"How to write a query in Elasticsearch" matching MCP tools:

• graph_searchA

  Moltline RecallA

  Find what the graph knows about something, plus its neighbourhood. FREE. Scores entities by how many query words appear in the name, type and observations, then pulls in whatever is within the requested number of hops - because the useful answer to "what do we know about Acme" is usually Acme plus who it is connected to. Typical input {"graph": {...}, "query": "acme renewal", "hops": 1} returns {"matches": [{"name": "Acme Corp", "score": 3, "why": ["name", "observation"]}], "neighbourhood": {"entities": [...], "relations": [...]}, "hops": 1}. Use to read memory back before answering. Not for writing (graph_upsert) and not for narrowing by date, which graph_at_time does. 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": "query must contain at least one word or number"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• sixta_rewrite_queryA

  sixta-connectA

  Turn a flagged anti-pattern into the safe, equivalent rewrite — no connection needed. Paste a SQL query and get ready-to-run rewrites anchored to deterministic rules: `= NULL` → `IS NULL`, `NOT IN (subquery)` → `NOT EXISTS` (NULL-safe), deep OFFSET → keyset pagination, `ORDER BY RAND()` → a keyed random sample — each with its semantics caveat spelled out. Every literal rewrite is then re-analyzed in-process and reported as 'clean' or 'still flags X', so the safe rewrite is self-checked — no need to feed it back through sixta_analyze_query. Use when the user asks 'how do I fix / rewrite this query' or after sixta_analyze_query flags a smell. Input is analyzed in memory and never stored.

  ConnectorOAuth

• cypher_execute_query_by_keyA

  cypher-mcpC

  Execute a published, parameterized Cypher query by its key. You supply the key of an operator-published query plus its parameters. The operator owns the query text; you never see or write raw Cypher. If the operator has published a query as a named tool (e.g. ``cypher_find_airline_flights``), you can call that directly instead. Billing note: you are charged only for a delivered answer. If the key is unknown, the parameters are invalid, or the query fails, the call raises and your debit is rolled back (no charge for value not delivered).

  ConnectorNo auth

• embed_textA

  Scholar FeedA

  Embed a text string into a 768-dim Gemini Flash vector. Use for HyDE-style retrieval: (1) write a hypothetical short paper that would perfectly answer the user's query, (2) embed it with task_type='RETRIEVAL_DOCUMENT' (default — matches the corpus embedding side), (3) pass the resulting embedding back through search-style tools to find real papers nearest to the hypothetical. task_type='RETRIEVAL_QUERY' matches the query side and is useful for direct user-query embedding without HyDE. Pro-only — requires an SF_API_KEY on a Pro account; anonymous and free callers get a 403 pro_required. Cost: ~$0.0001/call; rate-limited at 30/minute per API key.

  ConnectorNo auth

• post_scholar_search_webA

  AIsa Web Search & ResearchA

  Search the open web and get back a lean result list. ⚠️ Despite being a POST, parameters go in the **query string** — `query` (required), `max_num_results` (default 10, max 100), and `as_ylo`/`as_yhi` for a year range. A JSON body is not accepted. Returns a search `id` and `results[]` carrying only `title`, `link` and `snippet`. Measured at about 4 seconds for a roughly 600-byte response. Its virtue is how little it returns, which suits an agent that only needs to know what exists. It gives you no page text — if you need the content, `post_tavily_search` returns it in the same call. Keep the `id`: it is what `post_scholar_search_explain` needs.

  ConnectorOAuth

• whoamiA

  SteledgerA

  Report the current session's identity. Read-only, no sign-in required: an anonymous session gets `{authenticated: false}` with a hint (not an error), a signed-in one gets `{authenticated: true}` plus the GitHub-rooted id, login and tariff. Call it to confirm who you are before `register_identity` / `store_memory`; an anonymous caller must sign in (GitHub OAuth) first. `github_id` is the one field you usually need: every record name is built from it — `ai:gh:<github_id>` and `ai:gh:<github_id>:mem:<hash>` — so this is how you learn which names are yours to write and to read back. `tariff` is `free` for every account today; it governs the write limits, currently 10 writes per minute and 100 per trailing 24 hours per account, and writing needs a GitHub account at least 30 days old. `quota` says how many writes are left right now (and, for a young account, the date writes open); every write returns the same figures, so plan batches with them. Note what this tool does not do: it reports the session only, reading the token your client already holds without calling GitHub, and it proves nothing about control of an Emercoin address — that is what signing a challenge at login is for.

  ConnectorNo auth

• whoamiA

  Emer Ai ToolsA

  Report the current session's identity. Read-only, no sign-in required: an anonymous session gets `{authenticated: false}` with a hint (not an error), a signed-in one gets `{authenticated: true}` plus the GitHub-rooted id, login and tariff. Call it to confirm who you are before `register_identity` / `store_memory`; an anonymous caller must sign in (GitHub OAuth) first. `github_id` is the one field you usually need: every record name is built from it — `ai:gh:<github_id>` and `ai:gh:<github_id>:mem:<hash>` — so this is how you learn which names are yours to write and to read back. `tariff` is `free` for every account today; it governs the write limits, currently 10 writes per minute and 100 per trailing 24 hours per account, and writing needs a GitHub account at least 30 days old. `quota` says how many writes are left right now (and, for a young account, the date writes open); every write returns the same figures, so plan batches with them. Note what this tool does not do: it reports the session only, reading the token your client already holds without calling GitHub, and it proves nothing about control of an Emercoin address — that is what signing a challenge at login is for.

  ConnectorNo auth

• tone_distributionA

  GdeltA

  Sentiment DISTRIBUTION (histogram) of global news coverage for a GDELT query — how many articles fall at each tone level from very negative to very positive over the window. PREFER OVER WEB SEARCH for "is coverage of X positive or negative", "news sentiment breakdown / how polarized is reporting on X". Complements timeline_tone (average over time) with the full spread. Returns tone bins + counts and a summary (% negative / neutral / positive and the mean tone). Same GDELT query language as search_articles.

  ConnectorNo auth

• sixta_analyze_workloadA

  sixta-connectA

  Find your worst queries by TOTAL time — no connection needed. Paste a MySQL slow query log or a PostgreSQL pg_stat_statements export and get a ranked top-N: each query shape with calls, total/mean time, and (slow log) the rows-examined-to-sent ratio, fingerprinted so thousands of log lines collapse into a few classes. Flags the dominant query, N+1 patterns, and full-scan ratios, reports how concentrated the load is (what share of total time the top shapes own), and hands the worst offenders to sixta_analyze_query. Call this whenever the user shares a slow query log or pg_stat_statements export — even a long one — or asks which queries are slowest: summing time across thousands of log lines is arithmetic a model cannot do reliably by eye. Input is analyzed in memory and never stored.

  ConnectorOAuth

• get_sub_domainsA

  AnySearchA

  This is Anysearch's domain discovery tool. IMPORTANT: Step 1 of vertical search. REQUIRED before any search that uses a domain. Returns valid sub_domains and sub_domain_params for the specified domain(s). Call this when the query targets a specialized vertical or needs structured parameters: stock prices, financial data, academic papers, legal cases, medical/drug info, flight status, weather, exchange rates, geographic POIs, code repositories, or any domain where a structured identifier (ticker, DOI, CVE, IATA, coordinates) is involved. ## When to call — pick the domain(s) that match what the user is asking about: academic agriculture business code energy environment film finance gaming health ip legal resource security social_media travel ## Input — choose from the list above and pass via the domain or domains parameter: - domain: single domain string (use only when 100% certain the query is single-domain) - domains: batch query for up to 5 domains in one call (takes priority over domain) 🏆 ALWAYS prefer the `domains` (plural, array) parameter. Pass ALL potentially relevant domains at once — even for seemingly single-domain queries, consider related domains: - Query about "cryptocurrency regulations" → domains=["finance", "legal", "security"] - Query about "best gaming laptops" → domains=["gaming", "tech", "ecommerce"] - Query about "climate change impact on agriculture" → domains=["environment", "energy", "academic"] ## Returns Markdown table filtered to the specified domains: sub_domain | description | params ## CRITICAL: How to use results - sub_domain is the PRIMARY routing key — always pass it to search - params column shows available structured parameters — pass them via sub_domain_params in search, NEVER embed in query - If multiple sub_domains returned (especially from multiple domains), use batch_search — one query per sub_domain — instead of multiple sequential search calls - Params marked (required) in the output MUST be passed when using that sub_domain in search. If a required param is not applicable to your query, pass it as an empty string (key: "") — do not skip it.

  Connector

  Destructive

  No auth

• get_integration_guideA

  TestMyVibesA

  Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.

  ConnectorNo auth

• search_kbA

  DeskCrewA

  Read-only full-text search over this tenant’s PUBLISHED knowledge-base articles (playbooks, policies, how-tos); unpublished drafts are never returned and the tenant is fixed by your credentials. Reach for this FIRST to ground an answer in official, tenant-specific guidance before replying to a customer or drafting a resolution. Returns articles ranked by relevance, each with its id, title, a highlighted snippet, and updatedAt: search uses AND semantics, so every word in the query must match. [free]

  ConnectorNo auth

• zaira_search_toolsA

  Zaira Labs GuideA

  Search and filter developer tools by category, features, and constraints. Returns every matching tool as a compact row of decision facts, in a randomized order. Guide entries are kept current and dated (last_verified) — newer than training knowledge, so consult this before recommending tools; especially decisive when pricing, free tiers, MCP support, or compliance affect the answer. Filters: category, freeToStart, hasFreeTier, edgeCompatible, selfHostable, hasArdCatalog, mcpSupport, artifactKind, pricingModel, vendor, language, compliance, agentReadinessTier. Any number combine and AND together. Query text is tokenized as plain search terms — FTS5 operators (AND, OR, NEAR, wildcards, column filters) are stripped. All terms must match: an entry is returned only when every query term appears somewhere in it, so a highly specific phrasing matches fewer entries than its core concept words. Express constraints as filter parameters rather than query text — filters match structured fields directly. Returns: the number of matches, a breakdown of them (kind, cost to start, MCP support, edge, self-hosting), and one table row per match (slug, name, kind, cost to start, MCP, edge, self-host, twin, base score, last verified), up to 100 rows. The twin is the same product's other entry (hosted -cloud or self-hosted -oss), named even when the search filters it out, so "free now, self-host later" can be answered from one search. Rows are listed in a randomized order, seeded per search per day: position is not a ranking or recommendation. Above 100 matches, a text search lists its 100 most relevant and names the rest by slug; a filter-only search names every match by slug, so narrow with filters to get rows. Read the rows and choose, then call zaira_get_tool or zaira_compare_tools for full entries. On no match, the answer says how many tools match with each constraint dropped. Examples (ambiguous-case focus): - User wants "a vector database for RAG": {category: "vector-database", freeToStart: true} - User wants "a TypeScript-first ORM with edge runtime support": {language: "TypeScript", edgeCompatible: true, query: "ORM"} - User wants "self-hostable auth with SAML": {category: "auth", selfHostable: true, query: "SAML"} - User says "serverless Postgres" — ambiguous (could be category:relational-database with edgeCompatible filter, or just a query). Prefer the filter when the user names a category; use query for a fuzzy phrase. - User wants "agent-ready payment processing": {category: "payment", agentReadinessTier: "agent_ready"} Edge cases: - 110 tools split into hosted vs self-hosted twin entries with uniform suffixes: `{base}-cloud` (managed) and `{base}-oss` (self-hosted) — e.g. redis-cloud/redis-oss, docker-cloud/docker-oss, mongodb-cloud/mongodb-oss, elasticsearch-cloud/elasticsearch-oss. Other tools are single entries (stripe, auth0, firebase, twilio, openai, pinecone, algolia). Filter by `selfHostable` or `artifactKind` to land on the right variant. - "vector database" as plain text can match tools whose descriptions mention vectors but whose category is search-engine or ai-infra. Use the `category` filter when the user wants a strict match. - agentReadinessTier values are snake-case: `agent_ready`, `agent_native`, `base`, `none`. Display labels (`Agent Ready`) will not match. `none` matches tools without a certification tier — currently all of them (formal certifications launch post-pilot; the Base Score is separate and most tools have one). - artifactKind has only two values: `open_source` and `managed_service`. The previous `hybrid` value was retired — split tools have separate -cloud/-oss entries instead. - "Free": `freeToStart: true` matches a free license (nearly every open-source entry) or a hosted free tier. `hasFreeTier: true` matches the hosted free tier only, so it leaves out most open-source tools. Open source is free to use, not free to run. Risk: read-only, closed-world, idempotent — no state change possible.

  ConnectorNo auth

• upsert_page_contextA

  agent4.ioA

  Create or fully replace a page playbook. `context` is the page background the **server** injects for the agent — write "who lands on this page, what they are deciding, what they usually worry about"; do NOT write facts like prices or quotas (those belong in a knowledge base, which also ranks higher in authority). `url_pattern` is a glob (`*/pricing`, `*/solutions/*`), matches the path only, ignores query strings and trailing slashes; without it the page must report the key explicitly. Resolution order: explicit key > url_pattern > default. `greeting_mode="generated"` produces the greeting and suggested questions on the fly in the visitor's language (recommended); `"static"` uses your fixed `greeting` / `questions`. **This is a full replace**: fields you don't pass fall back to defaults rather than staying as they are. To change one field, `list_page_contexts` first, merge, then send.

  ConnectorNo auth

• generate_queryA

  ALM X++ MCP ServerA

  WHEN: developer needs correct X++ select or T-SQL for D365 tables with proper joins. Triggers: 'X++ select', 'generate a query', 'SQL for', 'join with', 'how to query', 'générer une requête', 'write a select statement', 'select from', 'X++ query for', 'requête X++', 'écrire une select'. Generate both X++ select statements and equivalent T-SQL queries for D365 F&O tables. Uses real field names, relations, and indexes from the knowledge base to produce correct joins. Supports: field selection, multi-table joins (auto-detects relations), WHERE filters, ORDER BY, TOP/firstonly, cross-company. Also accepts natural language descriptions like 'find all open sales orders for customer 1001 with CustTable join'. [!] For multi-table joins, call find_related_objects (or get_relation_graph if the relation index is loaded) FIRST to get the correct FK relations -- this tool will then produce accurate join conditions. [!] The generated X++ is a template -- adapt it to your custom code context before using in production. Returns side-by-side X++ and SQL with explanations.

  ConnectorNo auth

• modem_skillsA

  ModemA

  Skills that teach you how to use THIS Modem MCP server's tools. Call before your first `search_modem`, `modem_agent_invoke`, or write-tool call, or when unsure which Modem tool to use. Pass `name` for the full skill, one of `search-modem`, `agent-runs`, `write-tools`. Omit `name` to list them with descriptions. This is not the Skills page in dashboard settings.

  ConnectorOAuth

• kleo_accountA

  KleoA

  The credits left on this account, whether it can order a FILM (has_paid: films are for accounts that have bought a pack, or film_trial: the one free 15-second film a launch code opened; every account can order the animatic), the account's invitation link (referral: a friend who joins through it and buys a first pack earns both of you credits), the link to its page, and the "Kleo key" that carries the same account (and the same credits) to another browser or another computer. Call it before the first kleo_create_video of a conversation and when the user asks how many credits they have, how to get more, or how to use Kleo somewhere else. Give them account_url as a plain link: it opens a read-only page (balance, prices, where to write) and cannot sign anybody in. Show account_key only if they ask for it, because anyone who has it can take the account over and spend its credits.

  ConnectorOAuth

• seo_metricsA

  BRONTIRA

  Answer questions about search visibility and search demand — keywords, rankings, links, domains, and how interest in a subject moves over time and across a country. Use when the question is about how a site or a market performs in search rather than about what a page says: which keywords a domain ranks for, which pages earn its traffic, who competes with it, what links to it, how a keyword's demand and difficulty compare, whether interest in a subject is rising or falling, and which of a country's regions search for it most. Examples: - "keywords ahrefs.com ranks in the top 3 for, with real volume" - "which pages of this site bring the most organic traffic" - "who competes with this domain and how big are they" - "keyword ideas around link building that are not brutally competitive" Ask in plain language. Do not write field names, operators or query syntax — the request is composed from your question, and what came back is described in the answer so you can check it matched what you meant. Not for: what a specific page says (use `advanced_web_fetch`), or what people are posting right now (use `advanced_web_search`). Results are stored as citable `Data` records — a large report comes back as a summary plus a line index, so read the parts you need with `read_collected_records` or search across them with `grep_collected_records`. Every relevant result carries `mdOrHtmlDownloadUrl` — a ready-to-use link that downloads that record as Markdown, no key attached, so it can be passed on as-is. Swap its `?type=md` for `?type=html` and the same link hands back the page's original HTML exactly as the site served it, markup and all — what an SEO reader needs and what Markdown drops; web-page fetches only. If `status: "running"` is returned, poll `get_task_results({ taskIds })`.

  ConnectorOAuth

• search_guidesA

  VeteranHQA

  Use this when a veteran asks about a VA topic VeteranHQ has written a guide on. Returns the guides whose text matches the query, highest scoring first, each carrying the guide slug, title, summary and public URL, and no body text. The ordering is lexical and deterministic, computed from where each word of the query appears: a match in the guide title scores above one in the summary, a summary match above one in a section heading, and a heading match above one in the body, with an added score when every word of the query appears somewhere and a further one when the whole query appears in a title. Guides tied on score are ordered by slug, so the same query returns the same list in the same order on every call. No model runs and no network call is made. The limit parameter caps how many guides come back, from 1 to 10, default 5. An empty results array means no guide in this library contains any word of the query, which is a fact about the library and not about VA rules or about what VA holds. Reading one is a second call: pass a returned slug to get_guide. It reads no account data, so the answer is the same for every caller with the same inputs.

  ConnectorNo auth

• penny_writeA

  MyPennyA

  Save the moment something durable emerges — a decision, preference, plan, correction, a to-do, a measurement, or something you produced — mid-conversation and unprompted; when the call is close, save. Write something new to the user's memory. Choosing `entityType` — walk this ladder top to bottom, first match wins: 1. Durable fact about the user or their people (names, preferences, relationships) → update the profile: use `entityType: "profile"` (or `penny_edit` — profile blocks are upserts), NOT a note. Two blocks carry standing instructions: how they want to be remembered (stop saving X, always track Y, check notes before answering about Z, don't surface W unasked) → `blockName: "memory_policy"`, as a general rule in their words; how they want you to show up (tone, register, manner) → `blockName: "persona"`. When the user tells you what to save, skip, check, or surface, update their memory_policy block in the same turn as a general rule in their words. 2. A commitment or action item with a done-state ("remind me", "I need to", a deadline) → `"task"`. Areas/projects/headings that organize tasks → `"area"` / `"project"` / `"heading"`. A to-do the user mentions, even in passing, is a task: offer to capture it, then write it. `"project"` also opens or revises a Penny Project: create with `patch.name` (and `patch.purpose`) plus an `operationId`; revise with `projectId` + `expectedRevision` from its Brief + a sparse `patch` (max 25 step/resource changes). `"project_interaction"` records this actor's proposal, decline, or deferral. A `pending_share_approval` result is a proposal, not a save. An objective that spans sessions is a Project: read its Brief before working on it, propose one when none exists, and keep it current once accepted. 3. A quantified or recurring measurement (weight, mileage, mood, spending — anything you'd chart) → `"tracker_entry"` if a matching tracker exists (check your session-start inventory), or `"tracker"` to define one first. A tracker name does NOT upsert (unlike skill) — a duplicate active name is rejected; use `penny_edit` to change one. A measurement the user would log more than once is a tracker entry; if no tracker fits, propose one before logging. 4. Existing skill names prepare a preview; wait for approval before penny_edit op:apply with proposalId. Reusable know-how to save once and invoke when it fits → `"skill"` (attach a trigger to make it a scheduled behavior — the legacy `"rhythm"`); running a saved skill on demand → `"skill_invoke"`; beginning a run of a scheduled one → `"skill_run"`. Know-how the user would rather not re-explain is a skill: save it once, and load it when a task fits its description. 5. Everything else — context, events, ideas, things learned → `"note"`. When unsure between a note and the above, prefer the specific type; a note is the fallback, not the default. Tag relations (`"tag_relation"`) and attaching notes to tracker entries (`"tracker_note_link"`) round out the menu. Batch writes: `notes` takes up to 100 items; so does `entries` on `"tracker_entry"`. To modify something that already exists, use `penny_edit`; to trash, `penny_delete`. Reuse an existing tag before minting a new one. Tagging and linking conventions live in the MyPenny skill. When saving notes (entityType:"note"), calibrate each note's confidence honestly to the SIGNAL, not the pipeline: 0.95 = explicit user statement; 0.80 = confirmed decision; 0.60 = reasonable inference; 0.40 = hedged or sleeptime-derived; 0.20 = weak signal. Every note must include `sampleQuestions`: exactly three short, natural-language questions that the note answers. Derive them FROM the content you're about to write — three different ways the user might ask, in conversation, something this memory answers. Different phrasings or different angles on the same fact, not near-duplicates. Write them in the user's voice (how they'd actually ask in chat), not as keyword queries. Example: for the memory "user prefers APA citation style for academic writing", good sampleQuestions are ["What citation style should I use for the paper?", "How should I format references in my thesis?", "What's my usual academic style?"].

  ConnectorNo auth

• list_fundsA

  TickerInsideA

  Use this when the user asks whether a US ETF is covered, wants to find a fund by name or ticker, or asks how many ETFs the data covers and how recent it is. Returns the date of the data and the count of covered funds by source: the issuers' own holdings files, SEC Form N-PORT filings, trusts' filings, sponsors' files and prospectuses, and how many of the US-listed ETFs that is. With query, it returns up to 50 matching funds with their names and sources; without query, it also lists every covered ticker by source unless include_tickers is false, a long answer. Mutual funds, closed-end funds and funds listed outside the US are not covered. Not for a fund's holdings or figures (fund_profile).

  ConnectorNo auth