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676,128 tools. Updated 2026-10-19 10:13

"Apache Kafka" matching MCP tools:

  • Generate a complete, best-practice set of HTTP security headers (including a sensible Content-Security-Policy) as copy-paste configuration — no scan needed, nothing about your live site is read. Pick a `preset`: 'recommended' is a safe baseline that works for most sites, 'strict' is hardened with a nonce-based CSP for higher security, and 'report-only' puts the CSP in report-only mode so you can roll it out and watch for breakage before enforcing it. Advanced users can instead pass a full `config` object to fine-tune every header; if you pass neither, it defaults to 'recommended'. Returns the resulting headers as name/value pairs, plus ready-to-paste output for nginx, Apache, Caddy, Cloudflare, a Netlify/Cloudflare-Pages `_headers` file, and raw headers, along with any warnings. Use this to set up headers on a new or unscanned site; use analyze_security_headers first when you want to see what an existing site is already missing.
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  • utilpeek: parse and format a phone number. For a prefix's country and network use lookup_phone_prefix. A string that is not a phone number, or a national form without region, is a 422 (not charged). Offline numbering-plan check (phonenumbers, Apache-2.0): `valid` means the number fits its country's numbering plan, not that it is assigned, in service or reachable; `carrier` is the original range holder (ported numbers are not detected). The submitted value is processed in memory only, never logged or stored. Typically under 0.1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/phone-number-validation-api.html
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  • utilpeek: parse and format a phone number. For a prefix's country and network use lookup_phone_prefix. A string that is not a phone number, or a national form without region, is a 422 (not charged). Offline numbering-plan check (phonenumbers, Apache-2.0): `valid` means the number fits its country's numbering plan, not that it is assigned, in service or reachable; `carrier` is the original range holder (ported numbers are not detected). The submitted value is processed in memory only, never logged or stored. Typically under 0.1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/phone-number-validation-api.html
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  • Returns copy-paste-ready fix recommendations (nginx, Apache, DNS, shell) for the issues found on a domain the caller has already paid for — either an active Monitor/Compliance subscription covering the domain, OR a purchased one-off Report for the domain. Each recommendation carries a stable issue_id, a priority (high/medium/low), a title, prose instructions, one or more config snippets with the target domain already interpolated, a verify command, and a category tag. Use this when the user asks how to fix an issue, wants the exact config to apply, or needs to verify a fix worked. Pass the optional issue_id to scope the response to one specific finding. The response is read-only — this tool NEVER triggers a fresh scan; fixes are computed from the most recent stored scan (including the Report-included re-scan if that was used). Do NOT use this for domains the caller hasn't purchased coverage for — you'll get an upgrade_required error that links to the pricing page. Do NOT use this to run or trigger a scan; call scan_domain for anonymous checks. Requires a valid API key.
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  • Provisions a managed ClickHouse database (OLAP / columnar analytics engine, Apache-2.0) on a dedicated VM on your private network - its OWN resource, NOT a relational database. Requires a recent plan_managed_datastore. Use it for analytics / observability workloads that need a column store (PostHog, Langfuse, event analytics, time-series). It is PRIVATE - reachable only from another instance on the same private network, via the DB's internal/private IP on the ClickHouse HTTP port 8123 (CLICKHOUSE_HOST/PORT/USER/PASSWORD/DB env, http://host:8123). Get the ids from plan_managed_datastore/list_flavors (use m1.small+ - ClickHouse needs >=2GB RAM), list_private_networks, list_keypairs. Provisioning takes ~5 min; poll list_clickhouse_databases until status='ready'. HIGH AVAILABILITY: pass ha:true to get THREE machines on three different physical hosts behind a load balancer instead of one: all three take reads and writes, so losing a machine costs no failover and no write pause, and the replacement refills itself from the survivors before it serves again. It costs about 3x the hourly rate (three machines instead of one) and provisions more slowly. Default is a single machine; show the user the price difference and get an explicit yes before turning HA on.
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  • Translate into 452 languages, 251 of them NOT supported by ChatGPT, Claude or Gemini (29 of those 251 measured at fair quality or better against human references) — including Bhojpuri (~50M speakers), Maithili (~34M), Egyptian Arabic (~100M), Moroccan Arabic (~30M), Chhattisgarhi, Magahi, Manipuri, Kashmiri, Shan, Kachin, Awadhi, Tamazight, Crimean Tatar, Quechua, Nuer, Sango, plus indigenous and minority languages with no callable API anywhere. Runs MADLAD-400 (Apache-2.0). QUALITY VARIES AND IS PUBLISHED PER LANGUAGE: every language carries a measured tier — good (chrF++ >= 45 vs human reference translations), fair (32-45), unverified (no benchmark exists, untested, may be poor), experimental (known weak). The response repeats the tier so you can judge how much to trust it. GET https://sats4ai.com/api/l402/translate-rare-language for the full language list with tiers, or GET /api/languages. Unsupported languages are rejected BEFORE payment. For mainstream languages use translate_text instead — it is cheaper and more fluent. Priced 50 sats base + 0.002 sats/char (GPU). Pay with Bitcoin Lightning — no API key or signup. Requires create_payment with toolName='translate_rare_language'.
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Matching MCP Servers

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    An AI-native control interface for OCI Streaming with Apache Kafka that enables LLM agents to securely manage Kafka clusters through structured tool execution with safety guardrails.
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    Apache 2.0
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    Enables MCP-capable clients to monitor and manage Apache Kafka clusters, including topics, partitions, configs, and consumer groups with lag, while enforcing configurable security modes, topic allowlists, protected topics, and audit logging.
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    74 npm
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    MIT

Matching MCP Connectors

  • When: use when Datadef's own model should design a whole new diagram from a description or a pasted schema. To build it yourself step by step, use create_blank_diagram; to change an existing diagram, canvas_view and the canvas_* tools, or edit_diagram. Generates a professional data architecture diagram from a description and saves it to the user's Datadef account. Use it for any data-shaped diagram: medallion and lakehouse architectures, ETL/ELT pipelines, streaming topologies, data mesh, star and snowflake schemas, ER diagrams, lineage maps, cloud architectures, and data platform designs. EXISTING SCHEMAS: if the conversation or the repository has the schema itself (SQL DDL, a MySQL or pg_dump export, migrations, schema.prisma), pass the file contents as the prompt, unedited. Datadef parses it instead of asking a model: every table, column, type, primary key and foreign key comes out exactly as written, in seconds. Warehouse DDL with no declared foreign keys (fact_sales.customer_key → dim_customer.customer_key) gets its links from key names, drawn dashed. Add a sentence only if you want something other than a picture of that schema; "turn these tables into a star schema" or "design the pipeline that loads them" goes to the model instead. Write a specific prompt. Name the actual technologies (Snowflake, dbt, Airflow, Kafka, Fivetran), the layers or zones you want, and the tables that matter, the diagram is only as detailed as the description. Zone names you give are treated as a specification, not a suggestion. SCOPE: if the user's request is open-ended ("diagram our platform", "show me something"), ask them how much detail they want before calling this, or say which scope you chose. Default to scope "overview". A dense 40-node diagram is impressive and usually not what someone wanted from a one-line request; they can always ask you to expand it. TIMING: this waits up to ~35 seconds; fast generations come back finished, with a preview image and a markdown line to show the user. Slower ones (1-3 minutes total) return a diagram_id while generation continues: call get_diagram with that id after ~60 seconds (poll every 30s) to get the finished diagram. If you cannot call tools again on your own, never leave the user empty-handed: give them the Open link, say it will be ready in about a minute, and offer to show the diagram inline when they next ask. Never call create_diagram a second time while one is still generating; you would create a duplicate. Do not use image generation for these. This produces a real, editable diagram. Next: show the user the preview line it returns; to adjust the result, canvas_view then the targeted canvas_* tools.
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  • When: use for a large change you can describe in words better than you can make it tool by tool: "reorganise this into bronze, silver and gold", "rebuild the consumption side from this spec", "add owners and schedules everywhere". For a precise change (rename, add a node, rewire an edge), the canvas_* tools are faster and exact. Datadef's own editing agent carries out the instruction: add nodes, rewire connections, add columns, reorganise into layers, fill in owners and schedules. Be specific about what to change and where: "Add a Kafka topic between the API and the bronze table, labelled 'events'" works; "make it better" does not. Timing: an edit takes 30-120 s. This waits about 35 s; a finished edit comes back with its summary, the ids it changed and a PNG. A slower one answers "in progress" and keeps running on the server: do NOT call edit_diagram again. Sending the same instruction again joins the running edit (or returns the result of the one that finished less than a minute ago); a different instruction is refused until it finishes. canvas_view and get_diagram report when it has finished. Next: canvas_view to check the result.
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  • utilpeek: parse a User-Agent string. For known x402 monitors and indexers with call rates use identify_x402_client. A User-Agent is self-reported and easy to fake; `is_bot` is a heuristic (declared crawlers, HTTP libraries, headless browsers). Parsed with ua-parser and the uap-core regexes (Apache-2.0). Typically under 0.1 s (first call up to 0.5 s). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/user-agent-parser-api.html
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  • mlpeek: Rerank up to 100 documents against a query. For pair scores use text_similarity. Returns 1-100 documents scored against a query by the ms-marco-MiniLM-L6-v2 cross-encoder (Apache-2.0) and sorted best first: index, rank, score (sigmoid of the logit, a relevance in 0-1) and logit; `top_k` keeps the best k. English model. Each text is cut at the model's 512-token window and the answer says so (longest side first). Over 32,768 tokens per request is a 422 too_many_tokens (not charged). Runs an open model pinned by hash on Tanod's own CPU (no LLM, no third-party API); an unavailable model is a 503 (not charged). Typically 0.1-2 s, up to about 10 s at the token budget. Price: USD 0.002. Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/embeddings-rerank-ner-mcp-server.html
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  • utilpeek: parse a User-Agent string. For known x402 monitors and indexers with call rates use identify_x402_client. A User-Agent is self-reported and easy to fake; `is_bot` is a heuristic (declared crawlers, HTTP libraries, headless browsers). Parsed with ua-parser and the uap-core regexes (Apache-2.0). Typically under 0.1 s (first call up to 0.5 s). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/user-agent-parser-api.html
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  • mlpeek: Rerank up to 100 documents against a query. For pair scores use text_similarity. Returns 1-100 documents scored against a query by the ms-marco-MiniLM-L6-v2 cross-encoder (Apache-2.0) and sorted best first: index, rank, score (sigmoid of the logit, a relevance in 0-1) and logit; `top_k` keeps the best k. English model. Each text is cut at the model's 512-token window and the answer says so (longest side first). Over 32,768 tokens per request is a 422 too_many_tokens (not charged). Runs an open model pinned by hash on Tanod's own CPU (no LLM, no third-party API); an unavailable model is a 503 (not charged). Typically 0.1-2 s, up to about 10 s at the token budget. Price: USD 0.002. Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/embeddings-rerank-ner-mcp-server.html
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  • Composite CVE risk score (0-100) — fuses CVSS, EPSS, KEV, and PoC into a single agent-ready triage signal. Formula: CVSS*0.20 + EPSS*0.35 + KEV*0.30 + PoC*0.15 (each component rescaled to 0-100 before weighting). Multiplicative boosters applied in order: KEV+PoC combo (*1.15), critical-severity-with-high-EPSS (CVSS>=9 AND EPSS>0.7, *1.10), recently published (within last 7 days, *1.05). Final score clamped to [0, 100]. Label bands: CRITICAL>=90, HIGH>=70, MEDIUM>=40, LOW<40. Urgency text encodes patch SLA (immediate when KEV; 24h/72h/30d by label). Use to triage a single CVE without orchestrating cve_lookup + exploit_lookup separately. PoC signal here is the local ExploitDB mirror only — for full multi-source exploit detail (GitHub Advisory + Shodan refs + ExploitDB), call exploit_lookup separately. Methodology adapted from mukul975/cve-mcp-server (Apache-2.0): https://github.com/mukul975/cve-mcp-server. Free: 30/hr, Pro: 500/hr. Returns {cve_id, score (0-100), label (CRITICAL/HIGH/MEDIUM/LOW), urgency, has_public_poc, components (cvss_v3, epss_score, in_kev, has_public_poc, weighted_breakdown), boosters_applied, recommendation, summary, verdict, next_calls}.
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  • Perform a full-text vulnerability search in SecDB. ## What this tool does Searches across: - CVE entries - Security advisories - Exploit references - Product and vendor vulnerability data Results are formatted in Markdown and include a search summary. ## Searchable fields (Lucene syntax supported): - type: result type - cve, cwe, advisory, nasl, exploitdb, nuclei - id: exact identifier (e.g. id:CVE-2026-12345, id:RHSA-2026:1234) - title: resource title or name - summary: short summary - description: full description text - alias: known vulnerability names (e.g. alias:log4shell) - severity: critical, high, medium, low - kev: true/false — CISA KEV catalog membership - status: NVD status (CVE only) e.g. analyzed, modified - published: publication date (e.g. published:[2026-01-01 TO 2026-12-31]) - modified: last modification date - source: CNA or advisory source (e.g. source:"Red Hat", source:fortinet) - cve: related CVE ID (e.g. cve:CVE-2026-44827) - cwe: related CWE ID (e.g. cwe:CWE-79) - tag: advisory tag (e.g. tag:scada, tag:ics) - attack_vector: network, adjacent, local, physical - cvss_score: CVSS base score (e.g. cvss_score:[7.0 TO 10.0]) Default operator is AND. Use OR for alternatives, quotes for exact phrases, * for wildcards. ## Examples: - "apache struts rce" → RCE vulnerabilities in Apache Struts - "id:CVE-2026-44827" → exact CVE lookup - "source:fortinet AND severity:critical" → critical Fortinet advisories - "alias:log4shell" → Log4Shell by alias - "cve:CVE-2026-44827 AND type:exploitdb" → ExploitDB entries for a CVE - "cvss_score:[9.0 TO 10.0] AND kev:true" → critical KEV CVEs - "tag:scada AND severity:high" → high severity ICS/SCADA advisories ## When to use this tool Use this tool when the user asks: - to look up a CVE, advisory, exploit, or product - "show vulnerabilities for X" - "search for advisories about Y" - exploratory or broad vulnerability discovery ## Inputs - **query**: free-text search term (CVE ID, advisory ID, product name, exploit name, vendor, keyword, etc.) ## Outputs - **results**: array of Markdown-formatted search hits - **summary**: Markdown summary with counts and a link to continue searching on SecDB ## LLM usage guidelines - Use this tool instead of assuming whether a CVE/advisory/exploit exists. - Present `results` and `summary` directly to the user-they are already Markdown. - Combine with `vulnerability_score`, `epss_timeseries`, or `sightings_search` for deeper analysis.
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  • Analyse the HTTP security headers of a public URL OR of raw response headers you paste in. Grades each header (A–F) for: Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, X-XSS-Protection, Cross-Origin-Opener-Policy, Cross-Origin-Resource-Policy, and Cross-Origin-Embedder-Policy. Returns an overall score (0–100), per-header grades, missing headers, and fix snippets for Express, Nginx, and Apache. For localhost/private targets the remote server cannot reach, pass the `headers` parameter instead of `url`.
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  • Audit a CycloneDX or SPDX SBOM against an SPDX licence policy and return a PASS/WARN/BLOCK verdict. sbom: Full SBOM as a JSON string — CycloneDX or SPDX format. Required. 500 KB max. policy: Optional dict with block/warn/allow arrays of exact SPDX licence identifiers (e.g. GPL-3.0, MIT). Defaults to block GPL-3.0 and AGPL-3.0, warn LGPL-2.1/MPL-2.0/BSD-4-Clause, allow MIT/Apache-2.0/BSD-2-Clause/BSD-3-Clause. No glob patterns — exact SPDX IDs only. Unlisted licences default to WARN. Returns verdict (PASS/WARN/BLOCK), blocked_packages, warned_packages, and the policy applied. Use security_audit_sbom_vulnerabilities for CVE auditing instead. Sources: deps.dev (Google). 1-hour cache per package. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="security_audit_sbom_license_policy", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".
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  • Given a list of packages (name + optional exact version or semver range — e.g. straight from a package.json "dependencies" object) and an optional allow/deny license policy, resolves each package's declared SPDX license and reports a compliance verdict per package. Classifies every license into one of permissive/weak-copyleft/copyleft/network-copyleft/proprietary/public-domain/unknown, and understands simple SPDX expressions: "(MIT OR GPL-3.0)" is compliant if EITHER side is permitted (a consumer may legally pick the clean alternative), "MIT AND Apache-2.0" requires both sides to pass, and "X WITH exception" is judged on X. A mixed/nested expression like "(MIT OR ISC) AND Apache-2.0" is reported as needsReview rather than guessed at. `policy.deny` entries always win over `policy.allow` (so a name can appear in both without a silent contradiction); with `policy.allow` set, anything not matching it is a violation (unproven is treated as non-compliant); with neither given, the default policy flags only copyleft/network-copyleft/proprietary (e.g. GPL/AGPL/UNLICENSED) — weak-copyleft (LGPL/MPL/EPL) and unrecognized license strings are surfaced but not auto-flagged. Policy entries accept an exact SPDX id, a family prefix ("GPL" catches GPL-2.0/GPL-3.0-only/etc.), or a category name. This reads only the registry-declared `license` field — it does not fetch or parse LICENSE file contents from the source repository.
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  • Given a list of packages (name + optional exact version or semver range — e.g. straight from a package.json "dependencies" object) and an optional allow/deny license policy, resolves each package's declared SPDX license and reports a compliance verdict per package. Classifies every license into one of permissive/weak-copyleft/copyleft/network-copyleft/proprietary/public-domain/unknown, and understands simple SPDX expressions: "(MIT OR GPL-3.0)" is compliant if EITHER side is permitted (a consumer may legally pick the clean alternative), "MIT AND Apache-2.0" requires both sides to pass, and "X WITH exception" is judged on X. A mixed/nested expression like "(MIT OR ISC) AND Apache-2.0" is reported as needsReview rather than guessed at. `policy.deny` entries always win over `policy.allow` (so a name can appear in both without a silent contradiction); with `policy.allow` set, anything not matching it is a violation (unproven is treated as non-compliant); with neither given, the default policy flags only copyleft/network-copyleft/proprietary (e.g. GPL/AGPL/UNLICENSED) — weak-copyleft (LGPL/MPL/EPL) and unrecognized license strings are surfaced but not auto-flagged. Policy entries accept an exact SPDX id, a family prefix ("GPL" catches GPL-2.0/GPL-3.0-only/etc.), or a category name. This reads only the registry-declared `license` field — it does not fetch or parse LICENSE file contents from the source repository.
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  • Look up a MIME type by type string or file extension. Accepts "image/webp", ".webp", or "webp". Returns the canonical MIME type, known file extensions, whether the type is compressible (relevant for Content-Encoding decisions), and the data source (iana, apache, nginx). For extension lookups, returns the canonical MIME type — e.g., ".jpg" resolves to "image/jpeg". When multiple types map to an extension, the canonical type is returned first with alternatives listed.
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  • utilpeek: Detect the language of a text. To translate use translate_text. Returns the most likely language (ISO 639-1 and 639-3 code, name, confidence) and 3 alternatives, from an offline ensemble of lingua (Apache-2.0) and langid.py (BSD-2-Clause) over 75 languages; `reliable` is true only with at least 20 letters and confidence 0.6 or more. Short text is often flagged unreliable; text with no letters is a 422 no_text (not charged). Typically under 0.1 s (a few seconds on the worker's first call). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/language-detection-api.html
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  • utilpeek: Detect the language of a text. To translate use translate_text. Returns the most likely language (ISO 639-1 and 639-3 code, name, confidence) and 3 alternatives, from an offline ensemble of lingua (Apache-2.0) and langid.py (BSD-2-Clause) over 75 languages; `reliable` is true only with at least 20 letters and confidence 0.6 or more. Short text is often flagged unreliable; text with no letters is a 422 no_text (not charged). Typically under 0.1 s (a few seconds on the worker's first call). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/language-detection-api.html
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