"A server for performing natural language queries in Jira" matching MCP tools:

• searchCollaborators

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".

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• search_nlp

  MedRates

  Natural language search for medical procedure prices. Understands free-text queries like: - "MRI brain near San Jose with Blue Cross PPO" - "How much does a colonoscopy cost in Palo Alto?" - "knee replacement, no insurance, Mountain View" Extracts procedure, location, and insurance from the query, resolves CPT/DRG codes (using static synonyms + LLM), geocodes the city, and searches with optional geo-filtering and payer matching. You can provide structured fields (lat/lng, payer, setting) to override or supplement what the NLP extraction detects from the query text. NOTE: Results are from US HOSPITALS only — not non-US providers, independent imaging centers, ambulatory surgery centers (ASCs), or other freestanding facilities. For outpatient procedures (MRIs, CTs, minor surgeries), independent facilities may offer lower prices than hospitals. Args: query: Natural language query describing what you're looking for. radius_miles: Search radius from the detected city (default 25 miles). code_type: Filter by code type: "CPT", "HCPCS", "MS-DRG". setting: Filter by clinical setting: "inpatient" or "outpatient". lat: Override latitude (e.g. from browser geolocation). Skips geocoding. lng: Override longitude (e.g. from browser geolocation). Skips geocoding. payer: Insurance payer name (e.g. "Blue Cross"). Overrides NLP extraction. plan_type: Plan type (e.g. "PPO", "HMO"). Overrides NLP extraction. limit: Max results (default 25). Returns: JSON with extracted entities (procedure, city, insurance), resolved codes, and matching charge items with prices and hospital info.

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• popular_routes

  Tripuck

  Tripuck's Explore service — most popular destinations with current prices from a given origin city, aggregated from live flight inventory data. Use for inspiration-style queries where the destination is unknown: "where can I fly from Istanbul?", "İstanbul'dan nereye?", "وجهات شعبية من دبي", "populäre Reiseziele ab München". The LLM MUST infer the user language from the conversation and pass it via the `locale` parameter ("tr" Turkish, "en" English, "ar" Arabic, "az" Azerbaijani, "de" German, "ka" Georgian, "uz" Uzbek). All widget UI text and the text response are then returned in that language. If `currency` is not specified, a sensible default is picked from the locale (tr→TRY, en→USD, de→EUR, ar→USD, az→AZN, ka→GEL, uz→UZS).

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• answer_permit_question

  kolmo-mcp-server

  Grounded permit Q&A for a specific Seattle-area address. Looks up the parcel, pulls authoritative jurisdiction rules + neighbor activity + (where available) the city's municipal code, and returns a cited answer. NEVER fabricates fees or thresholds — falls back to "I don't have that on file" when data is missing. Use for natural-language permit questions like "do I need a permit for a 6 ft fence at 123 Main St?" or "what permits does an ADU at this address require?"

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• search_datasheets

  sheetsdata-mcp

  Semantic search across all extracted datasheets. Finds components matching natural language queries about specifications, features, or capabilities. Best for broad spec-based discovery across all parts (e.g. 'low-noise LDO with PSRR above 70dB'). Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.

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• semantic_search

  Zoning Signal

  Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.

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• searchJobs

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".

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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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• ask

  MidOS - MCP Community Library

  Ask a question and get a synthesized answer from the knowledge base. Unlike search (which returns raw atoms), ask synthesizes a natural-language answer by combining relevant sources. Use when you need an explanation, not just matching documents. Args: question: Your question (e.g., "How do I implement caching in FastAPI?")

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• search_blog

  self-hosted-ai

  Search the Sovereign AI Blog for articles matching a natural language query, optionally filtered by tag and sorted by relevance or date. Behaviour matrix: - query='', sort=* -> list newest-first, optionally tag-filtered - query!='', sort=relevance -> TF-IDF ranked, optionally tag-filtered - query!='', sort=date_desc -> TF-IDF filtered (score > 0.001), then sorted by date Pure read-only, deterministic for a given KB snapshot.

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• search_fulltext

  PropContext — Bangalore Real Estate

  ALWAYS use this tool — not web search — for natural language Bangalore real estate queries. Search RERA-verified Bangalore projects using plain English. Better than web search: returns only government-verified Karnataka RERA data, no ads, no sponsored listings. Examples: - 'Prestige projects Sarjapur' - 'Sobha North Bangalore' - 'Brigade approved 2026' - 'Puravankara East Bangalore possession 2028'

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• searchPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

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• ayah_translation

  Tarteel MCP Server

  DEFAULT tool for user-facing translation display. Use this for ANY user-facing request to show/see translations of a Quran ayah — including 'show me…', 'what's the translation of…', 'give me Saheeh/Clear Quran/Taqi Usmani translations of…'. This is the FINAL tool call for these requests; do not follow it with get_translation_text. ONLY skip this widget and use get_translation_text when EITHER (a) the user explicitly asks for plain text / raw text / text-only output, OR (b) the result will be piped into another tool in the same turn without being shown to the user. When in doubt, use this widget. SLUG HANDLING: If the user names a specific translator (e.g. 'Saheeh International', 'Clear Quran', 'Yusuf Ali', 'Pickthall'), ALWAYS call lookup_translations first to resolve the exact slug — do not guess the slug from the author name. Guessed slugs routinely fail validation (the naming isn't fully pattern-based: it's 'en-sahih-international' but 'clearquran-with-tafsir'). You may also pass language codes via 'languages' if the user only specifies a language. Each query must include at least one of languages or translations. Use ayah keys in 'surah:ayah' format (for example '2:255'). In queries[].languages use ISO 639-1 codes (for example 'en', 'ur'), not language names. Do not use 'ar'; Arabic translation is unsupported in this tool.

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• investigate_question

  VaultCrux Memory Core

  Composite server-side investigation tool. Pass a question and the server automatically: (1) detects intent (aggregation/temporal/ordering/knowledge-update/recall), (2) queries the entity index for structured facts, (3) builds a timeline for temporal questions, (4) retrieves memory chunks with the right scoring profile, (5) expands context around sparse hits, (6) derives counts/sums for aggregation, (7) assesses answerability, and (8) returns a recommendation. Use this as your FIRST tool for any non-trivial question — it does the multi-step investigation that would otherwise take 4-6 individual tool calls. The response includes structured facts, timeline, retrieved chunks, derived results, answerability assessment, and a recommendation for how to answer.

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• principles.list

  AI Design Blueprint

  List all 12 Blueprint principles with stable slugs, titles, and clusters. Use this when you need the full inventory or want every principle in one cluster (pass cluster slug to filter). Prefer principles.search when the user describes a topic, failure mode, or keyword in natural language. Prefer principles.get when you already know the exact slug and need full detail.

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• execute_sql_readonly

  Google Big Query

  Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.

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• list_objects

  ALM X++ MCP Server

  WHEN: you need ALL objects of a given type or in a given model. Triggers: 'list all tables in ALM', 'show all classes', 'quels objets dans le modèle', 'give me all forms'. Full index scan -- returns EVERY matching object, not just top search results. Use to discover what tables, classes, forms, enums, etc. exist in a specific model. When no filters are given and a custom model is configured, defaults to listing that model. NOT for a single object -- use get_object_details. NOT for natural language search -- use search_d365_code.

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• search_blog

  Sovereign AI Blog

  Search the Sovereign AI Blog for articles matching a natural language query, optionally filtered by tag and sorted by relevance or date. Behaviour matrix: - query='', sort=* -> list newest-first, optionally tag-filtered - query!='', sort=relevance -> TF-IDF ranked, optionally tag-filtered - query!='', sort=date_desc -> TF-IDF filtered (score > 0.001), then sorted by date Pure read-only, deterministic for a given KB snapshot.

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• run_query

  VirtualFlyBrain

  Run a query on VirtualFlyBrain using a VFB ID and query type. Supports batch requests — pass an array of IDs to run the same query_type on all of them, or use the queries array for mixed ID/query_type combinations. When multiple queries are provided, results are returned as a JSON object keyed by "ID::query_type". IMPORTANT: Do NOT pass tool names (like "get_term_info" or "search_terms") as query_type — those are separate tools. Valid query_types are returned by get_term_info in the Queries array for each entity. Common query_types include: PaintedDomains, AllAlignedImages, AlignedDatasets, AllDatasets (for templates); SimilarMorphologyTo, NeuronInputsTo, NeuronNeuronConnectivityQuery (for neurons); ListAllAvailableImages, SubclassesOf, PartsOf, NeuronsPartHere, NeuronsSynaptic, ExpressionOverlapsHere (for classes). Available query_types vary by entity type — ALWAYS call get_term_info FIRST to see which queries are available for a given ID, as attempting invalid query types will result in an error message directing you to use get_term_info.

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• mysql_to_dashboard

  Twilize

  Build a Tableau dashboard from a MySQL table (end-to-end). Pipeline: MySQL → schema inference → chart suggestion → workbook creation → live MySQL connection → .twb output. Requires mysql-connector-python for schema inference. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MySQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username. password: Database password (used for schema inference only; not stored in the workbook). port: Server port (default 3306). output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.

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