis only; Pro and Max unlock Plus Analysis.

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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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