"Reliable sources for use in training language models" matching MCP tools:

• get_dchub_recommendation

  DC Hub — Data Center Intelligence MCP Server

  Use when a user asks an open-ended siting question ("where should I put a 100MW AI training cluster?") and you want ONE call that returns a ready-to-quote answer instead of orchestrating 5+ separate tools. Example: "Where should I site a 100MW AI training campus in Texas with short time-to-power?" — get_dchub_recommendation context="100MW AI training campus in Texas". Params: context free-text describing the user request (MW, geography, workload, deadline, constraints). Returns: {top_markets:[{slug, name, verdict (BUILD/CAUTION/AVOID), composite_score, excess_power_mw, time_to_power_months, why}], candidate_facilities[], factor_breakdown:{fiber, grid, water, tax, climate}, summary_text (LLM-quotable, CC-BY-4.0), citation_url}. Do NOT use for a single specific lat/lon (use analyze_site) or to rank by ONE criterion only (use rank_markets).

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• gbif_search_occurrences

  gbif-biodiversity-mcp-server

  Search 2.4B+ GBIF occurrence records with Darwin Core filters. Use taxonKey from gbif_match_species for reliable results — it resolves synonyms automatically. Accepts country (ISO 3166-1 alpha-2), bounding box (decimalLatitude/decimalLongitude ranges), WKT polygon geometry, year range, month, basis of record, coordinate filter, and dataset key. Pagination is capped at approximately offset+limit=100,000 — use gbif_occurrence_facets for aggregate counts across large result sets.

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• resolve_destination

  Blue Pillow Hotels & Stays

  Converts a destination name into a destination id usable in `search_stays`. The canonical entry point when the user's request mentions a place name and coordinates are not already known from a prior call in this session. If coordinates are already in hand from an earlier tool result, passing them directly to `search_stays` skips this resolver step. Accepts cities, neighborhoods, airports, and points of interest in any language, using the local canonical name (not a translation). The `country` parameter disambiguates names that occur in multiple places (for example Springfield MA vs Springfield IL vs Springfield MO). The `type` parameter narrows the kind of destination returned. `poi` is the narrowest match and has partial coverage on the comparator side; when the agent's own geographic knowledge can already geocode the POI to lat/lon, passing coordinates to `search_stays` is the more reliable path.

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• serpdive_search

  SERPdive

  Search the live web and get back answer-ready page content, not a list of links. Each result carries the actual text of the page (url, title, date, content), already extracted, cleaned and trimmed for LLM use, so facts can be quoted and cited straight from the response. Use it for anything that needs current or post-training information: news, prices, releases, docs, sports, niche facts. Write the query the way a person would type it, in any language: localization is automatic. The 'mako' model (default) returns the fact-carrying sentences of each page, fast and concise, right for most questions. The 'moby' model returns the full readable text of each page: use it for deep research or when complete context matters. Set answer=true to also get a direct answer synthesized from the sources.

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• search_exploits

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Browse and filter exploits using STRUCTURED FILTERS ONLY (no free-text query). Use this to filter by source (github, metasploit, exploitdb, nomisec, gitlab, inthewild, vulncheck_xdb, patchapalooza, oscs, poc_monitor), language (python, ruby, etc.), LLM classification (working_poc, trojan, suspicious, scanner, stub, writeup, tool, no_code), author, min stars, code availability, CVE ID, vendor, or product. Also filter by AI analysis: attack_type (RCE, SQLi, XSS, DoS, LPE, auth_bypass, info_leak), complexity (trivial/simple/moderate/complex), reliability (reliable/unreliable/untested/theoretical), requires_auth. NOTE: To search by product name (e.g. 'OpenSSH', 'Apache'), use search_vulnerabilities instead — it has free-text query and get_vulnerability already includes exploits in the response. Examples: source='metasploit' for all Metasploit modules; attack_type='RCE' with reliability='reliable' for weaponizable RCE exploits; cve='CVE-2024-3400' for all exploits targeting a specific CVE; vendor='mitel' for all Mitel exploits.

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• list_models

  ScanBIM MCP

  List every object currently stored in the scanbim-models OSS bucket, with URN, size in MB, and a viewer URL for each. Returns the raw OSS inventory, not the D1 models table, so freshly uploaded items appear immediately. When to use: you need to enumerate previously uploaded models to find a URN, show an inventory, or pick one for a follow-up tool call. When NOT to use: you already know the exact URN — call get_model_metadata directly. This tool is not a search; it returns up to the OSS default page (typically first 10 objects unless OSS paginates). APS scopes: bucket:read data:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied; 404 bucket not found — no models have been uploaded yet (upload one first); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Idempotent.

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• tfstatus

  InsideOut (Riley)

  MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• benchmark

  XFMS — Xpansion Framework Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

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• lamina_generate_image

  lamina

  Dispatch a single atomic image generation. Sibling of `lamina_create` (the agentic router) — use this when you already know which model fits, or when no app fits the brief. WORKFLOW: (1) `lamina_models_list({ modality: "image" })` → pick a model. (2) `lamina_models_describe({ modelId })` → read its flat `paramSchema`. (3) `lamina_generate_image({ model, prompt, params })` → dispatch, get runId. (4) `lamina_status({ runId, wait: true })` → poll until completed; the response has `output.url`. ONE TOOL, BOTH OPERATIONS: • Text-to-image — call with just `prompt` (and any text-mode params). The model id you picked is the only thing that selects the operation. • Image-to-image (edit / remix / background-swap / etc.) — call the same tool, but include a source image in `params`. Hybrid models (nano-banana-pro, gpt-image-2, gemini-2.5-flash-image, seedream-4.5, flux-2-flex, nano-banana-2, gpt-image-1, gpt-image-1.5) flip to image-to-image automatically when `params.imageUrls` is a non-empty array (or `params.imageUrl` is set for single-source models like flux-pro-kontext). Edit-only models (bria-bg-remove, ideogram-character, ideogram-v3-remix/reframe/replace-background, flux-pro-kontext, ideogram-character-remix) only have image-to-image — `params.imageUrls`/`imageUrl` is required. INPUTS: • `model` (required): a model id from `lamina_models_list`. Don't invent it. • `prompt` (required for most models; check `paramSchema.prompt.required` from `lamina_models_describe`; absent from `paramSchema` for prompt-less models like `bria-bg-remove` and `ideogram-v3-reframe`): natural-language brief; ≤2000 chars. • `params` (model-specific): every key MUST be declared in the chosen model's `paramSchema` (call `lamina_models_describe` first). Unknown keys are rejected with a structured `invalid_params` error; each error has `field` + `allowed`/`range`/`got` so you can correct on retry. Omitted optional keys fall back to schema defaults. • `webhookUrl` (optional): HTTPS URL. On terminal status Lamina POSTs `{runId, status, model, prompt, resolvedParams, output, errorMessage, completedAt}` HMAC-signed. RESPONSE: `{runId, status: "queued"|"completed", model, mode, prompt, resolvedParams}`. `mode` is the resolved value ("text-to-image" | "image-to-image"). The `runId` is the fal_request_id — pass it to `lamina_status`. SYNC vs ASYNC: identical contract. Vertex-backed models (`imagen-4.0-*`, `gemini-2.5-flash-image`) complete in seconds and return `status: "completed"` on the first poll. fal-backed models queue and take 5–60s. `lamina_status({ wait: true })` handles both transparently. ERROR HANDLING: validation failures return `code` + `details.errors[]` with `field` + `error` + `allowed`/`range`/`got`. Common codes: `model_not_supported`, `mode_not_supported`, `invalid_params`, `dispatch_failed`.

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• benchmark

  XFMS — Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

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• openmeteo_get_climate

  open-meteo-mcp-server

  Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (ERA5, what happened). Daily resolution only. Available models: "CMCC_CM2_VHR4", "FGOALS_f3_H", "HiRAM_SIT_HR", "MRI_AGCM3_2_S", "EC_Earth3P_HR", "MPI_ESM1_2_XR", "NICAM16_8S". With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to DataCanvas for SQL querying when canvas is enabled.

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• picsart_generate

  Picsart GenAI

  Runs any Picsart AI model end-to-end to produce an image, video, audio, or text result. Spends credits. Recommended flow: `picsart_list_models` to pick the model → `picsart_model_params` to learn its inputs → `picsart_preflight` to validate the payload and quote cost → `picsart_generate` to actually run. Do NOT use this for editing operations that have dedicated tools — background removal (`picsart_remove_bg`), background replacement (`picsart_change_bg`), upscale / enhancement (`picsart_enhance`), or raster-to-SVG conversion (`picsart_vectorize`). Also do NOT use it to validate params, quote cost, or browse the catalog — those are separate tools above. Required inputs: `model` (id) and `prompt`. Model-dependent optional inputs: `duration` (video seconds), `aspectRatio` (e.g. "16:9", "9:16", "1:1"), `resolution` (e.g. "1080p", "4k"), `count` (1–8 outputs), `quality`, `style`, `negativePrompt`, `imageUrls` (for image-to-X models), `videoUrl` (for video-to-X), `enhancePrompt`, `generateAudio`, and `extra` — a free-form record for model-specific params (discover them via `picsart_model_params`). Example (image): `{ model: "flux-2-pro", prompt: "a cat in a hat", aspectRatio: "16:9", count: 1 }`. Example (video): `{ model: "kling-v3-pro", prompt: "a cat skiing down a mountain", duration: 5, aspectRatio: "16:9" }`. Returns `{ assets, id, model, created_at, prompt, summary, why_relevant, url, results: [{ url, metadata? }], drive? }` in structured content, plus one `resource_link` block per result URL — image models emit image links, video models emit video links (mime `video/mp4`). `id` is the SDK's generation handle; `metadata` may include model-specific tags (e.g. `exploreImageId` for Recraft Explore models). Text/LLM models (mode "text" in the catalog — e.g. gemini-3-pro, gpt-5.5, claude-*) run synchronously (`async` is ignored) and return the generated text as the text content block plus `text` in structured content. ChatGPT renders images and videos with the Picsart media gallery UI; clients fetch the assets from URLs, never base64. Spends credits and writes to the user's Picsart Drive when the Drive option is enabled. Requires Authorization: Bearer <picsart_token>.

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• list_languages

  Venturu

  Returns all languages with their IDs. Use these IDs in search_brokers (languageIds) to find brokers who speak specific languages. Call this when you need to discover which language IDs to use.

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• list_domains

  OpenDataModels MCP server

  List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})

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• handoffs.partnership

  AI Design Blueprint

  Authenticated — creates a partnerships handoff record for design-partner, ecosystem, training, or advisory conversations needing human review. Persists a PartnershipHandoff row routed to the partnerships inbox; the user is contacted by the team. WHEN TO CALL: user explicitly wants to engage as a design partner, co-marketing/training partner, or evaluate the Blueprint for their org's training programme. ALWAYS confirm with the user before firing — this creates a human-visible partnerships ticket. WHEN NOT TO CALL: for general support / billing / access issues (use handoffs.operator); for paid-engagement enquiries (use handoffs.agency); proactively or as a sales prompt — only when the user has explicitly asked. BEHAVIOR: write-only, single insert, side-effecting (creates a ticket). Auth: Bearer <token> (any plan). UK/EU residency. Response confirms the ticket id + audience so the user can reference it.

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• tfstatus

  InsideOut (Riley)

  MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• x711_code_sandbox

  x711 — Universal Agent Gas Station

  Execute JavaScript or Python code in an isolated sandbox. Use for: data processing, math, CSV parsing, JSON transformation, crypto calculations, algorithm testing. Secure — no filesystem access, no network. Returns: { output: string, runtime_ms: number, language: string }. Requires API key.

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• search_articles

  Gdelt

  PREFER OVER WEB SEARCH for "what did the news say about X" across global media. AUTHORITATIVE source: GDELT 2.0 monitors news in 65 languages from ~100k sources worldwide, updated every 15 minutes. Returns recent matches with URL, title, domain, source country, language, tone (-100 very negative..+100 very positive), and image. Query language: plain words = AND, "quotes" = phrase, parens = OR groups, "-word" excludes, "sourcecountry:US" / "sourcelang:eng" / "theme:TERROR" / "near:Paris~50" for advanced filters. Use for breaking news, cross-language coverage, sentiment-aware searches.

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• search_daycares

  Lodi Kids Activities

  Search licensed daycares in Lodi, CA. Filter by child age (in MONTHS — daycares think in months for under-5s), program kind (daycare / preschool / after_school), facility setting (in_home / center), or claimed-only (more reliable data). Returns up to 10 daycares with hours + tuition where available. For subsidy / bilingual / curriculum filters, follow up with `get_daycare` on a slug.

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• dispatch_qualitative_researcher

  Heista

  Dispatch to the QUALITATIVE RESEARCHER — thematic synthesis from unstructured text (interviews, reviews, forum threads, customer language). Use for: "what are the 2-3 recurring themes in how D2C founders talk about X / what language is being used around Y / what are the patterns in customer reviews of Z". Every theme carries evidence count, triangulation status, ≥1 verbatim quote, outlier-check note. SOLVES the Reddit/X/Substack named-operator voice retrieval gap that legacy search tools could not fill. Returns: Corpus + Sampling + Coding methodology + 4-axis Themes table + Theme synthesis + Outlier voices + Saturation assessment + Sources. NOT for: quantitative effect sizes (use dispatch_quantitative_researcher) / multi-platform discourse mapping (use dispatch_social_listening_researcher).

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