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458,158 tools. Updated 2026-08-14 23:05

"Debugging software using GDB (GNU Debugger)" matching MCP tools:

  • 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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  • 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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  • Ricerca full-text sui codici ATECO 2007/2022 ISTAT partendo dalla DESCRIZIONE dell'attività ("fotografo", "sviluppo software", "commercio abbigliamento") invece che dal codice. Case/accent-insensitive, con stemming e alias colloquiali per-mestiere; accetta anche un prefisso di codice. Usa questo quando il codice NON è noto — `lookup_ateco` serve al caso opposto (codice noto → descrizione). Risultati ordinati per pertinenza. Gratis (€0), deterministico, nessun login richiesto.
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  • 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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  • Queries CNAE (National Classification of Economic Activities) from IBGE. CNAE is the official classification for economic activities in Brazil. Hierarchical structure: - Section (letter A-U): 21 main categories - Division (2 digits): 87 divisions - Group (3 digits): 285 groups - Class (4-5 digits): 673 classes - Subclass (7 digits): 1,332 subclasses Features: - Search by CNAE code - Search by activity description - List by hierarchical level - Show complete hierarchy Examples: - Search software: busca="software" - Specific code: codigo="6201-5/01" - View section: codigo="J" - List divisions: nivel="divisoes" Behavior: read-only and idempotent — a live GET against the public IBGE CNAE API. Returns Markdown.
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  • Echo strings through the daemon via DERO.Echo. Useful for round-trip sanity checks. When to call: when you need to confirm that string payloads reach the daemon intact (e.g. before debugging a malformed call to a more complex tool). PREFER dero_daemon_ping for a lighter-weight liveness probe. Input Requirements (CRITICAL): - `words` MUST be a non-empty array of strings. Output: the echoed string concatenated by the daemon.
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Matching MCP Servers

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    quality
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    maintenance
    MCP server that exposes GDB debugging as tools. An AI assistant can set breakpoints, run programs, step through code, inspect variables and memory, and examine registers — all via structured tool calls. Reverse debugging with rr is also supported.
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    3
    MIT
  • A
    license
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    An MCP server that provides programmatic access to the GNU Debugger (GDB), enabling AI models to interact with GDB through natural language for debugging tasks.
    9
    Apache 2.0

Matching MCP Connectors

  • 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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  • Analyze any image using AI vision for manual inspection, debugging, visual description, or supplemental critique. Provide exactly one source: generation_result_id for a Shoot Board generation, uploaded_file_id for a Files item, or image_url for a public HTTPS image. Do not use this as the primary QA mechanism when the user asks to QA, quality-check, validate, review, approve/reject, or assess generated results; for QA requests use queue_generation_result_qa first, then read_generation_result_qa.
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  • 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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  • List pre-configured group-conversation templates. Templates are shapes for common multi-agent setups: software team, research pod, content team. Each has a slug, default title + description, suggested role labels, and an optional starter message that gets pinned at creation. Use ``colony_create_group_from_template`` with the slug to create.
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  • Look up real H-1B base salaries for a job title from DOL LCA disclosures — a market wage benchmark backed by actual filed salaries (not estimates). Answers 'what do H-1B software engineers earn at company X / in city Y'. Filter by job title, and optionally by employer, city, and year. Returns salary statistics (count, min / median / average / max) plus a sample of individual records (employer, title, salary, location, dates).
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  • 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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  • Lightweight catalogue of all registered frameworks — one row per framework with framework_id + intent + 1-line applies_when + version. Useful for discovery / debugging without parsing the full library. For the actual decision template, call tengu_v3_framework_lookup.
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  • Which Nice classes a business like this files in. Free, no account, no database. The question people cannot answer for themselves: not "is my name taken" but "taken in WHAT". Nice has 45 classes and the numbering is opaque — software you download is class 9, software you log into is class 42, and selling other people's goods is class 35 whatever the goods are. Filing in one and not the other is the most expensive routine mistake in the process. Run this BEFORE screen_mark when someone describes a business rather than naming a class: the classes it returns are what makes a screen mean anything. Relay the reasoning, not just the numbers, and keep the closing caveat — this reports how similar businesses file, and their counsel decides what they actually file.
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  • 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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  • 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 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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  • 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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  • 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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  • 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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