"A tool to evaluate the suitability of semantic search queries" matching MCP tools:

• sti_searchA

  Stimulsoft Documentation MCP ServerA

  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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• sti_searchA

  docsA

  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_family_dayA

  tickadoo Experiences and EventsA

  Use this when the user wants family-suitable options for a day in one city. Returns a flat ranked list of experiences (search rows) with family-tagged rows promoted and out-of-season products dropped, not a structured morning/lunch/afternoon/evening plan. `date` selects which season counts as current; `kids_ages` and `budget` are accepted but NOT yet applied, so compose the day yourself and verify age suitability per pick via get_experience_details.

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• get_briefingA

  the drainA

  Before security-sensitive tooling or agent operations: find recent threads relevant to a task, with source links, provenance and matching keywords. Keyword overlap on title, tags and excerpt over the last 200 active threads; not semantic search and not the full archive (use search for that). Seeds excluded by default.

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• search_hybridA

  Tripitaka MCP (hosted)A

  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`). ⚠️ **Send the question and nothing else. Do not pad the query.** The whole string becomes one vector, so every word you add moves it. Appending your own candidate terms — synonyms, Pāli equivalents, a keyword list — searches for the blend, not for the question. Measured on `Buddha flies in the sky`: asking it plainly put the right passage at **rank 1** (3 relevant suttas). Appending three guessed Pāli terms (`buddha, agga, sagga`) pushed it down to **rank 5** and left only 1 — because `agga` (supreme) and `sagga` (heaven) drag the vector toward their own meanings. Have candidate terms worth searching? Give them their own `search_by_keyword` call and merge the two result lists. One tool asks what a passage means, the other asks where a word occurs; combined into a single string they cancel out. Rewriting the question to sound more canonical does not help either — the same query phrased as `rose into the air and flew like a bird` scored **zero** relevant hits. 💡 **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. - `language` only chooses what comes back; it does not change what matches or how results are ranked. - Looking for a concrete thing rather than a concept (an animal, an object, a place)? `search_by_keyword` over `language="english"` is often better. The translations are segment-aligned to the Pāli, so one search for `turtle` finds every turtle passage whatever the Pāli underneath says (`kacchapa`, `kumma`, `maṇḍūkakacchapa`), and each hit still carries its segment id. - 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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• job_statusA

  OwlCADA

  Report on a job handle you were given. A slow analysis on a SAVED project (evaluate, print_check, measure, check_interference) is finished in the background instead of failing, and answers with a `jobId`; call this with that id until status is "done", waiting `retryAfterMs` between polls. The `result` is exactly what the original tool would have returned. STEP jobs are polled with export_step, not here.

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• search_documentsA

  FrameOn — Agentic Project ManagementA

  Semantic search over this project Wiki pages and uploaded documents. It matches meaning, not keywords, so give it a question rather than a term. Returns between 1 and 10 excerpts (default 5), never whole pages — when an excerpt looks right, follow up with `get_document_full_content`. Always search before `create_wiki_page`: the point is to extend the team memory, not to duplicate it.

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• start_auto_clusteringA

  MencoroA

  Start a background job that proposes how to group up to 500 tracked queries into clusters. mode "fill_gaps" places only queries that have no cluster yet; "add_on_top" adds clusters without touching existing memberships; "full_regroup" proposes a fresh grouping of every query. restrictToExistingClusters only uses the project's current clusters. Nothing changes until apply_auto_clustering: poll get_job, show the proposal to the user, then apply it. Requires an active subscription.

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• delete_tracked_queriesA

  MencoroA

  Delete up to 100 tracked queries, together with their captured answers, matches and metrics history. Irreversible; to stop spending budget without losing history, pause them with pause_tracked_queries instead. Requires a confirmationToken: call preview_operation with tool "delete_tracked_queries" and these arguments first, show the returned plan to the user, and call this tool only after the user explicitly agrees.

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  Destructive

  OAuth

• save_queryA

  SaturnSQLA

  Save SQL to the query library, or overwrite one of your own saved queries by passing its id. Use it when the user wants to keep, reuse or schedule a query; pass the returned id to run_saved_query or create_schedule. Does not run the SQL, so test it with execute_query first. A new query is private to you until shared in the app. Passing an id replaces that query's name, SQL, connection and folder; you can only update queries you created. Needs the write scope, plus execute to change the SQL of a scheduled query.

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  Destructive

  API key

• z2h_search_marketplaceA

  Zero to Hundred marketplaceA

  Find live software by keyword before building from scratch. Public, no sign-in needed. Returns descriptions, USD license prices, tech stacks and listing links. Example: {"search": "invoice", "limit": 5}. Empty search browses recent software. When hasMore is true without nextCursor, narrow the search. Matching is lexical. Errors set isError; retry a rate limit later. Use z2h_compare_marketplace_listings for a shortlist or z2h_show_marketplace for cards. A result does not guarantee suitability or purchase availability.

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• get_keyword_metricsA

  RillowA

  Monthly search volume, difficulty (0-100), intent, cost per click, the top 10's average domain rating and linking sites, and the change in searches over a year for keywords you already have, up to 200 in one call, in the project's country. Use it for Search Console queries, the user's own ideas or keywords from a competitor's page, before you plan them. Rillow saves the numbers, so update_plan fills them in. Use find_keywords instead to discover new keywords around a seed. Uses 0.4 credits to 1.1 credits, by the number of keywords. Numbers are DataForSEO estimates. A keyword missing from the result has no search data.

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• docs_resolve_libraryA

  SCRIPTMASTERLABS — ScriptDocsA

  Turn a fuzzy or partial name into real, ranked candidate package names, using the registry's own live search index — not a guess at what the package is probably called. For npm this queries registry.npmjs.org's actual search API (the same one npmjs.com uses). For Cargo this queries crates.io's real search endpoint. For PyPI: there is currently no official PyPI search API (XML-RPC search was permanently disabled in 2022 and never replaced) — calling this with ecosystem 'pypi' returns an explicit message saying so rather than a fabricated or scraped result, along with a suggestion to use the exact package name with docs_get_package_info instead. Args: - ecosystem ('npm' | 'pypi' | 'cargo') - query (string): the name or description to resolve, e.g. "react" or "async http client" - max_results (number, 1-10, default 5) Returns JSON with: candidates (array of {name, version, description, score, url}), source_url, fetched_at. Error Handling: - ecosystem 'pypi' always returns an explanatory message, not an error and not fabricated results - Returns "Error: ..." only for actual npm/crates.io request failures

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• oh_code_searchA

  Ohio CodeA

  Search the Ohio Revised Code BY TOPIC or keyword rather than by section number -- "EDGE certification minority business", "county competitive bidding threshold", "OVI penalties", "landlord security deposit". Covers all chapters of the Revised Code. Returns ranked sections with citation, heading and an opening excerpt; call oh_revised_code with a result's section for the full text. Full-text match, not semantic search. A query with no hits returns found:false. Keyless.

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• get_dataset_statusA

  Algenta MCP ServerA

  Get the live training status and model tier of one dataset: whether semantic training is still running or the dataset is ready, and which model serves queries — model_tier 'none' = deterministic fallback only, 'base' = generic model, 'schema' = fully trained schema-specific model (best quality). Poll this after onboard_dataset until training completes, and use retrain_dataset after schema or alias changes. Read-only; an unknown dataset_id fails with not_found. Also returns name, column_count, source_names, and updated_at.

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• execute_sqlA

  Google Big QueryA

  Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `AI.KEY_DRIVERS`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.

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  Destructive

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• searchA

  SharebenchA

  Search this workspace's published artifacts (skills, agents, workflows, and knowledge documents in SKILL.md format). Returns ranked metadata — name, description, type, contributor, timestamps, bundledCount, slug, authorCredit, and industries — but NOT the full body. To read an artifact's content, call `get_by_id` with the returned `artifactId` (or `slug`), or read it as a resource at `artifact://<artifactId>`. Use this whenever the user wants to find, discover, browse, or filter existing artifacts before reading or contributing. Modes: `hybrid` (default; combines lexical and semantic ranking via reciprocal rank fusion — best for most queries), `bm25` (exact-keyword or name lookups), `semantic` (concept matching when the user's terms differ from artifact text). Pass `industries: ['marketing', 'legal']` to narrow results to artifacts tagged with ANY of those industries (keyword-array overlap). If hybrid silently degrades because the embedding service is unavailable, the response's `warnings` array will contain `embedding_degraded:hybrid-fell-back-to-bm25` — surface this to the user if precision matters.

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• search_trials_by_sponsorA

  xpay Academic Research CollectionC

  Search clinical trials by sponsor/organization. This tool allows you to search for clinical trials based on a list of sponsor organizations. Input: - `sponsors`: A list of strings, where each string is a sponsor organization to search for. The search will find trials sponsored by any of the specified organizations. Example: `['National Cancer Institute', 'Pfizer']` - `max_studies`: The maximum number of studies to return. Defaults to 50. - `fields`: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).

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• get_research_papersA

  TensorFeedA

  Get recent AI/ML research papers from one of three feeds, chosen with the source argument. arxiv_recent is the firehose: newest arXiv submissions in cs.AI / cs.LG / cs.CL / cs.CV by submission date, refreshed daily at 11:30 UTC. trending is citation-ranked from Semantic Scholar across five fan-out queries, deduped, refreshed daily at 11:00 UTC. hf_daily is Hugging Face editor-curated with community upvotes and discussion counts, refreshed daily at 14:15 UTC. Pick arxiv_recent for what is brand new, trending for what is influential, hf_daily for what practitioners are discussing. License: arXiv and Semantic Scholar permit metadata use; the standard attribution block ships on every response.

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• searchA

  OrbitA

  Find and evaluate public API endpoints and MCPs that match your query. Set `q` to a natural language query, keywords, an API name, or a question — results are matched by meaning and keyword; each result includes `id`, `resourceType` (`endpoint` or `mcp`), `name`, `description`, `method` (for an `endpoint`) or `transport` (for an `mcp`), `url`, and `evaluateGuide` — an evaluation of what the endpoint or MCP does, when to use it, and its limitations. Review `evaluateGuide` to pick the best fit, then pass each chosen result's `id` and `resourceType` (as `type`) to `integrate`. Paginate with `cursor` from `meta.nextCursor` (`limit` defaults to 10, max 25; pagination stops at 40 results total). No authentication required. Best practices for querying: - Use focused keyword queries that include the product or provider name along with the endpoint details, for example "PayPal create invoice". - Alternatively, use natural language queries such as "PayPal API to create an invoice". - Avoid jumbled queries that cram many unrelated keywords into a single query, for example "paypal invoice payment delivery payments ordering". - Avoid OR-separated queries such as "paypal invoice OR paypal create invoice OR paypal OR invoice creation". - If you need to explore multiple intents, try each as a separate call.

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• dataset.searchA

  FlexOrch MCPA

  Search across all indexed FlexOrch datasets by keyword or meaning. Use this to find specific documents or records without processing a new file. Requires at least one dataset to exist. Structured search works on all plans. Semantic and hybrid modes require a Pro plan — a clear upgrade message is returned if the plan is insufficient. mode='auto' picks structured on free plans, hybrid on Pro+. Args: query: Search query — natural language or keyword. Max 1000 characters. top_k: Number of results to return. Default: 5, max: 50. mode: Search strategy — auto (default), structured, semantic, hybrid. semantic and hybrid require Pro plan. document_type: Filter to a specific document type, e.g. invoice (optional). language: Filter by document language, ISO 639-1 code, e.g. en, de, tr (optional). quality_grade: Filter by quality grade: A, B, C, or D (optional).

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