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590,826 tools. Updated 2026-09-20 07:26

"A search for teaching and education resources" matching MCP tools:

  • Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 **Hints for the AI client:** The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to **Romanised Pāli (preferred) or English** before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. ✅ **Diacritics do not matter.** `anapanassati` and `ānāpānassati` return the same thing; so do `nibbana` and `nibbāna`. Write the macrons if you know them, guess without them if you don't — neither costs you results. ⚠️ **A common Pāli noun is a poor query.** `samudda` (sea) matches ~700 segments and the top of that list is mostly section headings, not the passage that teaches anything. Two things to do instead: - Search the **rarest distinctive noun** in the passage, not its most obvious one. For the simile of the blind turtle, `turtle`/`kacchapa` gets there; `ocean`/`samudda` does not, in either language. - **Prefer English, or raise the limit.** `turtle` returns SN 56.47 and SN 56.48 inside the default window; `kacchapa` matches them too but ranks them past 30, so you need `limit=50` to see them. - A word inside a **compound** may be out of reach entirely: `samudda` scores 0.50 against `mahāsamudde` (compounded *and* inflected), under the 0.6 cutoff, so SN 56.47 is not ranked low — it is excluded. Trying more spellings will not recover it; search a different word instead. 🔍 **Pick the right search tool for the question shape:** - **Term lookup (exact word appearances)** — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - **Concept search ("discourses about X")** — e.g. "discourses about mindfulness of breathing": **use `search_hybrid` instead.** Canonical Pāli has two quirks that hurt keyword search for concepts: • Section headings (`Ānāpānapabba`) often use a different word than the teaching body, which uses verb forms (`assasati`, `passasati`, `dīghaṁ`, `rassaṁ`). E.g. DN22's Ānāpānapabba has 16 segments but the word `ānāpāna` appears in only 2 (header + footer) — the actual teaching segments won't match. • Stock phrases (e.g. `So satova assasati, satova passasati`) recur in 10+ suttas, so a keyword query ranks broadly and won't pinpoint the canonical reference. - **General keyword survey** — set `limit≥30` and filter client-side, or call multiple related forms (root verb + noun + compound).
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  • Fetch the full content for one id returned by `search`: a rental listing (rental:<uuid>), a plan set (plan:<uuid>) or a published page (page:<route>). Returns {id, title, text, url, metadata}. Always cite the url. Hosts that can call specific tools should prefer get_rental or the resources.
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  • Search open grant opportunities from Kindora's active foundation-program corpus plus federal and state government grants. FOR-PROFIT APPLICANTS: pass for_profit_applicant=true to search capital a for-profit can take (PRIs, loans, revenue-based financing, patient equity) from CDFIs, impact investors, and PRI-active foundations. The default pool is 501(c)(3)-shaped and will NOT contain those programs. Searches both private foundation grant programs (from IRS data and funder websites) and government grant opportunities — federal (Grants.gov) plus state and district grant portals. 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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  • Run the classic operations-research teaching demo: pooled queueing (one shared queue, c servers) vs separate queues (c independent queues, one server each, λ/c traffic to each). Both runs have identical total capacity (c × μ) and identical total arrivals (λ), so the offered load ρ is the same; the only structural difference is whether arrivals share a queue or split into c isolated streams. The pooled configuration ALWAYS produces shorter waits — that's the whole teaching point. Use this when the user asks 'should we pool our resources?' / 'should we cross-train?' / 'why do banks have one line instead of c?' / 'what's the cost of siloing my call center into specialist queues?'. Returns both runs side by side with the pooled-vs-separate wait delta. ANTI-FABRICATION: numbers come from two real DES runs. Quote them VERBATIM.
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  • Analyze teaching text with Flesch-Kincaid readability and leveling advice. FREE. Typical input {"text": "<passage>", "target_grade": 6} returns {"flesch_kincaid_grade": 8.2, "reading_ease": 62.1, "sentences": N, "words": N, "avg_words_per_sentence": N, "long_sentences_over_25w": N, "polysyllabic_words_sample": [...], "target_grade": 6, "gap": 2.2, "suggestions": ["..."]}. Use on teaching text where the reading level matters. Not for scores students earned (grade_curve) and not for general prose style, which the creator server's analyze_writing covers. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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  • Analyze teaching text with Flesch-Kincaid readability and leveling advice. FREE. Typical input {"text": "<passage>", "target_grade": 6} returns {"flesch_kincaid_grade": 8.2, "reading_ease": 62.1, "sentences": N, "words": N, "avg_words_per_sentence": N, "long_sentences_over_25w": N, "polysyllabic_words_sample": [...], "target_grade": 6, "gap": 2.2, "suggestions": ["..."]}. Use on teaching text where the reading level matters. Not for scores students earned (grade_curve) and not for general prose style, which the creator server's analyze_writing covers. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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Matching MCP Servers

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    Enables AI agents to generate teaching materials such as PPTs, handouts, lecture scripts, mind maps, teaching video storyboards, and Manim math animations, plus run quality checks and language normalization through 15 MCP tools.
    MIT
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    Provides AI assistants access to international education data from UNESCO UIS (4,000+ indicators) and OECD Education at a Glance via SDMX, with no API keys required.
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    MIT

Matching MCP Connectors

  • Returns the full document for an id obtained from `search`, as { id, title, text, url, metadata }: `text` is the readable content (Markdown) and `url` the canonical public page to cite. Companion of `search` in the OpenAI Deep Research contract, over the UNESCO UIS statistics (≈5,000 indicators: education — enrolment, completion, literacy, teachers, spending, SDG 4 —, science/R&D (SDG 9.5), culture (SDG 11.4) and demographic context) catalog. Only ids returned by `search` are valid; an unknown id returns an error. The `uis_*` tools remain the tools for data queries. Behavior: read-only and idempotent — a live GET against the public source when the document needs it.
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  • Get the full profile for one of the user's LinkedIn connections: work history, education, skills, and their About summary. Use this after search_connections when you need depth on a specific person. Identify them by name, or by linkedin_url for an exact match. A found:false response carries the user's imported-connection count: if no_imported_data is set, nothing was searched, so report the missing import rather than a missing person.
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  • The user's complete Remoet profile in one call: profile core (summary, links, location, avatar), workExperience, projects, and education (each section entry carries an id for update/delete) plus the current visibility setting. Always read this before changing anything. After reading, run a quick gap analysis and offer to fix what you find, ranked by impact (be encouraging, not critical): - SUMMARY (highest impact, the first thing companies read): flag if empty, under ~100 chars, or missing tech stack / years of experience / a differentiator. See update_profile for the pattern. - VISIBILITY: if NONE, suggest STARRED via update_profile so companies the user has starred can discover them. - PROJECTS (#1 gap for junior/mid devs): flag zero projects (ask about side projects, OSS, hackathons, scripts, blogs), or projects missing a URL, description, or technologies. - WORK HISTORY: flag missing descriptions, empty technologies arrays (they feed job matching), or descriptions without measurable impact ("Built React frontend" -> "...serving 50K users, 40% faster"). - BASICS: flag a missing avatar, a vague location (needs city + country for timezone/jurisdiction), or missing GitHub/LinkedIn. - EDUCATION: flag if empty (degrees, bootcamps, notable certifications). If the user shares a CV, resume, or website, populate the whole profile (update_profile, save_work_experience, save_project, save_education), then search companies by their stack, surface jobs with search_jobs, and suggest stars for the genuine fits.
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  • The user's complete Remoet profile in one call: profile core (summary, links, location, avatar), workExperience, projects, and education (each section entry carries an id for update/delete) plus the current visibility setting. Always read this before changing anything. After reading, run a quick gap analysis and offer to fix what you find, ranked by impact (be encouraging, not critical): - SUMMARY (highest impact, the first thing companies read): flag if empty, under ~100 chars, or missing tech stack / years of experience / a differentiator. See update_profile for the pattern. - VISIBILITY: if NONE, suggest STARRED via update_profile so companies the user has starred can discover them. - PROJECTS (#1 gap for junior/mid devs): flag zero projects (ask about side projects, OSS, hackathons, scripts, blogs), or projects missing a URL, description, or technologies. - WORK HISTORY: flag missing descriptions, empty technologies arrays (they feed job matching), or descriptions without measurable impact ("Built React frontend" -> "...serving 50K users, 40% faster"). - BASICS: flag a missing avatar, a vague location (needs city + country for timezone/jurisdiction), or missing GitHub/LinkedIn. - EDUCATION: flag if empty (degrees, bootcamps, notable certifications). If the user shares a CV, resume, or website, populate the whole profile (update_profile, save_work_experience, save_project, save_education), then search companies by their stack, surface jobs with search_jobs, and suggest stars for the genuine fits.
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  • Fetch affiliation records for an ORCID researcher. The `types` parameter controls which affiliation sections to return: employment, education, invited-positions, distinctions, memberships, qualifications, services, or all. Default is employment and education. Returns organization names, disambiguated organization identifiers (ROR/GRID/Ringgold), departments, roles, and date ranges. Affiliation data is self-reported; absence does not mean no affiliation.
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  • List OECD dataflow refs we have pre-vetted, grouped by topic (gdp, labour, prices, finance, households, health, demographics, projections, tax, education, environment, technology). Pass the flow_ref to fetch_dataset. For everything else use search_dataflows or browse https://data-explorer.oecd.org.
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  • List all 21 World Bank thematic topics (Economy & Growth, Health, Education, and others) with descriptions. Use it to browse the indicator space by theme or to find a topic_id for worldbank_search_indicators.
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  • Everything about an NTNU course except exam logistics: credits, level, campus, language of instruction, prerequisites, mandatory activities, course content / learning outcomes, credit reductions ('studiepoengreduksjon'), which study programs the teaching is planned for, contacts, and any alert notices (e.g. 'no longer taught'). English text by default; pass language 'nb' for Norwegian. Omit year for the current study year. For exam dates, times, aid codes, and rooms use get_exam_info.
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  • Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.
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  • Validate an invoice against EN 16931 and its national CIUS rule sets (XRechnung UBL/CII, Peppol BIS 3.0, Factur-X). Returns every failure as a "teaching error": the official rule id, the business term (BT-/BG-) it constrains, what the regulation actually requires, and a concrete fix. REQUIRES AN API KEY and costs 1 document against the monthly quota — call issue_api_key first if you do not have one. For explaining a rule id you already have, use explain_rule instead: it is free.
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  • Get the available services, prices, durations, and bookable staff or resources for a specific Korean beauty or wellness shop. Use this after finding a shop when service details, prices, durations, staff, or resources are needed before checking appointment availability. Pass lang to receive the content translated into the customer's language.
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  • Assemble one person's entire public footprint into a single sourced profile: identity, contact details, social accounts unified across platforms, work history, education, relatives, locations, and web mentions - every claim linked to the page it came from. Prefer this over reading search results yourself when you need the whole picture of one person rather than a single fact; it does the cross-platform correlation that a web search leaves to you. Pass a name plus the headline or username from search_people so the right individual is profiled. Repeat profiles are served from a shared cache: free and instant. Public sources only - never private accounts or breach data.
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  • Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.
    ConnectorAPI key
  • List the REAL, currently purchasable plans for a service that needs one — DATA bundles, CABLE packages, or EDUCATION products (WAEC/JAMB) — with their exact codes and current VTpass prices. ALWAYS call this before pay_bill for these three services and pass back one of the returned codes as variation_code. Never guess a plan, a code, or a price — if this returns nothing usable, say so rather than inventing one.
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