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207,082 tools. Last updated 2026-06-17 20:13

"Understanding the concept of 'handle' in different contexts" matching MCP tools:

  • Fetch one contributor's profile card for a GitHub handle not already returned by find_candidates — e.g. the user names a specific person, references an external handle, or wants verification before outreach. find_candidates already returns full inline profiles; use get_profile only for handles outside those results or when the user asks for deeper detail. IMPORTANT — interpreting recent_activities: indexed GitHub activity in the current ingestion window (2025–2026), up to ~20 events per recent project. NOT a complete career history. Empty or older activity does not mean inactive.
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  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • Return a canonical definition for a primitive Eurorack / synthesis concept and its relations to other concepts in the corpus. Use this for VOCABULARY questions, not module questions — when the user is asking what a term means or how two terms relate, not which modules implement it. Typical shapes: - "Is four-quadrant mult the same as through-zero AM?" → lookup_concept("four-quadrant mult") - "What's the difference between a gate and a trigger?" → lookup_concept("gate") - "Modular signal level vs line level — when does it matter?" → lookup_concept("modular signal level") - "Are clock dividers just pulse counters?" → lookup_concept("clock divider") - "Are polyphonic patch cables TRRRRRS?" → lookup_concept("polyphonic cable") Lookup is case-insensitive across three axes, tried in order: the canonical id ("through-zero-fm"), the canonical label ("Through-Zero FM (TZFM)"), and any registered alias ("tzfm", "through zero fm"). Spaces and hyphens are matched literally; the lookup does NOT normalize whitespace beyond lowercasing. If the term doesn't match anything, the response includes up to 5 substring-matched suggestions. Args: - name (string, required, min length 2): the term to look up. Examples: "AM", "ring mod", "four-quadrant mult", "TZFM", "clock divider", "gate", "trigger". Returns: { "concept": { "id": "amplitude-modulation", "label": "Amplitude Modulation (AM)", "description": "A multiplication of two signals: the carrier...", "aliases": ["am", "amplitude modulation", "amplitude mod"], "related_concepts": [ { "related_concept_id": "ring-modulation", "related_concept_label": "Ring Modulation (RM)", "relation_kind": "commonly_confused_with", "note": "AM with a unipolar modulator preserves the carrier..." }, ... ], "source_id": null, "citation_url": "https://learningmodular.com/glossary/...", "citation_quote": "Amplitude modulation is when..." } | null, "_meta": { "query": "<the name argument verbatim>", "matched_via": "id" | "label" | "alias" | "none", "concept_suggestions": [ { "id": "...", "label": "...", "matched_via": "alias", "matched_text": "..." } ], "feedback_hint": "...?" } } Relation kinds: - "related_to" — see-also link (default; symmetric in spirit). - "subtype_of" — X is a specific case of Y (RM ⊂ AM, TZFM ⊂ linear FM). - "inverse_of" — X is the opposite of Y (clock-divider ↔ clock-multiplier). - "commonly_confused_with" — they're distinct, but people conflate them (gate vs trigger, AM vs RM, modular level vs line level). When to cite: every concept carries either source_id or citation_url + citation_quote. Surface the citation when the answer affects a decision (e.g. "the corpus cites learningmodular.com — TRS cables are physically the same connector whether carrying balanced mono or unbalanced stereo; only the destination determines the role"). When the result is null and concept_suggestions are provided, present 2–3 closest matches to the user. If none look right, the corpus genuinely doesn't carry that concept — call report_gap with kind="missing_field" and tool_name="lookup_concept" naming the term and its expected definition.
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  • Use this tool when the user asks BOTH what a financial figure is AND which filing reported it — for example "What was Apple's most recently reported revenue, and which 10-Q filed it?" or "Show me the accession ID for Tesla's latest net income" or "Which filing form reported Amazon's Q3 operating cash flow?" This tool returns a single fact plus its complete filing provenance: entity, concept, period, value, accession ID, filing URL, and form type (10-K, 10-Q, etc.). Use this INSTEAD OF `search_companies` when the user already names a company and wants a financial figure with its source filing — `search_companies` only resolves company identifiers and returns no financial data. Use this INSTEAD OF `get_company_fundamentals` when the user explicitly wants to know which filing or form type reported a number, or needs the accession ID — `get_company_fundamentals` returns metrics across multiple periods but omits filing provenance. Two lookup modes: (1) by fact_id (SHA-256 hash of entity_id|accession_id|concept|period_end|unit) for deterministic identity; or (2) by concept name (e.g., TotalRevenue, NetIncome, EPSDiluted, TotalAssets, OperatingCashFlow) plus a ticker to retrieve the most recently reported fact. Optionally pin a point-in-time cutoff via as_of_date (YYYY-MM-DD) — returns the latest filing accepted by SEC on or before that date, eliminating look-ahead bias. Check `_meta.pit_safe` in the response to confirm PIT correctness. DURATION: income-statement flow concepts (NetIncome, TotalRevenue, etc.) are reported over a window, and a single 10-K tags BOTH a 12-month figure and a 3-month Q4 stub at the same fiscal-year-end period_end. On a tie this tool returns the longer (headline) window, and every result carries `period_type` (instant | quarterly | half_year | nine_month | annual | duration) and `period_span_days` so you always know whether a number is a quarter or a full year — never present a 3-month stub as the annual figure. Provide either fact_id or concept (required). Returns empty result with error_code FACT_NOT_FOUND if no matching fact exists for the given concept and ticker. Available on all plans.
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  • Fetch time-series data for 1–50 BLS series by SeriesID in a single API request (one query against the 500/day limit). Supports optional year range (up to 20 years per request) and BLS-computed period-over-period calculations (net change and percent change; a survey returns whichever it supports — CPI and PPI return percent change only, the inflation rate — so check bls_list_surveys first). When the total observation count would exceed the inline context budget, results spill to a canvas dataframe and the response includes a dataset.name handle for follow-up SQL via bls_dataframe_query. Use bls_search_series first if you need to resolve a concept to a SeriesID.
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  • Retrieve a seller's public profile: name, location (city/region/country), storefront URL, delivery fee, delivery coverage, and catalog size. **Call this before `create_cart` or `set_shipping_address` to validate that the seller ships to the buyer's area or to set expectations about catalog size.** `delivery_coverage` is `{ states, cities, nationwide, state_count, coverage_configured }` — the US state codes (e.g. 'CA', 'NE') and city names the seller delivers to; an address is eligible when its region matches a covered state OR its city matches a covered city. **`coverage_configured: false` means the seller has set up NO delivery yet — they ship NOWHERE; never tell the buyer they ship anywhere (an empty `states` list is NOT nationwide). `nationwide: true` means all 50 states, with `states` omitted to save space.** `delivery_fee_cents` is the flat fee added at checkout; `catalog_size` is the total number of products listed. `delivery_zones` (legacy postal-code list) is still returned for backward compatibility but new agents should read `delivery_coverage`. **For network (cross-seller) tokens, pass `handle` to name which seller you're asking about** (e.g. `handle: 'bay-clothing-district'`). Handle lookup is case-insensitive — 'BayClothingDistrict' and 'bayclothingdistrict' both resolve. Seller-scoped tokens may omit `handle` — their own seller is implicit.
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  • Guardian Open Platform: content search, articles, sections, tags. Free dev key.

  • India Open Government Data (OGD) Platform MCP — data.gov.in

  • Formats a number using the locale conventions of a specific European country, applying the correct decimal separator and thousands separator. Returns { original: number, formatted: string, locale: string, country_code: string }. Different European countries use different conventions — Portugal and most of continental Europe use '1.234,56' (dot as thousands, comma as decimal), while Ireland uses '1,234.56'. Supports PT, ES, FR, DE, IT, NL, BE, PL, SE, DK, FI, AT, IE, GR, HU, RO. Use when displaying prices, measurements, or any numeric value to end users in a specific European country.
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  • Exhaustively survey the WHOLE Tipiṭaka for a term — guaranteed complete. Use this (not `search_by_keyword`) when the question is about **coverage or counting** rather than "show me the best passages": - "How many times does Kusinārā appear in the canon?" - "Every place ānāpānassati is mentioned — don't miss any" - "Which pitakas/how many suttas mention this term?" Unlike `search_by_keyword` (ranked, capped at 50, no total), this returns an **exact count**, a **per-pitaka breakdown**, the **distinct surface forms** that matched (so you can audit and discard over-matches), and a paginated enumeration. The `lexical` result carries `complete: true` — a hard guarantee that nothing was dropped for the chosen `match_scope`. Two layers, two different promises: - **lexical** — the word and its forms. Deterministic + EXHAUSTIVE. - **semantic** (`mode="thorough"`, hosted only) — passages teaching the same concept with DIFFERENT vocabulary (e.g. ānāpānassati via `assasati`/`passasati`). Approximate, **NOT exhaustive** — it never claims completeness, it only boosts recall.
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  • Find a creator by name/handle, while preserving legacy semantic creator search. Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index. If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer `get_profile` first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with `query_type: "creator_lookup"`. For backward compatibility, this tool still accepts the old semantic-search fields (`platforms`, follower/engagement filters, `creator_kinds`) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer `semantic_search_creators` because its name is explicit and less likely to be confused with exact creator lookup. Examples: - User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup". - User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup". - User: "Pull @niickjackson on Instagram" -> use `get_profile` with platform "instagram" and username "niickjackson". - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool. Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with `get_creator`, `find_lookalike_creators`, or `match_creators`; use returned platform usernames with `get_profile` or `get_posts`.
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  • Search EU legislation, treaties, and preparatory acts across the CELLAR corpus of 2.7M+ works. Filters by document type, date range, EuroVoc subject concept, author institution, and in-force status. Keyword search matches against English expression titles and CELEX strings — full-text body search is not available via this API. For multi-word searches, supply a single dominant keyword; use other filters to narrow results. Returns CELEX numbers, work URIs, human-readable document type labels, and dates — use these with eurlex_get_document to fetch full content. To filter by EuroVoc subject, first call eurlex_browse_subjects to obtain the concept URI. Case law (CJEU/GC judgments) is better searched via eurlex_get_cases which has court-specific parameters.
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  • Retrieve a seller's public profile: name, location (city/region/country), storefront URL, delivery fee, delivery coverage, and catalog size. **Call this before `create_cart` or `set_shipping_address` to validate that the seller ships to the buyer's area or to set expectations about catalog size.** `delivery_coverage` is `{ states, cities, nationwide, state_count, coverage_configured }` — the US state codes (e.g. 'CA', 'NE') and city names the seller delivers to; an address is eligible when its region matches a covered state OR its city matches a covered city. **`coverage_configured: false` means the seller has set up NO delivery yet — they ship NOWHERE; never tell the buyer they ship anywhere (an empty `states` list is NOT nationwide). `nationwide: true` means all 50 states, with `states` omitted to save space.** `delivery_fee_cents` is the flat fee added at checkout; `catalog_size` is the total number of products listed. `delivery_zones` (legacy postal-code list) is still returned for backward compatibility but new agents should read `delivery_coverage`. **For network (cross-seller) tokens, pass `handle` to name which seller you're asking about** (e.g. `handle: 'bay-clothing-district'`). Handle lookup is case-insensitive — 'BayClothingDistrict' and 'bayclothingdistrict' both resolve. Seller-scoped tokens may omit `handle` — their own seller is implicit.
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  • Create a new shopping cart on Kifly. **For network (cross-seller) tokens you MUST pass `seller_handle`** — each cart is bound to exactly one seller. Get the handle from search_products results (every item carries `kifly:seller.handle`) or get_seller. Seller-scoped tokens may omit the handle — their own seller is implicit. Returns a cart_id to use with add_to_cart and checkout.
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  • Send a direct message to another handle on rogerthat. Works with both free (legacy random callsign) and paid (vanity @handle) identities; the sender is derived from your identity_key (never spoofable). Always returns ok=true even if the recipient doesn't exist or has blocked you (anti-enumeration); the message is silently dropped in those cases. Offline recipients still get the DM in their inbox (24h retention, 500 msg cap). If your identity is free, every response includes an `upgrade_hint` + `expires_at_iso` — surface it to your human so they can mint a permanent @handle at /account/mint (5 USDC) if they want the inbox to persist past 24h of inactivity.
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  • Fetch a Bluesky actor's public profile by handle (e.g. "bsky.app") or DID (e.g. "did:plc:z72i7hdynmk6r22z27h6tvur"). Returns displayName, handle, DID, bio, follower/following/post counts, avatar URL, moderation labels, and pinned post AT-URI. Use this as the first step to resolve a handle to a DID before calling tools that require a DID or AT-URI. Handles and DIDs are interchangeable as input.
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  • Search the Brazilian CID-10 (Classificação Estatística Internacional de Doenças, 10ª Revisão) by Portuguese text. Use this tool to: - Find CID-10 codes for Brazilian SUS / ANVISA contexts ("infarto", "diabetes", "tuberculose") - Look up the official Portuguese (CBCD/USP) translation of a clinical term - Locate codes for billing, epidemiology, and clinical documentation in Brazil Returns matches from CID-10 categories (3-char) and/or subcategories (4-char). Search is diacritic-insensitive: typing "infeccoes" matches "infecções". This tool searches the Brazilian Portuguese CID-10 V2008 — for the international ICD-11 (current WHO revision, in English by default), use icd11_search.
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  • Retrieve one exact SVG icon when the icon ID and library are already known. Use search_icons first if the user only described a concept. Returns SVG code and public semantic guidance for the exact icon.
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  • Reflect on recent thoughts and patterns. Analyzes recent activity to identify patterns, topics, and insights. Useful for understanding "what have I been thinking about?" By default, only returns user-created memories (not document chunks). Set include_documents=True to also include chunks from uploaded documents. ⚠️ EXPERIMENTAL: - Importance weighting in results not yet implemented. Importance scores are stored but don't affect ranking. Args: time_window: Time period to analyze ('recent', 'today', 'week', 'month', '1d', '7d', '30d', '90d') include_documents: Whether to include document chunks (default: False, only user memories) start_date: Filter memories created on or after this date (ISO 8601: '2025-01-01' or '2025-01-01T00:00:00Z') end_date: Filter memories created on or before this date (ISO 8601: '2025-01-09' or '2025-01-09T23:59:59Z') ctx: MCP context (automatically provided) Returns: Dict with analysis including top memories, active topics, patterns, insights, and any saved contexts (checkpoints) created in the window. Examples: >>> await reflect("recent") {'success': True, 'memories_analyzed': 50, 'active_topics': [...], 'contexts': [...], ...} >>> await reflect("week", include_documents=True) {'success': True, 'memories_analyzed': 150, ...} # includes document chunks >>> await reflect(start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'memories_analyzed': 25, ...} # memories from first week of January
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  • Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs
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  • Fetch a work by Open Library Work ID (OL…W). Returns title, description, subjects, cover IDs, and linked author IDs for follow-up lookups. Works represent the abstract book concept independent of any specific edition. Note: author names are not included — use openlibrary_get_author or openlibrary_search_books for names.
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  • Batch-fetch up to 100 profiles by (platform, username) pairs. Use this when the user has a list of handles and you need profile data for all of them at once (e.g., "give me follower counts for these 30 accounts I'm considering" or "which of @a @b @c are real accounts?"). One round-trip beats 30 calls to `get_profile`. Use this for exact batch handle lookup, not semantic discovery. For one exact platform+username pair, use `get_profile`. For partial or fuzzy handle/name input, use `search_creators` or `autocomplete_creators`. Use `semantic_search_creators` only for topical/niche/audience discovery where false-positive semantic matches are acceptable. Examples: - User: "Compare @a, @b, and @c on Instagram" -> use this tool for the exact handle batch. - User: "Give me follower counts for these 30 accounts" -> use this tool. - User: "Find wellness creators in Austin" -> use `semantic_search_creators`, not this tool. The response splits results into `data` (profiles found) and `not_found` (the (platform, username) pairs that weren't recognized). Profiles are returned in no particular order — re-correlate via the platform/username fields if you need to preserve input order.
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