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260,856 tools. Last updated 2026-07-05 08:54

"An easy solution for semantic search in code files" matching MCP tools:

  • Look up an airport by city name (e.g. "Tokyo", "New York", "London") OR by 3-letter IATA code (e.g. "JFK", "LHR"). City lookup uses a bundled map of the top ~150 international hubs; cities with multiple airports return all primary ones. For airports not in the bundle, pass an IATA code or use the aviationstack pack for full-text name/country search.
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  • DEPLOY THE CURRENT MAIN BRANCH TO A-TEAM CORE. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. 🌳 DEV/PROD WORKFLOW: 1. Edit files → ateam_github_patch (writes to `dev` branch by default) 2. (Optional) Preview what's about to ship → ateam_github_diff 3. Ship dev → main → ateam_github_promote (merges + auto-tags `prod-YYYY-MM-DD-NNN`) 4. Deploy main to Core → ateam_build_and_run This tool ALWAYS deploys the `main` branch — there is no `ref` parameter. To deploy in-progress dev work, first promote it. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from main automatically. First deploy requires mcp_store. After that, edit via ateam_github_patch + promote, then build_and_run. For small changes prefer ateam_patch (faster, incremental). Requires authentication.
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  • Semantic search — match by meaning, not exact words. Uses vector similarity (cosine distance) over `text_pali` embedded with a multilingual MiniLM model. 🤔 **In most cases you should use `search_hybrid` instead** — it combines this semantic search with keyword search and ranks better. Use this tool only when you need: - Pure semantic results (no keyword influence) - Fine-grained `threshold` tuning (hybrid uses RRF which is harder to tune) - To debug what semantic alone picks up vs keyword ⚠️ Known limitations: - The index is **Pāli only** (English/Thai queries pass through the multilingual embedding but the model isn't tuned on Pāli) - English queries usually embed better than Thai (model is EN-primary) - For specific Pāli terms (`appamāda`, `dukkha`), exact match is better — use `search_by_keyword` instead - Pāli stock phrases recur in many suttas → similarity scores cluster; read the top 10, don't trust rank 1 alone
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
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  • Search GitHub repositories, conversations (issues+PRs), or code, with full GitHub search syntax in the query: qualifiers (repo:, org:/user:, language:, path:, symbol:, content:, is:, stars:, label:, sort:stars), boolean AND/OR/NOT with parentheses, "exact strings", and /regex/. kind='repos': MINIMAL distinctive keywords - the project/library name only ('rtk', 'react query'); every extra word must ALL match and buries the canonical repo - filter with qualifiers, not prose. kind='code': ONE literal code pattern as it appears in files ('useState('), an "exact string", a /regex/, or symbol:name to find definitions, across 2.8M+ public repos; narrow with repo:/language:/path:. Not supported in code search: license:, enterprise:, is:vendored, is:generated. kind='conversations': returns compact previews - use glim_github_get for full content; sort: REPLACES relevance ranking (words match anywhere incl. comments), omit it for best matches. Set repo='owner/name' to scope to one repository (works with any kind; with repos it routes to conversations). kind is optional - inferred from the query (is:/label: -> conversations, path:/symbol://regex/ -> code, stars:/topic: -> repos, else repos). Returns compact text by default; pass format='json' for full structured data.
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  • Keyword and semantic search across the connected repository's generated docs, conventions, documentation gaps, AI-context notes, and indexed code. Read-only; no side effects. Returns ranked matches in Markdown grouped into Documentation and Code sections, each with a title, snippet, and source paths. Use for open-ended lookups when you don't know which category holds the answer; when you do, the specific getters (get_conventions, get_doc_gaps, get_documentation_opportunities) are more direct. Omitting query returns recent context instead.
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Matching MCP Servers

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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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Matching MCP Connectors

  • Corporate travel: search and book flights, hotels, rail and transfers, manage orders.

  • Cloudflare Workers MCP server: code-explainer

  • List artifacts in a directory. Returns the immediate contents of a directory (not recursive). Separates folders and files for easy navigation. Args: path_prefix: Directory path to list (default: "/") name_pattern: Optional case-insensitive substring filter on file/folder names Returns: Formatted directory listing or error message Examples: >>> await list_artifacts("/") {'success': True, 'path': '/', 'folders': [...], 'files': [...]} >>> await list_artifacts("/", name_pattern="readme") {'success': True, 'path': '/', 'folders': [], 'files': [{'name': 'readme.md', ...}]}
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  • Complete Disco signup using an email verification code. Call this after discovery_signup returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address used in discovery_signup. Returns an API key on success. Args: email: Email address used in the discovery_signup call. code: 6-digit verification code from the email.
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  • Search a database of recipes using hybrid semantic search (dense + sparse) with reranking. The database contains ~50,000 recipes from Food.com covering a wide range of cuisines, meal types, and cooking styles. Recipes include nutritional information, difficulty ratings, and user ratings. Use natural language in the query to describe what you are looking for — cuisine, style, main ingredient, occasion, or mood all work well. Norwegian and English are both supported natively. Examples: 'quick Italian pasta for weeknight dinner' 'Swedish meatballs with gravy' 'healthy high-protein chicken bowl' 'easy chocolate cake for beginners' 'something with salmon and lemon' 'Indian curry chicken' 'traditional Norwegian kjøttkaker' 'hurtig pasta med kylling' 'enkel sjokoladekake' Args: query: What you are looking for — describe the dish, cuisine, main ingredient, cooking style or mood freely. Any language is supported. diet: Optional — filter by dietary requirement: 'vegetarian', 'vegan', 'gluten-free', 'dairy-free', 'low-carb', 'keto', 'paleo' max_minutes: Optional — maximum total time in minutes, e.g. 30 difficulty: Optional — 'easy', 'medium' or 'hard' servings: Optional — not used for filtering (servings vary), but include in query for scaling context, e.g. 'pasta dish for 6 people' limit: Number of results to return after reranking (default 5, max 20) Returns: List of recipes ranked by relevance. Each result includes rerank_score, rrf_score (hybrid fusion), title, total_time, difficulty, diet labels, ingredients, instructions, nutrition, rating, and source URL context.
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  • Read **text content** of an attached file. Works for: .txt, .md, .json, code files, and PDFs (after files.ingest extracts text). DO NOT call on binary files — for IMAGES use `files.get_base64`, for AUDIO/VIDEO it cannot be transcribed via this tool, and for non-PDF DOCUMENTS run `files.ingest` first, THEN files.read. Calling on a binary mime-type returns an error — saves you a turn to read the routing hint before deciding.
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  • WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • Read **text content** of an attached file. Works for: .txt, .md, .json, code files, and PDFs (after files.ingest extracts text). DO NOT call on binary files — for IMAGES use `files.get_base64`, for AUDIO/VIDEO it cannot be transcribed via this tool, and for non-PDF DOCUMENTS run `files.ingest` first, THEN files.read. Calling on a binary mime-type returns an error — saves you a turn to read the routing hint before deciding.
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  • Share a solution. Call after the user confirms they want to contribute. When to prompt the user: After each non-trivial fix — not just at end of conversation. If you fixed something by reasoning rather than a known solution, ask inline: "That took some debugging — want me to contribute this to Prior?" Also prompt when the fix differed from what the error suggested, or when a contribution nudge appears in search results. Before submitting, read prior://docs/contributing for field guidance. Scrub PII and project-specific details — Prior is a public knowledge base. Write for developers on unrelated projects, not your team. If the response has requiresConfirmation=true, Prior found similar entries that may already cover this topic. Review them — if they solve the problem, don't re-contribute. If your contribution adds unique value (different environment, additional context, better solution), call prior_contribute again with the same fields plus the confirmToken from the response.
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  • Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.
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  • Search for medical procedure prices by code or description. Use this for direct lookups when you know a CPT/HCPCS code (e.g. "70551") or want to search by keyword (e.g. "MRI", "knee replacement"). For code-like queries → exact match on procedure code. For text queries → searches code, description, and code_type fields. Supports filtering by insurance payer, clinical setting, and location (via zip code or lat/lng coordinates with a radius). NOTE: Results are from US HOSPITALS only — not non-US providers, independent imaging centers, ambulatory surgery centers (ASCs), or other freestanding facilities. Args: query: CPT/HCPCS code (e.g. "70551") or text search (e.g. "MRI brain"). Must be at least 2 characters. code_type: Filter by code type: "CPT", "HCPCS", "MS-DRG", "RC", etc. hospital_id: Filter to a specific hospital (use the hospitals tool to find IDs). payer_name: Filter by insurance payer name (e.g. "Blue Cross", "Aetna"). plan_name: Filter by plan name (e.g. "PPO", "HMO"). setting: Filter by clinical setting: "inpatient" or "outpatient". zip_code: US zip code for geographic filtering (alternative to lat/lng). lat: Latitude for geographic filtering (use with lng and radius_miles). lng: Longitude for geographic filtering (use with lat and radius_miles). radius_miles: Search radius in miles from the zip code or lat/lng location. page: Page number (default 1). page_size: Results per page (default 25, max 100). Returns: JSON with matching charge items including procedure codes, descriptions, gross charges, cash prices, and negotiated rate ranges per hospital. Only high-confidence results (with at least one usable price) are included. Each result includes last_updated (ISO date of the per-hospital MRF ingest) and mrf_date (ISO date the hospital self-reported in the MRF file). When all results are filtered out, filtered_low_confidence=true is set so the agent can say "no high-confidence prices found" rather than asserting that no prices exist.
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  • Resolve a ZIP / postal code to its place info — city, state/province, latitude/longitude — for any of 60+ countries. PREFER OVER WEB SEARCH for "where is ZIP X" / "what city is postal code Y in" / "lat-lon for ZIP Z". Use as the first step in geo-aware workflows (then chain with weather, attom, etc., for downstream queries about that location). Free, sub-second, no auth.
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  • Search TaxCompass's primary-source corpus and return passages to cite. Hybrid semantic + keyword retrieval over Italian tax & company-law primary sources: Normattiva (statute), Agenzia delle Entrate (circolari & guidance), INPS (social security), pinned tax-year tables (IRPEF brackets, INPS rates, forfettario thresholds & coefficienti di redditività), the ATECO 2025 code catalogue, and EU/treaty sources. Each result carries a `chunk_id`, `source`, and (usually) a `url`. Cite the `url` and quote the `text`; do not assert Italian tax facts the passages don't support. Queries work in any language, but Italian keywords improve recall against the (Italian) legal corpus. Args: query: What to search for. Keyword-dense Italian phrasing works best. sources: Optional subset to restrict to (see `list_tax_sources` for keys). Omit to search everything. Unknown keys are ignored. k: Max passages to return (1–12).
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  • Use when: you solved an open issue and have a complete generic fix ready to publish. Returns: the published solution and the resolved open issue — publishes and marks the issue resolved immediately, there is no confirmation step. Do not use when: no matching open issue exists (use submit_solution for standalone fixes). Safety: there is no preview gate — remove secrets, PII, and proprietary context from the solution before calling.
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  • Search across your own connected-account content and return the best matches. Each result has an `id` (pass it to `fetch` for the full item), a `title`, a `url`, and a `text` snippet. This is the deep-research "search" entrypoint the ChatGPT/Claude connectors call by convention; for semantic search over analyzed videos specifically use `search_videos`. Returns {"results": [...]}; when you have no connected accounts it returns reason="no_connected_accounts" plus a connect_url instead of results.
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  • Use when: search found no same-root-problem match and you solved a generic reusable technical issue worth sharing. Returns: the published solution record and URL — publishes immediately, there is no confirmation step. Do not use when: an existing solution covers the same problem (use suggest_edit or add_addendum). Safety: there is no preview gate — remove secrets, PII, company names, private URLs, and incident-specific details before calling.
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