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524,333 tools. Updated 2026-09-06 15:00

"Information about Rust programming language or rust corrosion" matching MCP tools:

  • Returns a 0-100 developer momentum score for TypeScript, Python, Rust, and Go (30-day new-repo creation via GitHub Search API, normalized to a 100k ceiling; hourly, history since 2016) with trend, confidence, top_drivers, per_language breakdown, and total_new_repos_30d. Call when the user asks about programming language popularity, adoption trends, developer ecosystem growth, or open-source activity, or when timing devtools GTM, developer-marketing spend, or language-community sponsorships. Updates: hourly.
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  • Search a live index of ~11.7M developers (GitHub-centric) by role, primary programming language, location, and recent activity — for recruiting, GTM/lead-gen, and developer-audience research. Returns matching GitHub usernames with role, primary language, location, and last-active timestamp. Filters: role (e.g. "backend engineer", "ai/ml engineer"), primary_language (e.g. "Python", "Rust"), location, active_within_days (only devs active in the last N days), sort_by (default last_active). Paginate with limit + offset. Example: revternal_search_developers({ primary_language: "Rust", active_within_days: 30, limit: 20 }).
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  • Get crates.io package metadata for a Rust crate (latest version, downloads, repo). Use for Rust-dependency research. Example call: {"pkg": "tokio"} Cost: $0.005–$0.05 USDC on Base per call.
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  • Search stories by tag (e.g., "rust", "programming", "security"). Returns matching stories with titles, URLs, scores, and tags.
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  • Format source code with language-aware indentation and style rules. Supports JS, TS, Python, Go, Rust, and more. Use when standardizing code style or preparing snippets for documentation.
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  • 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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  • 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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  • Get authoritative Senzing SDK reference data: method signatures and argument types per language binding, flags, response schemas, and V3→V4 migration. Use this instead of search_docs for anything precise about the SDK surface. Whenever 'filter' names a method, the response carries that method's callable signature for every binding (narrowed by 'language' if given) NO MATTER WHICH TOPIC you asked for — so looking up a method's flags also tells you what it takes. Topics: 'parameters' (aliases: functions, methods, classes, api, signatures, args) returns argument types per binding — the same method differs by binding in BOTH name and argument types: Python find_network_by_entity_id takes List[int], Java findNetwork takes SzEntityIds, C# FindNetwork takes ISet<long>, Rust takes &[EntityId], TypeScript findNetwork takes Array<number> and renames buildOutDegrees to buildOutDegree; 'flags' (all V4 engine flags and the methods they apply to); 'response_schemas' (JSON response structure per method); 'migration' (V3→V4 breaking changes, renames, flag changes); 'all'. 'filter' accepts any spelling — 'get entity', 'get_entity', and 'getEntity' all resolve. Pass 'language' (python/java/csharp/rust/typescript) to narrow to your binding; cross-binding divergence warnings are still included so you never translate a call between bindings by mistake
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  • Create a new RationalBloks project from a JSON schema. ⚠️ CRITICAL RULES - READ BEFORE CREATING SCHEMA: 1. FLAT FORMAT (REQUIRED): ✅ CORRECT: {users: {email: {type: "string", max_length: 255}}} ❌ WRONG: {users: {fields: {email: {type: "string"}}}} DO NOT nest under 'fields' key! 2. FIELD TYPE REQUIREMENTS: • string: MUST have "max_length" (e.g., max_length: 255) • decimal: MUST have "precision" and "scale" (e.g., precision: 10, scale: 2) • datetime: Use "datetime" NOT "timestamp" • ALL fields: MUST have "type" property 3. AUTOMATIC FIELDS (DON'T define): • id (uuid, primary key) • created_at (datetime) • updated_at (datetime) 4. USER AUTHENTICATION: ❌ NEVER create "users", "customers", "employees" tables with email/password ✅ USE built-in app_users table Example: { "employee_profiles": { "user_id": {type: "uuid", foreign_key: "app_users.id", required: true}, "department": {type: "string", max_length: 100} } } 5. AUTHORIZATION: Add user_id → app_users.id to enable "only see your own data" Example: { "orders": { "user_id": {type: "uuid", foreign_key: "app_users.id"}, "total": {type: "decimal", precision: 10, scale: 2} } } 6. FIELD OPTIONS: • required: true/false • unique: true/false • default: any value • enum: ["val1", "val2"] • foreign_key: "table.id" AVAILABLE TYPES: string, text, integer, decimal, boolean, uuid, date, datetime, json, uuid_array, integer_array, text_array, float_array Array types store PostgreSQL native arrays with automatic GIN indexing: • uuid_array: UUID[] — for sets of references (e.g., tensor coordinates) • integer_array: BIGINT[] — for dimension indices, integer sets • text_array: TEXT[] — for tags, categories, label sets • float_array: DOUBLE PRECISION[] — for weight vectors, scores GIN-indexed operators: @> (contains), <@ (contained_by), && (overlaps) BACKEND ENGINE: • python (default): FastAPI backend — mature, full-featured • rust: Axum backend — faster cold starts, lower memory, high performance WORKFLOW: 1. Use get_template_schemas FIRST to see valid examples 2. Create schema following ALL rules above 3. Call this tool (optionally choose backend_type: "python" or "rust") 4. Monitor with get_job_status (2-5 min deployment) After creation, use get_job_status with returned job_id to monitor deployment.
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  • Returns Fluentive's security, privacy, and compliance information. Use when the user asks about GDPR, data storage location, encryption, security certifications, or payment security.
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  • Published Truss information by topic. Localized topics (overview, about, services, engagement, fit, faq) use locale, default en; pass he for Hebrew. Language-independent topics (identity, certifications, testimonials, clients, contact) ignore locale for content selection. Prefer get_truss_overview or topic overview for broad business understanding; prefer list_truss_services for the complete service catalog.
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  • Watch one TCP or UDP port on a host and report it up only when the service behind it actually answers. This is the type for game servers, databases, mail and anything else that speaks its own protocol rather than HTTP - Minecraft, Rust, CS2, FiveM, Postgres, Redis, SMTP. The port must be given as part of the url. Set protocol to UDP for game servers; most of them do not answer on TCP at all.
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  • 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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  • ContextStream agent Q&A — ask the workspace/project knowledge base when you get stuck. When to use: - You need workspace-specific knowledge you cannot derive from code: prior decisions ("why was X chosen over Y?"), conventions ("what's the file naming pattern in this repo?"), runbooks ("how does the team handle this kind of incident?"), guardrails ("what's off-limits in this workspace?"). - You're about to make a non-trivial choice and the workspace probably has prior context that shapes it. - A teammate has likely answered this before and you'd rather reuse than re-derive. When NOT to use: - General programming questions you can answer yourself or via web search ("how does Rust async work?"). - Things you can determine by reading the code right in front of you — read it first. - Trivial syntax or single-line questions. Not a reflex, not a last resort. If you're spending more than ~30 seconds stuck on something workspace-shaped, ask. If you can find the answer in 30 seconds yourself, do that. Actions: - ask: submit a question, get a grounded answer with citations + confidence. - search: vector-similarity-free listing of prior Q&A — check before re-asking. - save_kb: store guidance/guardrail/faq/runbook/caveat for future asks to reference. - list_kb: browse stored knowledge. - get_kb / update_kb / delete_kb: manage individual KB items. - feedback: rate an answer (-1, 0, +1) so future retrievals weight it appropriately. Answers come from ContextCode, ContextStream's grounded Q&A agent. Every claim cites the source (`[id=decision:abc]` / `[id=lesson:xyz]` / `[id=qa_kb_item:def]` etc.) so you can verify before acting on it.
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  • Retrieve documents published after a training cutoff, ranked by similarity. Call this whenever the user asks about events, releases, papers, issues, or news that might post-date your training data. Fillin only returns documents published AFTER `cutoff`, so nothing returned is redundant with what the model already knows. Args: query: Natural-language search query (e.g. "rust async runtimes"). Max 512 characters. cutoff: ISO-8601 date representing the agent's training cutoff (e.g. "2026-01-01"). Documents on or before this date are excluded from results. k: Number of documents to retrieve, 1-20. Defaults to 5. Returns: A dict with: - cutoff: echoed cutoff (ISO timestamp) - query: echoed query - gap_days: days between cutoff and now - results: list of {id, source, url, published_at, title, text, score}
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  • Get business information including name, type, service area, contact details, working hours, supported languages, enabled features, and a profile image (logo or personal photo) when the owner has uploaded one. Use 'attributeDetails' (natural-language sentences about the business's offerings, approach, and specialties) to reason about fit for the user. The 'cardChips' and 'cardChipGroups' fields are UI-only display data — ignore them. The response echoes the exact slug; reuse it verbatim in later tool calls. Always available for any business.
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  • Codeforces competitive-programming API — the submission history of one Codeforces handle, newest first, paged with from/count. Each submission carries the contest and problem it targeted, the judge verdict (OK, WRONG_ANSWER, TIME_LIMIT_EXCEEDED), programming language, runtime and memory consumed. Answers which problems a Codeforces user attempted lately and whether they passed.
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  • Follow a tag so posts carrying it rank higher in your for-you feed. Tag follows are global — following ``rust`` covers rust-tagged posts in every colony, not just one. This is the cheapest way to fix a thin or generic for-you feed: it takes effect on your next poll, needs no reciprocal action from anyone, and is trivially reversible. Idempotent in both directions — following a tag you already follow, or unfollowing one you don't, reports the resulting state rather than erroring.
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  • Follow a tag so posts carrying it rank higher in your for-you feed. Tag follows are global — following ``rust`` covers rust-tagged posts in every colony, not just one. This is the cheapest way to fix a thin or generic for-you feed: it takes effect on your next poll, needs no reciprocal action from anyone, and is trivially reversible. Idempotent in both directions — following a tag you already follow, or unfollowing one you don't, reports the resulting state rather than erroring.
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  • Recover the native pixel grid from enlarged, softened, AI-rendered, or compressed pixel art. Standard uses the native Rust detector. Neural uses the neural reconstruction engine and accepts optional target width and height values. This repairs existing art—it does not generate a new image. Both engines are free and share a limit of 10 requests per minute per API token. Provide exactly one source as base64 input_image or a public HTTPS image_url. URL input avoids the ALB request-body limit. Decoded images may contain up to 16 megapixels. Successful response JSON is limited to 850,000 bytes for AWS ALB compatibility.
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  • 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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