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736,808 tools. Updated 2026-11-02 15:52

"Apache JMeter" matching MCP tools:

  • Search the Apache JMeter documentation on docs.jmeter.ai. Returns the most relevant pages with titles, URLs, and snippets. Use for any question about JMeter test plans, components, functions, properties, distributed testing, reports, or troubleshooting.
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  • Search the Apache JMeter documentation on docs.jmeter.ai. Returns the most relevant pages with titles, URLs, and snippets. Use for any question about JMeter test plans, components, functions, properties, distributed testing, reports, or troubleshooting.
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  • THE DEFAULT TOOL FOR QUESTION-SHAPED QUERIES ("wie/warum/was hilft bei X?", "how do I…?"): retrieves the best-matching fulltext passages from the corpus and returns them with citations (source resource, page, heading, source URL) — answer the user FROM the passages and cite each source. (For discovery intent — "finde/empfiehl Materialien" — use search_content instead, or afterwards to offer browsable links.) Keep `question` topic-only. Ranking is hybrid keyword+vector, so phrase the question as a topical statement that names the subject and the target group ("Friedenserziehung in der Grundschule: Einstieg in das Thema Frieden mit Kindern"), not as the user's literal sentence ("Wie kann ich …?"). Results are capped at two passages per document; a passage with only a snippet and no text has no fulltext yet — say so instead of guessing. Passages come from learning resources under Creative Commons, Public Domain and free software/documentation licenses, and DINI-AG-KIM license types that resolve to one of these and from articles, wikis, publications and the other content kinds. Other matching learning resources appear in `metadataOnlyMatches` (naddr, title, license, creator/publisher, sourcePage) without text, because their license does not permit quoting — point the user to them by title, license and link, never quote or paraphrase them as content. Cite the license and the creator/publisher with every learning-resource passage. licenseFilter narrows learning resources: "open" = CC0, Public Domain, CC BY, CC BY-SA, their DINI-AG-KIM license-type equivalents, and free software/documentation licenses (GPL, LGPL, AGPL, GFDL, MIT, Apache 2.0, BSD); "cc-any" = all of "open" plus every other Creative Commons license, incl. KIM non-commercial/no-derivatives (normally no metadataOnlyMatches); "any" = also unlicensed and all-rights-reserved resources, as metadataOnlyMatches. Scope is required but simple: with no source restriction from the user, pass the content kinds (e.g. kinds:[30142] for educational resources, [30040,30041] for publications, [30023] for articles). Route source restrictions ("nur Content von X") into the scope parameters: a metadata publisher (most organisations — resolve_publisher finds the exact spelling) goes into `search` as a QUOTED field filter, e.g. search:'publisher.name:"LEHRE LADEN"' (unquoted multi-word names match nothing); a Nostr signing account (resolve_author → pubkey) goes into `authors`. A published grimoire spell (kind 777, nevent or event id) can replace inline scope entirely; the response carries the canonical spell for whatever scope was used — publish it (e.g. via grimoire) to make the scope reusable. Spells may use $me/$contacts; they resolve to the calling user (pass `me` if the transport is anonymous). Fails rather than widening scope: an empty scope, unreachable relay (relay_unreachable — tell the user to retry), or unreachable index is a typed error, never a silently unscoped search.
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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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  • 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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  • Apache JMeter community documentation, conversion tools, linters, and calculators for AI agents.

  • Point Claude Code, Qwen Code, Cursor, or any MCP client at https://docs.jmeter.ai/api/mcp and your agent answers JMeter questions grounded in this documentation, with a source link for every answer. Free, no API key, no signup.

  • 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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  • Topic search across ALL content types on the relay in one ranked call: educational resources (kind 30142), long-form articles/blogs (30023), wikis (30818), projects (30143), measures (30144), and NKBIP-01 publications (30040 indices + 30041 sections — scientific articles, books). Results are interleaved and ranked by semantic passage match, and each carries the matched passage ("snippet") when available — use it to answer the user, not just list links. Every learning resource is returned by default, whatever its license; each resource result carries license ({id, label}, or null when unlicensed) and accessLevel ("full" for Creative Commons, Public Domain and free software/documentation licenses, and DINI-AG-KIM license types that resolve to one of these; "metadata-only" otherwise). Snippets and passages exist only for accessLevel:"full" resources — present a "metadata-only" resource by its title, description, license and link, never quoted beyond its metadata, and always name the license and the creator/publisher. licenseFilter narrows the results: "open" = CC0, Public Domain, CC BY, CC BY-SA, their DINI-AG-KIM license-type equivalents, and free software/documentation licenses (GPL, LGPL, AGPL, GFDL, MIT, Apache 2.0, BSD); "cc-any" = all of "open" plus every other Creative Commons license, incl. KIM non-commercial/no-derivatives; "any" = also unlicensed and all-rights-reserved resources. The other content types carry no license and are always included. This is the tool for DISCOVERY intent — the user wants materials to browse ("finde/suche/empfiehl Materialien zu X"): present the items. For QUESTION intent — the user wants an answer built from the content ("wie/warum/was hilft bei X?") — prefer search_passages, which returns quotable fulltext passages with citations; this tool then supplements the answer with browsable links. Each result carries eventAuthor (the Nostr signer who uploaded the event — often an aggregator) plus, for resources, creator/publisher (who actually made and published the resource); these can differ, so do not treat eventAuthor as the publisher. For full metadata (license, dates, complete entity lists) pass a result's naddr to get_resource. Publication facets ride inside the query string as NIP-50 field filters: append type:academic, doi:10.1234/abcd.5678, keywords:<term>, or partOf:30143:<pubkey>:<d> ("publications of a project") to the query — the relay resolves them server-side. When presenting results to the user, render each as a markdown link so they can open it directly — prefer sourcePage (the original external source page, present on most resources and on projects/measures/publications), then url, then naddr. For upcoming events on the same topic, follow up with search_calendar_events.
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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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  • Turn a recon'd stack into a ranked dig-order for authorized bug-hunting. For each discovered component, returns its top high-risk CVEs (KEV / CISA-SSVC- active / high-EPSS first) AND its `recurring_loci` — the bug classes (CWE) this product family repeatedly fails at, ranked by real in-the-wild exploitation — i.e. WHERE to look first and WHAT shape to expect. Components are ordered by their most-exploitable bug (`dig_rank`). Grounded in exploitation data, not model guesses; lays out facts for triage/where-to-look, never a payload. Banners go in verbatim ("OpenSSH_9.2p1 Debian-2+deb12u3", "Apache/2.4.62 (Debian)"): a Debian/Ubuntu/Alpine build adds `distro_fix`, and CVEs the distro already fixed in that build rank below the rest. Args: technologies: discovered stack, e.g. ["craftcms 4.4", "nginx", "keycloak"]. per_tech: how many top CVEs to surface per component (default 4).
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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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  • 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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  • Import an open-source load test (JMeter .jmx, k6 .js, or Locust .py) into an existing script, converting it to Loadster commands. An import may create script assets, datasets, and a scenario; replace=true also replaces the script's existing commands and variables. Returns action items to review. For OpenAPI/Swagger or Postman, read the spec yourself and build commands with create_script instead.
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