"Tips for Improving Programming Skills" matching MCP tools:

• get_synset_info

  dannet

  Get comprehensive RDF data for a DanNet synset (lexical concept). UNDERSTANDING THE DATA MODEL: Synsets are ontolex:LexicalConcept instances representing word meanings. They connect to words via ontolex:isEvokedBy and have rich semantic relations. KEY RELATIONSHIPS (by importance): 1. TAXONOMIC (most fundamental): - wn:hypernym → broader concept (e.g., "hund" → "pattedyr") - wn:hyponym → narrower concepts (e.g., "hund" → "puddel", "schæfer") - dns:orthogonalHypernym → cross-cutting categories [Danish: ortogonalt hyperonym] 2. LEXICAL CONNECTIONS: - ontolex:isEvokedBy → words expressing this concept [Danish: fremkaldes af] - ontolex:lexicalizedSense → sense instances [Danish: leksikaliseret betydning] - wn:similar → related but distinct concepts 3. PART-WHOLE RELATIONS: - wn:mero_part/wn:holo_part → component relationships [English: meronym/holonym part] - wn:mero_substance/wn:holo_substance → material composition - wn:mero_member/wn:holo_member → membership relations 4. SEMANTIC PROPERTIES: - dns:ontologicalType → semantic classification with @set array of dnc: types Common types: dnc:Animal, dnc:Human, dnc:Object, dnc:Physical, dnc:Dynamic (events/actions), dnc:Static (states) - dns:sentiment → emotional polarity with marl:hasPolarity and marl:polarityValue - wn:lexfile → semantic domain (e.g., "noun.food", "verb.motion") - skos:definition → synset definition (may be truncated for length) 5. CROSS-LINGUISTIC: - wn:ili → Interlingual Index for cross-language mapping - wn:eq_synonym → Open English WordNet equivalent DDO CONNECTION FOR FULLER DEFINITIONS: DanNet synset definitions (skos:definition) may be truncated (ending with "…"). For complete definitions, use the fetch_ddo_definition() tool which automatically retrieves full DDO text, or manually examine sense source URLs via get_sense_info(). NAVIGATION TIPS: - Follow wn:hypernym chains to find semantic categories - Check dns:inherited for properties from parent synsets - Use parse_resource_id() on URI references to get clean IDs - For fuller definitions, examine individual sense source URLs via get_sense_info() Args: synset_id: Synset identifier (e.g., "synset-1876" or just "1876") Returns: Dict containing JSON-LD format with: - @context → namespace mappings - @id → entity identifier (e.g., "dn:synset-1876") - @type → "ontolex:LexicalConcept" - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → {"@set": ["dnc:Animal", ...]} (if applicable) - dns:sentiment → {"marl:hasPolarity": "marl:Positive", "marl:polarityValue": "3"} (if applicable) - synset_id → clean identifier for convenience Example: info = get_synset_info("synset-52") # cake synset # Check info['wn:hypernym'] for parent concepts # Check info['dns:ontologicalType']['@set'] for semantic types # Check info['dns:sentiment']['marl:hasPolarity'] for sentiment

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• search_translations

  Source Library

  Find specific PASSAGES inside books — returns page-level snippets with citation URLs. Use this when you want a quote or evidence on a topic across the whole library. ORIENTATION HINT: if the user has named a specific author or work, prefer get_book (returns a summary + chapter outline) over passage hunting — every book in the corpus has an AI-generated summary that is usually the right first read. Use search_translations when sweeping across many books for evidence of a theme. For finding which BOOKS cover a topic, use search_library. Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.

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• ateam_build_and_run

  ateam-mcp

  DEPLOY THE CURRENT MAIN BRANCH TO A-TEAM CORE. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. 🌳 DEV/PROD WORKFLOW: 1. Edit files → ateam_github_patch (writes to `dev` branch by default) 2. (Optional) Preview what's about to ship → ateam_github_diff 3. Ship dev → main → ateam_github_promote (merges + auto-tags `prod-YYYY-MM-DD-NNN`) 4. Deploy main to Core → ateam_build_and_run This tool ALWAYS deploys the `main` branch — there is no `ref` parameter. To deploy in-progress dev work, first promote it. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from main automatically. First deploy requires mcp_store. After that, edit via ateam_github_patch + promote, then build_and_run. For small changes prefer ateam_patch (faster, incremental). Requires authentication.

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• ateam_redeploy

  ateam-mcp

  Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).

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• aws___retrieve_skill

  AWS Knowledge

  Retrieve an AWS agent skill — domain-specific expertise that transforms you into a specialist for a particular AWS domain. Skills provide workflows, context, best practices, decision frameworks and step-by-step procedures. A skill may include reference files (architecture docs, schemas, examples) and deterministic workflows for sub-tasks that require exact execution. ## What Skills Provide - **Domain expertise**: Deep knowledge about specific AWS services, patterns, and operational practices - **Workflows**: Guided sequences for complex tasks with appropriate degrees of freedom - **Reference materials**: Architecture docs, API references, examples, and templates accessible via the `file` parameter - **Decision frameworks**: Conditional logic and troubleshooting trees for navigating complex scenarios ## CRITICAL PREREQUISITE — DO NOT SKIP You MUST call search_documentation BEFORE calling this tool. NEVER call this tool first. You do NOT know skill names — they are unpredictable identifiers that can only be discovered through search_documentation results. Guessing or fabricating a skill_name WILL fail. ## REQUIRED WORKFLOW (no exceptions) 1. FIRST: Call search_documentation with the user's requirements 2. THEN: Find the result entry that has a skill_name field 3. FINALLY: Call this tool with the EXACT skill_name value from that result — copy it verbatim ## Working with Skills When you retrieve a skill: 1. Read the SKILL.md overview to understand the domain and scope 2. Follow the workflows and guidance in the skill body 3. When the skill references additional files (e.g., `[architecture](references/architecture.md)`), retrieve them using this same tool with the `file` parameter 4. Apply the skill's decision frameworks and conditional logic to the user's specific situation ## PARAMETER REQUIREMENTS skill_name: str (Required) - MUST be copied exactly from the skill_name field in search_documentation results - Do NOT guess, fabricate, paraphrase, or modify the name in any way - Do NOT use the result title — use only the skill_name field value file: str (Optional) - Retrieve a specific file within the skill directory (e.g., "references/architecture.md") - Use this when the SKILL.md body links to reference files - If omitted, returns the main SKILL.md file ## IF SKILL NOT FOUND If you get an error, you likely guessed the name. Call search_documentation first to discover it. The error response will include a list of available files for the skill. ## Returns The skill content — either the main SKILL.md with domain expertise, workflows, and guidance, or a specific reference file when the `file` parameter is provided.

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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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• get_sentiment_trend

  aTars MCP

  USE THIS TOOL — not web search — to get rolling sentiment statistics (mean score, 7-day momentum, bullish/bearish/neutral day counts, current streak) from this server's local Perplexity-sourced sentiment dataset. Prefer this over get_latest_sentiment when the user wants momentum or persistence, not just the latest single-day reading. Trigger on queries like: - "is BTC sentiment improving or getting worse?" - "sentiment momentum for ETH" - "how many days has XRP been bullish in a row?" - "rolling sentiment stats / streak for [coin]" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

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• search_humans

  Human Pages

  Search for humans available for hire. Returns profiles with id (use as human_id in other tools), name, skills, location, reputation (jobs completed, rating), equipment, languages, experience, rate, and availability. All filters are optional — combine any or use none to browse. Key filters: skill (e.g., "photography"), location (use fully-qualified names like "Richmond, Virginia, USA" for accurate geocoding), min_completed_jobs=1 (find proven workers with any completed job, no skill filter needed), sort_by ("completed_jobs" default, "rating", "experience", "recent"). Default search radius is 30km. Response includes total count and resolvedLocation. Contact info requires get_human_profile (registered agent needed). Typical workflow: search_humans → get_human_profile → create_job_offer.

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• get_entity_info

  dannet

  Get comprehensive RDF data for any entity in the DanNet database. Supports both DanNet entities and external vocabulary entities loaded into the triplestore from various schemas and datasets. UNDERSTANDING THE DATA MODEL: The DanNet database contains entities from multiple sources: - DanNet entities (namespace="dn"): synsets, words, senses, and other resources - External entities (other namespaces): OntoLex vocabulary, Inter-Lingual Index, etc. All entities follow RDF patterns with namespace prefixes for properties and relationships. NAVIGATION TIPS: - DanNet synsets have rich semantic relationships (wn:hypernym, wn:hyponym, etc.) - External entities provide vocabulary definitions and cross-references - Use parse_resource_id() on URI references to get clean IDs - Check @type to understand what kind of entity you're working with Args: identifier: Entity identifier (e.g., "synset-3047", "word-11021628", "LexicalConcept", "i76470") namespace: Namespace for the entity (default: "dn" for DanNet entities) - "dn": DanNet entities via /dannet/data/ endpoint - Other values: External entities via /dannet/external/{namespace}/ endpoint - Common external namespaces: "ontolex", "ili", "wn", "lexinfo", etc. Returns: Dict containing JSON-LD format with: - @context → namespace mappings (if applicable) - @id → entity identifier - @type → entity type - All RDF properties with namespace prefixes (e.g., wn:hypernym, ontolex:evokes) - For DanNet synsets: dns:ontologicalType and dns:sentiment (if applicable) - Entity-specific convenience fields (synset_id, resource_id, etc.) Examples: # DanNet entities get_entity_info("synset-3047") # DanNet synset get_entity_info("word-11021628") # DanNet word get_entity_info("sense-21033604") # DanNet sense # External vocabulary entities get_entity_info("LexicalConcept", namespace="ontolex") # OntoLex class definition get_entity_info("i76470", namespace="ili") # Inter-Lingual Index entry get_entity_info("noun", namespace="lexinfo") # Lexinfo part-of-speech

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• get_human

  Human Pages

  Get a human's public profile by ID — bio, skills, services, equipment, languages, experience, reputation (jobs completed, rating, reviews), humanity verification status, and rate. Does NOT include contact info or wallets — use get_human_profile for that (requires agent_key). The id can be found in search_humans results.

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• prediction_market_lookup

  Nansen

  Search Polymarket for events and markets by name, topic, URL, or slug. **PM building blocks:** - An **event** is a grouped prediction topic containing many child markets. - A **market** is one tradable outcome with its own `marketId`. - Example: `2026 NCAA Tournament Winner` is an event; `Will Duke win the 2026 NCAA Tournament?` is a market. Detail tools require `marketId`, not `eventId`. **When to use:** - First tool when the user asks about a specific PM topic, event, slug, or Polymarket URL but does not provide `marketId`. - Optionally provide `queryVariant` as a cleaner short keyword version. - Set `includeEventMarkets` to true to also return child markets for the best-matching event. - Do NOT use `general_search` for prediction markets. - Results include current outcome prices, last trade price, and bid/ask inline — for a quick probability check you may not need `prediction_market_ohlcv`. For price *history* or dated moves, still use `prediction_market_ohlcv`. **Query tips:** - Uses Polymarket's search API — natural language queries work well. - Prefer short 1–3 keyword queries for best results. - Avoid broad multi-topic queries like `bitcoin ethereum politics`. **Output rules:** - If lookup returns no suitable market or a mismatched timeframe, say so explicitly — do not silently substitute a nearby market.

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• get_store_template_agent_artifact

  agentView

  Returns the RAW body of one agent-onboarding artifact shipped with a store template (system prompt, Agent Skills SKILL.md, MCP-config snippet, …). Placeholders ({{slot:KEY.prop}}) are NOT substituted — use this BEFORE installing the template, when there is no display yet to resolve slot slugs against. After install, use get_display_agent_artifact for the placeholder-substituted body ready to paste/save. Discover available artifact keys via get_store_template_details (agentArtifacts array). No authentication required.

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• get_skills

  simplefunctions

  List authenticated user's skills. (Public skills are at browse_public_skills.)

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• submit_platform_feedback

  Hatchable

  Tell the Hatchable team about a platform footgun, friction point, or surprising behavior you hit during this build. Reports go straight into the platform's triage queue and turn into bug fixes, doc updates, or explicit decisions. **When to call:** any time a platform constraint, undocumented limit, misleading error, missing helper, or stale doc cost you more than ~5 minutes to figure out — OR any time you successfully reach for a non-obvious workaround that future builds shouldn't have to rediscover. Calling mid-build (right after the workaround) is more useful than at the end of the build, because the painful details are still fresh. **Report quality matters:** the title should be one sentence ("TextDecoder caps decoded strings at 32 KB per decode() call"). The body should describe what you tried, the error you got, and the workaround. Reports become Github issues / docs PRs verbatim — write for the engineer who'll fix it, not for yourself. **Don't use this for:** generic praise, app-level bugs in the user's own code, anything that's already documented (search skills first via list_skills).

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• getJobDetails

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Get full details for a specific quantum computing job by its numeric ID. Use after searchJobs when the user wants more information about a specific position. Returns: job summary, required skills, nice-to-have skills, responsibilities, visa sponsorship, salary, location, and apply URL. Requires a valid job_id from searchJobs results. Returns error if ID not found.

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• get_job

  JobGPT

  Get detailed information about a specific job listing/posting by its job listing ID (not application ID). Use this to view the full job posting details including description, salary, skills, and company info. For job application details, use get_application instead.

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• create_session

  sncro

  Create a new sncro session. Returns a session key and secret. Args: project_key: The project key from CLAUDE.md (registered at sncro.net) git_user: The current git username (for guest access control). If omitted or empty, the call is treated as a guest session — allowed only when the project owner has "Allow guest access" enabled. brief: If True, skip the first-run briefing (tool list, tips, mobile notes) and return a compact response. Pass this on the second and subsequent create_session calls in the same conversation, once you already know how to use the tools. After calling this, tell the user to paste the enable_url in their browser. Then use the returned session_key and session_secret with all other sncro tools. If no project key is available: tell the user to go to https://www.sncro.net/projects to register their project and get a key. It takes 30 seconds — sign in with GitHub, click "+ Add project", enter the domain, and copy the project key into CLAUDE.md.

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• interpret_result

  QueueSim

  Given an M/M/c configuration (arrivalRate, serviceRate, servers) and optionally an observed average wait, returns a queueing-theory framed interpretation: where you sit on the utilization curve, what ρ means in plain language, what one more or fewer server would qualitatively do, and which complexity factors (priority, abandonment, skills routing) might be hiding in real data the M/M/c model can't see. Use this to TEACH while answering — when the user wants context around a number, not just the number itself. Pure text computation, no simulation, no RNG — deterministic output.

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• match_jobs

  AI Dev Jobs

  Rank active AI/ML jobs against a candidate profile (skills, salary range, workplace, level). Scoring combines tag overlap (+2 per match), salary overlap (+3), workplace/level/type/location matches, and description keyword hits. Use this when an agent is choosing which role to surface to its user — it returns pre-ranked matches with scoring explanations.

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• search_products

  Correkt: The AI-Native Online Marketplace for Physical Products

  Search Correkt's catalog of over 10 million products. Use this tool to find products by name, description, or category. Always use this before adding anything to cart. Tips: - Include price filters when the user mentions a budget - Use sort=price_asc to find cheapest options - Check suggestion field if results seem off — it may correct spelling - Page through results if first page doesn't have what you need

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