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306,570 tools. Last updated 2026-07-26 01:33

"Understanding and Building Probability Density Functions (PDF) Models" matching MCP tools:

  • Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.
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  • Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.
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  • Get the building-by-building breakdown for one transaction: footprint area, number of storeys, and estimated total floor area (footprint × storeys) for each building on the property. search_transactions / search_by_area / search_by_polygon return per-transaction building SUMS inline; this tool splits them into individual buildings. Use it after a search when a result has building data and you need the detail (e.g. a developed-land deed covering several buildings). The transaction_id is the id shown on a search result that has building data. Cost: 4 tokens. Returns nothing for a transaction with no buildings.
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  • Returns guidance on diagram-creation workflow and tool selection: how to choose between Mermaid and manual element creation, and the recommended iteration workflow. Consult before building complex diagrams.
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  • Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.
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  • Deep parcel and building analysis for Slovenia using GURS WFS data. Returns zoning, actual use, heritage protection, road access, buildings on parcel, and utilities. USE FOR: - "Analyze parcel 3086 in Ljubljana center" - "Find buildable parcels ~500m² in Ljubljana" - "What buildings are on this parcel?" - "Find parcels near these coordinates" - "Get full details on building 1234" NOT FOR: simple parcel lookup → use slovenia-cadastre instead (faster, lighter). NOT FOR: spatial/zoning map queries → use slovenia-wfs-expert instead. SEARCH MODES — pick ONE per call: 1. PARCEL BY NUMBER (requires --parcel AND --ko) → --parcel 3086 --ko 1725 2. LOCATION SEARCH (requires --lat AND --lon, or --location) → --lat 46.058 --lon 14.501 --radius 100 → --location "Tivoli Park Ljubljana" --radius 200 3. BUILDING BY NUMBER (requires --building, optionally --ko) → --building 1234 --ko 1728 4. COMMUNITY SEARCH (requires at least --community or --size) → --community LJUBLJANA --size 500 --buildable COMMON KO IDs: 1725 = Ljubljana center 1728 = Ljubljana Šiška 1740 = Ljubljana Bežigrad 2131 = Maribor NOTE: This tool makes multiple WFS calls per result and can be slow (10-30s). Use --limit to keep response times reasonable.
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Matching MCP Servers

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    Analyzes text for overused industry jargon, returning buzzword count, density score, severity level, and flagged terms. Optionally provides humorous critique in roast mode.
    Last updated
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    MIT
  • A
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    Classifies development task complexity (LIGHT/MEDIUM/HEAVY) and recommends the most cost-efficient AI model per provider, enabling optimized model selection for coding tasks.
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    26
    MIT

Matching MCP Connectors

  • buzzword-density MCP — wraps StupidAPIs (requires X-API-Key)

  • Fresh US building permits with contacts from official city APIs. Construction lead generation.

  • Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.
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  • 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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  • Sign a PDF: opens an interactive widget where the user draws, types or uploads a signature and places it on the document. Optionally pass signature_name to pre-render a handwritten-style signature. ALWAYS use this for PDF signing requests — never sign or modify the PDF yourself; the user reviews and downloads in the widget. All processing happens locally in the user's browser — the file is never uploaded. Podpisz PDF: narysuj, wpisz lub wgraj podpis i umieść go na dokumencie; plik nie opuszcza przeglądarki.
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  • Generate and archive the certificate PDF for a fingerprint attested in this session with `attest_hash`. The PDF is cryptographically signed, anchored in Bitcoin (OpenTimestamps) and archived server-side; this tool returns the permanent links (the PDF itself is downloadable from its URL — it is never inlined here).
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  • List all available component types and example configurations for building wiring diagrams. Use this to understand what parameters are needed before calling generate_wiring_diagram.
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  • Safely evaluate mathematical expressions with support for basic operations and math functions. Supported operations: +, -, *, /, **, () Supported functions: sin, cos, tan, log, sqrt, abs, pow Note: Use this tool to evaluate a single mathematical expression. To compute descriptive statistics over a list of numbers, use the statistics tool instead. Examples: - "2 + 3 * 4" → 14 - "sqrt(16)" → 4.0 - "sin(3.14159/2)" → 1.0
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  • List every error code in the Trillboards API error catalog. WHEN TO USE: - Understanding what error codes the API can return. - Building a client-side error handler that covers all cases. - Looking up error types, HTTP statuses, and documentation URLs. RETURNS: - object: "list" - data: Array of { code, type, http_status, description, doc_url } - total: Total number of error codes. Equivalent to GET /v1/errors but executed in-process (no HTTP round-trip). EXAMPLE: Agent: "What error codes can the API return?" list_error_codes()
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  • Find the cheapest current models, ranked by input price, output price, or a blended cost. The generic ranking covers generative text models (embeddings, OCR and realtime models are excluded — they price different work); pass category to rank a specific pool instead, e.g. 'embedding'. Use to answer 'what is the cheapest model for <use case>'.
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  • DEPRECATED — use find_low_competition_lanes instead; this alias behaves identically and stays available for existing clients. The old name wrongly implied a win forecast: TendFeed measures observed competition density, not the probability that you win. Data/research tool over TED (Tenders Electronic Daily, CC BY 4.0). No guarantee of award.
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  • Use this tool when the user provides two or more PDF files and wants them combined into one. Triggers: 'merge these PDFs', 'combine these documents', 'join these files into one PDF'. Accepts 2–20 base64-encoded PDFs in order. Returns the merged PDF as a base64 string.
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  • Show which quality dimensions matter for a stated purpose, WITHOUT ranking any models. Returns the inferred weights and the discovery-walk trace. Useful for understanding how XFMS interprets the purpose before committing to a pick.
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  • Latest 6h agent-economy research brief: MCP servers, arxiv papers, trending GitHub agent repos, and trending HuggingFace models, with specific names cited. Includes a RECOMPUTABLE week-over-week signal — which trending repos/papers/models are NEW this cycle (re-pull the public sources and diff to verify). Refreshes every 6h.
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  • Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet 4/4.5, Gemini 2.5 Pro/Flash, DeepSeek V3/R1, Grok 3, and legacy models.
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  • List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.
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