"Learning Programming" matching MCP tools:

• research_papers

  dynamic-feed

  Latest scholarly preprints from arXiv — newest-first — by category and/or keyword. Returns up to 15 papers, each with: title, authors, truncated abstract, primary + all categories, published/updated dates, arXiv id, abstract URL, PDF URL, and DOI / journal reference when a published version exists. `category` = an arXiv taxonomy term (default "cs.AI"). Common ones: cs.AI (AI), cs.LG (Machine Learning), cs.CL (NLP/LLMs), cs.CV (Computer Vision), cs.RO (Robotics), cs.CR (Security), stat.ML, cs.MA (Multiagent). Any valid arXiv category works — see arxiv.org/category_taxonomy. `query` = optional free-text keyword/phrase, AND-combined with the category. Source: arXiv API (Cornell University) — descriptive metadata is CC0 1.0 public domain (keyless, commercial use permitted). arXiv is a PREPRINT server; most papers are not peer-reviewed.

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• me.learning_path

  AI Design Blueprint

  Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.

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• handoffs.agency

  AI Design Blueprint

  Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer <token> (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.

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• aws___search_documentation

  AWS Knowledge

  # AWS Documentation Search Tool Use this tool to find relevant AWS documentation — always follow up with `read_documentation` to get complete answers. Prefer this over general knowledge for AWS services, features, configurations, troubleshooting, and best practices. ## When to Use This Tool **Always search when the query involves:** - Any AWS service or feature (Lambda, S3, EC2, RDS, etc.) - AWS architecture, patterns, or best practices - AWS CLI, SDK, or API usage - AWS CDK or CloudFormation - AWS Amplify development - AWS errors or troubleshooting - AWS pricing, limits, or quotas - Strands Agents development - "How do I..." questions about AWS - Recent AWS updates or announcements **Only skip this tool when:** - Query is about non-AWS technologies - Question is purely conceptual (e.g., "What is a database?") - General programming questions unrelated to AWS ## Skill Suggestions for Actionable Queries When your search query matches tasks that benefit from domain-specific expertise, this tool will suggest relevant **Agent Skills**. Skills package domain knowledge, workflows, best practices, decision frameworks, and reference materials that make you a specialist in a particular AWS domain. **How it works:** - Your search query is scored against the skills registry using semantic search over skill descriptions and metadata tags - If your query matches a skill's domain, relevant skills are returned alongside documentation results - Skills cover a wide range of domains: deployment, troubleshooting, security, optimization, architecture, and more - To load a suggested skill, use the `retrieve_skill` tool with the `skill_name` - Once loaded, follow the skill's workflows and retrieve any referenced files as needed **Example queries that may return skills:** - "deploy a web application to AWS" — may return a deployment skill with architecture guidance and step-by-step deployment instructions - "debug Lambda cold start issues" — may return a troubleshooting skill with diagnostic workflows - "secure S3 buckets" — may return a security skill with best practices and compliance checklists - "optimize API Gateway latency" — may return a performance skill with decision frameworks - "set up VPC peering" — may return a networking skill with step-by-step procedures ## Quick Topic Selection | Query Type | Use Topic | Example | |------------|-----------|-------| | API/SDK/CLI code | `reference_documentation` | "S3 PutObject boto3", "Lambda invoke API" | | New features, releases | `current_awareness` | "Lambda new features 2024", "what's new in ECS" | | Errors, debugging | `troubleshooting` | "AccessDenied S3", "Lambda timeout error" | | Amplify apps | `amplify_docs` | "Amplify Auth React", "Amplify Storage Flutter" | | CDK concepts, APIs, CLI | `cdk_docs` | "CDK stack props Python", "cdk deploy command" | | CDK code samples, patterns | `cdk_constructs` | "serverless API CDK", "Lambda function example TypeScript" | | CloudFormation templates | `cloudformation` | "DynamoDB CloudFormation", "StackSets template" | | Architecture, blogs, guides | `general` | "Lambda best practices", "S3 architecture patterns" | | Strands Agents | `strands_docs` | "Strands Agents Python structured output", "Strands Agents AWS CDK EC2 Deployment Example" | | Domain expertise, workflows, guided procedures | `agent_skills` | "deploy serverless app", "debug Lambda cold starts", "secure IAM policies" | ## Documentation Topics ### reference_documentation **For: API methods, SDK code, CLI commands, technical specifications** Use for: - SDK method signatures: "boto3 S3 upload_file parameters" - CLI commands: "aws ec2 describe-instances syntax" - API references: "Lambda InvokeFunction API" - Service configuration: "RDS parameter groups" Don't confuse with general—use this for specific technical implementation. ### current_awareness **For: New features, announcements, "what's new", release dates** Use for: - "New Lambda features" - "When was EventBridge Scheduler released" - "Latest S3 updates" - "Is feature X available yet" Keywords: new, recent, latest, announced, released, launch, available ### troubleshooting **For: Error messages, debugging, problems, "not working"** Use for: - Error codes: "InvalidParameterValue", "AccessDenied" - Problems: "Lambda function timing out" - Debug scenarios: "S3 bucket policy not working" - "How to fix..." queries Keywords: error, failed, issue, problem, not working, how to fix, how to resolve ### amplify_docs **For: Frontend/mobile apps with Amplify framework** Always include framework: React, Next.js, Angular, Vue, JavaScript, React Native, Flutter, Android, Swift Examples: - "Amplify authentication React" - "Amplify GraphQL API Next.js" - "Amplify Storage Flutter setup" ### cdk_docs **For: CDK concepts, API references, CLI commands, getting started** Use for CDK questions like: - "How to get started with CDK" - "CDK stack construct TypeScript" - "cdk deploy command options" - "CDK best practices Python" - "What are CDK constructs" Include language: Python, TypeScript, Java, C#, Go **Common mistake**: Using general knowledge instead of searching for CDK concepts and guides. Always search for CDK questions! ### cdk_constructs **For: CDK code examples, patterns, L3 constructs, sample implementations** Use for: - Working code: "Lambda function CDK Python example" - Patterns: "API Gateway Lambda CDK pattern" - Sample apps: "Serverless application CDK TypeScript" - L3 constructs: "ECS service construct" Include language: Python, TypeScript, Java, C#, Go ### cloudformation **For: CloudFormation templates, concepts, SAM patterns** Use for: - "CloudFormation StackSets" - "DynamoDB table template" - "SAM API Gateway Lambda" - "CloudFormation template examples" ### strands_docs **For: Strands Agents API reference, integrations, model providers, session managers, tools, examples, user-guide** Use for: - "Strands Agents Python SDK example" - "Strands Agents AWS integration" - "Strands Agents community contributions" - "Strands Agents usage examples" - "Strands Agents usage guide" ### general **For: Architecture, best practices, tutorials, blog posts, design patterns** Use for: - Architecture patterns: "Serverless architecture AWS" - Best practices: "S3 security best practices" - Design guidance: "Multi-region architecture" - Getting started: "Building data lakes on AWS" - Tutorials and blog posts **Common mistake**: Not using this for AWS conceptual and architectural questions. Always search for AWS best practices and patterns! **Don't use general knowledge for AWS topics—search instead!** ### agent_skills **For: Discovering agent skills — domain-specific expertise packages for AWS workflows** Use for: - Complex tasks that benefit from guided workflows: "deploy a serverless application" - Troubleshooting scenarios: "debug Lambda cold starts", "resolve ECS task failures" - Security and compliance: "secure S3 buckets", "review IAM policies for least privilege" - Architecture and optimization: "optimize API Gateway latency", "design multi-region architecture" - When you need domain expertise beyond what documentation provides Skills go beyond documentation — they provide workflows, decision frameworks, best practices, and may include embedded procedures for critical sub-tasks. **Important**: This topic is meant for discovery. Once you identify the skill you need, use `retrieve_skill` tool with the `skill_name` to load the full skill and its reference materials. **Note**: If combined with other topics, skills will be mixed into the documentation results. Use `agent_skills` alone for a clean skill-only listing. ## Search Best Practices **Be specific with service names:** Good examples: ``` "S3 bucket versioning configuration" "Lambda environment variables Python SDK" "DynamoDB GSI query patterns" ``` Bad examples: ``` "versioning" (too vague) "environment variables" (missing context) ``` **Include framework/language:** ``` "Amplify authentication React" "CDK Lambda function TypeScript" "boto3 S3 client Python" ``` **Use exact error messages:** ``` "AccessDenied error S3 GetObject" "InvalidParameterValue Lambda environment" ``` **Add temporal context for new features:** ``` "Lambda new features 2024" "recent S3 announcements" ``` **If the first search does not return results that directly answer the question, refine your query and search again with different terms, a more specific phrase, or a different topic. Try conceptual/architectural topics (general, blogs) if reference docs are too narrow.** **After searching, use `read_documentation` on the top-ranked URLs to verify and complete your answer.** ## Multiple Topic Selection You can search multiple topics simultaneously for comprehensive results: ``` # For a query about Lambda errors and new features: topics=["troubleshooting", "current_awareness"] # For CDK examples and API reference: topics=["cdk_constructs", "cdk_docs"] # For Amplify and general AWS architecture: topics=["amplify_docs", "general"] # For actionable tasks: topics=["agent_skills"] ``` ## Response Format Results include: - `rank_order`: Relevance score (lower = more relevant) - `url`: Direct documentation link — use with `read_documentation` to get the full page content - `title`: Page title - `context`: Partial excerpt only — not the complete documentation. After reviewing results, call `read_documentation` on the most relevant URLs before answering. Do not answer based on the context excerpt alone. ## Parameters ``` search_phrase: str # Required - your search query topics: List[str] # Optional - up to 3 topics. Defaults to ["general"] limit: int = 5 # Optional - max results per topic ``` --- **Remember: When in doubt about AWS, always search. This tool provides the most current, accurate AWS information. But search is only step 1 — always read the full documentation to give complete answers.**

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• me.learning_path

  AI Design Blueprint Doctrine

  Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.

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• lookup_native

  RedM Mcp

  Resolve a RedM/RDR3 SCRIPT native by hash or name — O(1), exact. Use whenever you see `Citizen.InvokeNative(0x...)`, `Citizen.invokeNative('0x...')`, `GetHashKey('NAME')`, or a SCREAMING_SNAKE_CASE native name (e.g. `SET_ENTITY_COORDS`, `GetPedHealth`) in Lua/JS/TS. NOT for game-data hashes (weapon/ped/animation names) — use `grep_docs`. Pass `hash` (0x… optional, case-insensitive) or `name` (exact first, ILIKE substring fallback). Returns name, hash, namespace, return type, params, description, full content, plus `findings[]` — community gotchas linked to that native. Inspect `findings[].id` and call `get_document({path: 'learning:<id>'})` for full body. Also returns `refDocs[]` — enum/flag value tables for that native (the constants to pass for params like flagId/attributeIndex/eventType). When `refDocs[].content` is set, it's the inline enum table — use those values directly. When `content` is null but `refDocs[].fetch` is present, the table was too large to inline — run that exact call (e.g. `get_document({ path: "refdoc:eEventType" })`) to get the full table; `refDocs[].preview` shows the first lines. github entries (no `fetch`) are url-only.

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• me.learning_path

  AI Design Blueprint Doctrine

  Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.

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• read_datasheet

  sheetsdata-mcp

  Read from a component's datasheet. Two modes: **Section mode** (default): Returns a named section. Start with section='summary' to get an overview and a list of available_sections. Then request specific sections by name. Section names are dynamic — any heading in the actual datasheet works (e.g. 'register_map', 'i2c_interface', 'power_management'). If a section name isn't found, automatically falls back to search mode. **Search mode**: Semantic search within the part's datasheet. Best for targeted questions (register bit fields, I2C config, specific specs). Use when you need to find specific information rather than a whole section. First call for a new part triggers extraction (30s-2min). Subsequent calls are cached. **Datasheet vs Reference Manual**: Manufacturer datasheets cover high-level specs, pinout, absolute maximum ratings, and package info. For microcontrollers (STM32, nRF52, RP2040), register-level programming details (I2C CR1/CR2, DMA config, interrupt bits) are in a separate Reference Manual, not the datasheet. The summary's available_sections will show what's actually present. The part_number must be a specific manufacturer part number (e.g. 'TPS54302', 'STM32F446RCT6') or LCSC number (e.g. 'C2837938'). Do NOT pass bare component values ('100nF', '10K'), descriptions, or reference designators. DATASHEET STATUS VALUES: - 'ready' — extracted and indexed; call read_datasheet, search_datasheets, or analyze_image. - 'extracting' / 'in_progress' / 'queued' / 'pending' — extraction running or scheduled. Poll check_extraction_status every 5-10s until 'ready' or 'failed'. Typical time: 30s-2min. - 'not_extracted' — known part but datasheet hasn't been fetched yet. Trigger it via prefetch_datasheets (cheapest) or by calling read_datasheet (auto-triggers on first read). - 'no_source' — we couldn't find a public datasheet URL for this MPN. First, retry prefetch_datasheets in 10-30s (the URL resolver re-runs and often finds a source on the second pass). If still 'no_source', the agent can upload the PDF manually via request_datasheet_upload + confirm_datasheet_upload (see those tools). Org-uploaded datasheets are private to the org. - 'unsupported' — PDF exists but can't be extracted (scanned image-only, encrypted, or corrupted). Upload a clean text-based PDF via request_datasheet_upload to override. - 'failed' / 'error' — extraction errored. The response includes the error reason. Retry via prefetch_datasheets or escalate to support. - 'rejected' — input wasn't a real MPN (bare value like '100nF', description, or reference designator). Fix the input and re-call. - 'deduplicated' — another part in the family already has this datasheet; same content is returned under the primary MPN.

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• search_open_grants

  foundation-discovery

  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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• ceevee_generate_report

  CareerProof MCP

  Generate a CeeVee career-intel report asynchronously (15 credits, takes 2-3 min). Returns report_id and status. POLLING: Call ceevee_get_report(report_id) every 30 seconds, up to 40 times (20 min max). If status='completed', download PDF with ceevee_download_report(report_id). If status='failed', relay the error_message to the user. If still processing after 40 polls, stop and inform the user with the report_id so they can check later. Call ceevee_list_report_types first to discover valid report_type values and required inputs. Report categories: Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, Fractional Leadership.

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• asterwise_get_varshaphal_saham

  Asterwise

  Computes all 10 Tajika Saham (sensitive points) for a Varshaphal solar return chart. Sahams are the Tajika equivalent of Arabic Parts — mathematically derived zodiac points that focus the annual horoscope on specific life themes. SECTION: WHAT THIS TOOL COVERS Saham formula: (A - B + Ascendant) % 360, with a conditional +30° correction applied when the Ascendant does not fall in the forward zodiacal arc from B to A. This conditional is the defining classical Tajika Saham rule — without it, results are wrong. Day and night formulas differ: the Minuend and Subtrahend swap based on whether the solar return falls during daytime or nighttime at the birth location. Punya Saham (Fortune) is always computed first because Yashas (Fame) and Mahatmya (Status) use it as an operand. The Saham lord (planet ruling the sign where the Saham falls) is the Sahamesha — its strength, house placement, and Tajika aspects to the Varsha Ascendant determine whether the theme manifests positively or is obstructed. 10 Sahams returned: punya — Fortune and Luck (Moon-Sun day / Sun-Moon night) vidya — Education and Learning (Sun-Jupiter day) yashas — Fame and Reputation (Jupiter-Punya day) — uses Punya as operand mitra — Friends and Allies (Jupiter-Venus day) mahatmya — Greatness and Status (Punya-Mars day) — uses Punya as operand asha — Desires and Fulfillment (Saturn-Venus day) karmakarya — Action and Profession (Mars-Mercury day) vyapara — Business and Trade (Mars-Saturn day) vivaha — Marriage and Relationships (Venus-Saturn day) santapa — Sorrow and Stress (Saturn-Moon day) SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_varshaphal — understand the base solar return chart (year lord, Muntha, Varsha Ascendant) before interpreting Saham lords. The Saham is meaningless without knowing which house it occupies from the Varsha Ascendant. AFTER: asterwise_get_varshaphal_harsha_bala — assess the Saham lord's positional happiness score to determine ease or difficulty of manifestation. SECTION: INPUT CONTRACT Same as asterwise_get_varshaphal — BirthData plus target_year. target_year (required int): The Gregorian calendar year of the solar return. Not age — the civil year (e.g. 2026). Feeding age instead of year silently produces the wrong return. time (required): Solar return Ascendant is time-sensitive. Accurate birth time is required for reliable Saham interpretation. SECTION: OUTPUT CONTRACT data.target_year (int — calendar year of the solar return) data.ayanamsa (string — ayanamsa system used, e.g. 'lahiri') data.solar_return_utc (string — ISO UTC timestamp of solar return moment) data.is_day_return (bool — true if solar return occurs between sunrise and sunset; determines which Saham formula variant is used) data.varshaphal_ascendant_longitude (float — Varsha Ascendant in degrees; all 10 Saham longitudes are computed relative to this) data.total (int — always 10) data.sahams[] — 10 objects in order [punya, vidya, yashas, mitra, mahatmya, asha, karmakarya, vyapara, vivaha, santapa]: slug (string — lowercase key, e.g. 'punya') name (string — full display name, e.g. 'Punya Saham') theme (string — life area, e.g. 'Fortune and Luck') longitude (float — Saham longitude in degrees 0–360) rashi_index (int — 0–11, 0=Mesha) rashi (string — Sanskrit sign name, e.g. 'Mesha') degree_in_sign (float — degrees within the sign) saham_lord (string — classical lord of the sign where Saham falls) formula_used (string — describes whether day or night formula was applied and which planets were operands, e.g. 'day: Moon - Sun + Asc') (string — methodology note) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS SLOW_COMPUTE — internally runs the full solar return computation (binary-search Sun longitude + house computation) before deriving Sahams. SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): None — upstream rejection surfaces as MCP INTERNAL_ERROR. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Day/night determination uses sunrise/sunset at the birth coordinates for the solar return date. Polar latitudes where sunrise cannot be computed → MCP INTERNAL_ERROR. — target_year is a Gregorian year, not age — always verify the caller passes the civil year. SECTION: DO NOT CONFUSE WITH asterwise_get_varshaphal — returns the full base solar return chart including Muntha, year lord, and planet positions; Saham points are not included there. asterwise_get_varshaphal_harsha_bala — scores planet positional happiness; this tool computes zodiac points, not planet positions. asterwise_get_gemstone_recommendations — birthchart gemstone recommendations, unrelated to Tajika Saham.

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• sti_search

  Stimulsoft Documentation MCP Server

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

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• atlas_technique_lookup

  ContrastAPI

  Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.

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• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

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• calc_statistics

  Math MCP Learning

  Perform statistical calculations on a list of numbers. Available operations: mean, median, mode, std_dev, variance Note: Use this tool to compute descriptive statistics over a list of numbers. To evaluate a single mathematical expression, use the calculate tool instead. Examples: statistics([1.0, 2.5, 3.0, 4.5, 5.0], "mean") # Returns 3.2 statistics([1.0, 2.5, 3.0, 4.5, 5.0], "std_dev") # Returns ~1.58

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• compare_analytical_vs_simulated

  QueueSim

  Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.

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• ceevee_list_report_types

  CareerProof MCP

  List all available CeeVee career-intel report types with descriptions, required/optional input fields, and credit costs. Call this BEFORE ceevee_generate_report to discover valid report_type values and the exact inputs each type requires. Categories include Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, and Fractional Leadership. Free.

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• view-hmr-unit-tests

  hmr

  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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• export_dataset

  Trillboards DOOH Advertising

  Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy. Queries the relevant table based on the selected dataset type, applies filters, enforces k-anonymity by suppressing groups with fewer than 5 observations, and returns structured data. WHEN TO USE: - Exporting audience data for external analysis - Building datasets for machine learning or reporting - Getting structured vehicle or commerce data for a specific time/place - Creating cross-signal datasets for correlation analysis RETURNS: - data: Array of dataset rows (schema varies by dataset type) - metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range } - suggested_next_queries: Related exports or analyses Dataset types: - observations: Raw observation stream data (all families) - audience: Audience-specific data (face_count, demographics, attention, emotion) - vehicle: Vehicle counting and classification data - cross_signal: Pre-computed cross-signal correlation insights EXAMPLE: User: "Export audience data from retail venues last week" export_dataset({ dataset: "audience", filters: { time_range: { start: "2026-03-09", end: "2026-03-16" }, venue_type: ["retail"] }, format: "json" }) User: "Get vehicle data near geohash 9q8yy" export_dataset({ dataset: "vehicle", filters: { time_range: { start: "2026-03-15", end: "2026-03-16" }, geo: "9q8yy" } })

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• query-docs

  xpay✦ DevTools Collection

  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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