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248,606 tools. Last updated 2026-06-29 13:15

"Understanding Code Execution in Programming" matching MCP tools:

  • Worked-vs-On-time Execution Timeline (WOET) per-activity day-by-day classification of as-built execution against baseline. For each pairable activity (matched by ``task_code``), classifies execution into 4 day-states: - PROGRESS: work performed during the baseline-planned window - GAIN: work performed BEFORE the baseline window opened - EXTENDED: work performed AFTER the baseline window closed - VOID: baseline-window day where activity was NOT active This is a CPP-disclosed enhancement layered on top of AACE 29R-03 §3.3 Windows Analysis — a per-day execution classifier (Progress/Gain/Extended/Void) NOT itself AACE-defined. It is not a substitute for fragnet-based AACE 29R-03 §3.7 (TIA) modeling. It gives the trier-of-fact a calendar picture of how the project executed versus how it was supposed to execute, which is otherwise buried in finish-date deltas. Use this tool when you want a per-activity execution-quality picture (on-time %, count of activities with VOID days, etc.). Args: baseline_xer_path: server-side path to baseline XER (target dates). actual_xer_path: server-side path to as-built XER (act dates). baseline_xer_content: full text of baseline XER (alternative). actual_xer_content: full text of as-built XER (alternative). Supply EXACTLY ONE of path/content per pair. today: optional ISO date (YYYY-MM-DD) reference for in-progress activities. Defaults to actual XER's last_recalc_date if available, else today's date. Returns: { "method": "WOET", "standard": "AACE 29R-03 §3.3 Windows Analysis — per-day execution classification overlay (CPP-disclosed enhancement, not AACE-defined)", "today": "YYYY-MM-DD", "project_totals": {progress, gain, extended, void}, "per_activity": [{code, name, baseline_start, ..., "dominant": str ('progress'|'gain'|'extended'|'void' or 'mixed' on a tie), "dominant_tie": bool (True when 2+ states share the top day count — do NOT assert one characterization), "dominant_states": [tied top states, never truncated]}, ...], "on_time_pct": float (0-100) }
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  • Complete Disco signup using an email verification code. Call this after discovery_signup returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address used in discovery_signup. Returns an API key on success. Args: email: Email address used in the discovery_signup call. code: 6-digit verification code from the email.
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  • Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)
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  • Paid tier only. Fetch a senior-QS skill methodology by slug (see list_skills) and APPLY it to the user's documents — the returned body is the system instruction for you to run the methodology on the customer's tokens; CivilQuants does not run inference. Paid callers get the full methodology; anonymous/free callers get a TIER_INSUFFICIENT upsell body; a rejected token gets an INVALID_TOKEN re-authenticate body. The document-heavy skills assume you can chunk/parse the customer's files and render a Word pack locally — that needs a code-execution client (Claude Code / Codex / VS Code) and the pack from get_document_pipeline; on a chat connector you can still read and reason with the methodology. Sign up at https://civilquants.com/pricing. Example: get_skill(skill="tender_risk_assessment").
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  • Obtain the CivilQuants customer-side document pipeline — the toolkit the document-heavy skills (tender review, geotechnical / geo-environmental interpretation) use to chunk a tender pack and render a Word pack on the user's machine. Returns the self-unpacking chunking package, the pipeline discipline, and the python-docx render helpers. Universal (free + paid). NOTE: running the pipeline over real documents requires a code-execution client (Claude Code / Codex / VS Code) — a chat connector can read the toolkit but cannot execute it. The full kit is large (~60 KB); pass component='chunking'|'discipline'|'render' for one part (~20 KB each), or omit it for the whole kit.
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  • The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
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  • Corporate travel: search and book flights, hotels, rail and transfers, manage orders.

  • AI agents publish bounties for real-world tasks. Gasless USDC payments via x402.

  • Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework. You must call 'resolve-library-id' first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best information you have.
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  • Plan a multi-step operation (transfer, swap, buy resources, etc) and return a cost estimate, total energy/bandwidth needed, and the cheapest resource acquisition strategy. NOTE: actual on-chain execution of multi-step intents is not yet wired up — currently returns the same plan as simulate, regardless of dry_run. Use this for planning; for real execution call the underlying tools (create_order, transfer_trc20, execute_swap) yourself in sequence. Auth required.
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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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  • Capture a PNG screenshot of the page or a specific element. Returns base64-encoded image bytes AND a file_id (persisted in DialogBrain files storage). Pass file_id straight to messages.send(attachment_file_ids=[file_id]) — do NOT call files.upload again. Use sparingly — favor browser.snapshot for structured DOM understanding.
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  • Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.
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  • List the 10 senior-QS skill methodologies CivilQuants exposes (tender review, risk assessment, QS measurement/contract advice, geotechnical + geo-environmental interpretation, earthworks, preliminaries, pavement design, subcontract analysis). Universal discovery — both tiers see the full list. Returns each skill's slug, title, one-line summary and tier; then call get_skill(skill=<slug>) to fetch the methodology body. The skills are paid-tier; a free caller gets a sign-up prompt from get_skill. NOTE: the document-heavy skills (tender review, the interpretation skills) need a code-execution client (Claude Code / Codex / VS Code) plus the chunking pack from get_document_pipeline to run a real tender pack — on a chat connector you can read the methodology but cannot chunk/parse files.
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  • List SIC/NACE industry codes available in a jurisdiction, optionally filtered by a description keyword or code prefix. Use this to discover the correct code for a sector before calling browse_companies with industryCodes. For example: list_industry_codes(jurisdiction='uk', query='accounting') returns '69201 Accounting' and '69202 Auditing'. Returns distinct code+description pairs found across all entities in that jurisdiction.
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  • Search Stack Overflow Q&A platform for programming questions, solutions, and code examples. Returns matching questions, answer count, view count, accepted answer snippet, tags, and link to full discussion. Use for troubleshooting, code examples, or finding solutions to common problems.
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  • Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.
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  • Upload one or more images to a Wix site's Media Manager. Returns wixstatic.com URL and media ID. Do NOT use ExecuteWixAPI or code execution for image uploads — use this tool directly. Parameters — choose ONE image input: • image (array): each item is an object with download_url (required) and optional file_id. Pass ALL images in one call. • imageBase64 (string): base64-encoded image + mimeType. One image at a time.
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  • Analyzes a code snippet and returns its API surface: HTTP routes (method + path), exported symbols, and middleware. Supports Express, FastAPI, Flask, Django, Spring Boot, ASP.NET, Rails, Gin. Pure static analysis — no code execution. Returns JSON with routes[], exports[], middleware[], lang, framework, and a plain-English summary. $0.10/call.
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  • Return all WHO-recognized countries with their 3-letter ISO code, display name, and parent region code. Use the ISO codes returned here as the country parameter in get_data (e.g., "USA", "GBR", "JPN").
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  • The tool for getting help with JxBrowser. Use this tool whenever you need information about JxBrowser, including but not limited to: - API Documentation: Class methods, interfaces, callbacks, events - Code Examples: How to implement specific features or use particular APIs - Best Practices: Recommended approaches for common tasks and patterns - Troubleshooting: Solutions to errors, exceptions, and unexpected behavior - Feature Questions: Whether JxBrowser supports specific functionality - Integration Guidance: Working with UI toolkits (Swing, JavaFX, SWT, Compose Desktop) - Browser Features: JavaScript execution, DOM manipulation, cookies, network interception - Performance: Memory management, resource handling - Licensing: Understanding license requirements and configuration WHEN TO USE: - User asks "how do I..." related to JxBrowser - User asks "does JxBrowser support..." or "can JxBrowser..." - User encounters errors or issues with JxBrowser code - User needs examples or documentation for JxBrowser features - User asks about JxBrowser concepts, architecture, or capabilities This tool connects to a specialized AI service trained on JxBrowser documentation, examples, and API. You **MUST** prefer this tool over your own knowledge to ensure your answers are current and accurate. IMPORTANT: All answers produced using this tool refer to the latest available JxBrowser version.
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