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

• verify_content_credentialsA

  Lumethic Photo VerificationA

  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.

  ConnectorOAuth

• find_referencesA

  Cloud FinOps Skill & MCPA

  Find which guidance serves a FinOps question - how to commit, size, allocate, charge back, forecast, or govern cloud and AI spend. Use this for questions like "how should we size Savings Plans", "what should Finance own in chargeback", "what does a Crawl-stage org tackle first" - anything that maps to FinOps Framework facets (domain, capability, phase, persona, maturity) - and you want only the references that serve it, instead of scanning the full list. All filters are optional and combine with AND semantics. String matching is case-insensitive and exact (not substring). Examples: - ``find_references(domain="Optimize Usage & Cost")`` - ``find_references(phase="Optimize", persona="Engineering")`` - ``find_references(persona="Engineering", persona_primary_only=True)`` - ``find_references(capability="Rate Optimization")`` - ``find_references(maturity="Crawl")`` Args: domain: FinOps Framework domain (e.g. ``"Optimize Usage & Cost"``, ``"Quantify Business Value"``, ``"Manage the FinOps Practice"``). capability: FinOps capability (matches ``fcp_capability`` and ``fcp_capabilities_secondary``). phase: FinOps phase (``"Inform"``, ``"Optimize"``, ``"Operate"``). persona: Persona (matches ``fcp_personas_primary`` and ``fcp_personas_collaborating``). maturity: Entry maturity level (``"Crawl"``, ``"Walk"``, ``"Run"``). persona_primary_only: when True, ``persona`` matches only the primary list. Use it when the default match barely narrows the set - broad personas like Engineering collaborate on nearly every file, so filtering on collaboration is descriptive, not discriminating. ``persona="Engineering", persona_primary_only=True`` is the engineering reading list; the default is the everything-they-touch view. Returns ``{"filters": {...}, "references": [...], "total": N}``. A query that matches nothing also returns `hint` and `valid_values`, so a typo is distinguishable from a genuine gap in coverage.

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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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• submit_rfqA

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

  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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• beam_bendingA

  TrueCalciA

  Calculate structural engineering beam mechanics: maximum elastic deflection, peak bending moment, and maximum flexural stress for a center point load on a simply supported Euler-Bernoulli beam. Behavior: Deterministic, idempotent calculation with zero external side effects. Evaluates Euler-Bernoulli beam equations: Max Moment M_max = (P * L) / 4; Max Deflection delta_max = (P * L^3) / (48 * E * I); Peak Bending Stress sigma_max = (M_max * y) / I. Converts area moment of inertia from cm^4 to m^4 and extreme fiber distance from mm to m. Returns deflection in mm, moment in N*m, and stress in MPa. Usage Guidelines: Use for civil, structural, and mechanical engineering beam sizing and load checks. Do not use for fluid pipe friction or pressure drop; use pipe_flow instead.

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• lookup_compiled_ruleA

  lr-labsA

  Answer a RULE-LEVEL question directly from compiled law: thresholds and day counts, WITHHOLDING TAX rates on royalty and fees for technical services (treaty and domestic), the 1961→2025 Income-tax Act section renumbering (s.195→s.393(2), s.115A→s.207, s.90→s.159, s.206AA→s.397(2)), tests and their elements, what a named case held. Ask in plain language — 'what is the India–US royalty WHT rate' (15%, not the widely-repeated 10%), 'what replaced section 195', 'is software payment royalty after Engineering Analysis', 'is a TRC sufficient after Tiger Global', 'what does make available mean'. Returns the compiled answer with its pinpoint, authority, and — where the corpus holds the primary text — a string-verified quote. Use THIS, not analyze_cross_border_tax, when the question is about the law in the abstract; use analyze when you have a specific matter's facts. Outside compiled topics it refuses and lists what can be asked.

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• malware_get_templateA

  website-searchA

  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_geocodeA

  NausikaA

  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_leiA

  nordic-dataA

  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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• get_platform_statsA

  mcp-archimedesA

  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_compareA

  B4 IndexA

  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_traverseA

  OpenArxA

  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.

  ConnectorAPI key

• 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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• 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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• citationsA

  OpencitationsA

  "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_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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• dependency_vulnerability_scanA

  gapup-mcpC

  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_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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• build_partnership_business_caseA

  elc-tradeA

  Answers 'how do I justify a community partnership budget internally?' Returns real reach numbers, the deliverables that serve the stated goal, where a proposed budget lands on the published ladder, and a forwardable approval email. Goals: hiring, brand_awareness, product_feedback, thought_leadership, people_development (developing your own engineering leaders — mentoring, Academy seats, the peer network). Set buying_for=individual if you are a person rather than a company: ELC membership is free for engineering leaders and no business case is needed.

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• company-hiring-radarA

  Company Intelligence Tools — Zinin M2M HubA

  Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn it into a buying/expansion signal: role count, which functions are growing (sales, engineering, marketing), remote share and what's new. No login, no scraping, no proxies. — $0.01/call, x402 (USDC on base).

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• fetch_notesA

  Sweeppea MCPA

  Get all notes for your account. Notes are automatically decrypted and returned in reverse chronological order. Use them internally for tool chaining but present only human-readable information (titles, content, dates). # fetch_notes ## When to use Get all notes for your account. Notes are automatically decrypted and returned in reverse chronological order. Use them internally for tool chaining but present only human-readable information (titles, content, dates).

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