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164,310 tools. Last updated 2026-05-31 06:04

"Model-Controller-Presenter (MCP) for Building iOS Apps" matching MCP tools:

  • Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.
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  • Interactive single-site design-conditions explorer. Returns full ASHRAE design conditions + diurnal chart for the requested scenario. In MCP Apps-capable hosts (Claude Desktop, ChatGPT, VS Code, Goose), the response renders as a widget with sliders for SSP / year / percentile / UHI — dragging a slider re-calls this tool live. Use when a user wants to interactively tune a single site. For multi-site comparison, use analyze_weather(urls=[...]) instead. Defaults to present-day TMY (no morph) — pass ssp+year for future scenarios. P75 default percentile is design-realistic; P50 underestimates the tail. No auth required.
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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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  • Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Lists Vocab Voyage's MCP starter prompts (also exposed via the standard MCP prompts/list endpoint). Useful for hosts that don't yet support prompts/list.
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  • Calculate the maximum buildable area (building envelope) for a lot given zoning constraints. USE WHEN: user asks 'how much can I build', 'max square footage', 'what's the buildable area', 'calculate the envelope', 'how big can my house be', or has specific lot dimensions and zoning rules they want to model. RETURNS: max buildable square feet, max number of stories, envelope dimensions (length × width × height), usable footprint, and coverage math. Takes lot area, setbacks, FAR, height limit, and coverage as inputs — a pure calculation tool, does not query data.
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  • List of 24 top-level categories with i18n labels. USE WHEN: building a category picker, validating ?category= input. Stable enum (rarely changes). For higher-resolution browsing → quizbase_subcategories or quizbase_topics.
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  • Return the AutoConfigController telemetry from the most recent `auto_configure` or `agent_deduplicate` call in this MCP session. Same JSON shape as the web /api/v1/controller/telemetry endpoint.
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  • Search recipes by keyword across titles, descriptions, tags, and full source code. Use for any iOS, SwiftUI, or backend topic — e.g. subscription, authentication, camera, animation, chart, onboarding, paywall, infrastructure.
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  • Search the AI agent directory — find registered agents by name, capability, protocol support, or reputation. Powered by the live ERC-8004 registry via 8004scan (110,000+ agents indexed across 50+ chains). Returns agent identity, owner wallet/ENS, reputation scores, supported protocols (MCP/A2A/OASF), verification status, and links to 8004scan profiles. Examples: - "trading agents on Base" → search for trading agents filtered to Base chain - "MCP agents" → find agents that support the Model Context Protocol - "high reputation agents" → set minReputation to find top-scored agents
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  • Return the Claidex MCP feature map, configured storage/model providers, safety controls, resources, prompts, and tool counts.
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  • List all available dashboard chart types and their required data schemas. Call this before create_dashboard to discover which chart types are supported and what keys your model output must contain for each type. No project access required — returns static metadata only. Note: MCP transport authentication is still required (POST /mcp requires a valid JWT).
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  • LLM-narrated competitive-intelligence BRIEFING — for human consumption (board meeting, pitch prep). Pair tool: `competitive_deep_dive` for raw structured multi-source data (agent-shaped JSON). Returns: recent competitor moves with severity (critical/high/medium/low), prioritised signals, pricing-radar comparison, 3-6 quantified recommendations (impact in € or %, 7/30/90/180-day horizons), and an 8-12 slide presenter script. Use when the buyer wants a narrative briefing or a deck. Inputs: your company (name + one-paragraph pitch) + 1-10 competitors. Delivered by Manue, AI CMO of the Gapup portfolio.
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  • Get the builder workflows — step-by-step state machines for building skills and solutions. Use this to guide users through the entire build process conversationally. Returns phases, what to ask, what to build, exit criteria, and tips for each stage.
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  • Sign up for ThinkNEO MCP free tier or retrieve current account if already authenticated. Idempotent: existing valid token returns current account info, no duplicate created. After signup, configure your MCP client with: Authorization: Bearer <api_key>
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