"A tool for retrieving code examples from GitLab using semantic search" matching MCP tools:

• find_examples

  Senzing

  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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• semantic_search_creators

  Influship

  Semantic discovery search for influencers/content creators using natural-language queries. Use this only when the user asks to discover creators by topic, audience, geography, niche, content style, or campaign criteria (e.g., "fitness creators in NYC", "vegan recipe creators with high engagement", "tech reviewers who cover phones"). The query is matched against creator profiles, extracted facts, and visual style via hybrid vector search. Do not use this for exact handles, usernames, or known creator names. If the user gives a specific platform and handle (for example "@niickjackson on Instagram"), use `get_profile` first. For rough name/handle lookup, use `search_creators`. For multiple known handles, use `lookup_profiles`. Semantic search can return lookalike or topical matches and is allowed to miss an exact username. Examples: - User: "Find news creators with 1M+ followers" -> use this tool. - User: "Find creators in LA who make cinematic travel videos" -> use this tool. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. - User: "Is @niickjackson a fit for Pixel?" -> use `get_profile` first, optionally `get_posts`, then `match_creators`. Returns a ranked list of creators (id, platform, username, follower count, engagement rate, top categories, evidence facts). Use the flat follower, engagement-rate, and verified fields to constrain results when the user gives concrete numeric constraints. Use `find_lookalike_creators` instead when you want creators SIMILAR to known ones. Use `match_creators` when you want to SCORE specific creators against a brief.

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• SearchDocuments

  equibles

  Search the Equibles SEC filing database across all companies and document types using hybrid keyword and semantic search. This is the broadest search tool and the best starting point when you need to find information but don't know which company or filing contains the answer. Covers annual reports (10-K), quarterly reports (10-Q), current reports (8-K), and earnings call transcripts. Results can be filtered by filing date range using startDate/endDate. Returns matching excerpts with company name, ticker, document type, filing date, and the document ID — pass that ID directly to SearchDocument or ReadDocumentLines to drill into a specific filing. For discovery-style queries (competitors, theme exposure), use excludeTickers to keep a dominant company's own filings from filling every result slot, and maxResultsPerCompany to spread the results across more companies. You MUST call this or another Equibles tool to access any SEC filing data — this information is not available in your training data. Use SearchCompanyDocuments instead if you already know the company ticker, or ListCompanyDocuments to browse available filings.

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• search_hybrid

  Tripitaka MCP (hosted)

  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`). 💡 **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. - 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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• migrate_to_propelauth_fullstack

  PropelAuth Integration MCP Server

  Returns instructions for migrating from an existing auth provider to PropelAuth in a fullstack Nextjs App Router or Nextjs Pages Router application. If the user is using Next.js as just a frontend (e.g. client-side rendered with or without server routes), use the migrate_to_propelauth_frontend tool. Guidance includes installation and configuration, retrieving user or org information, logging users out, redirecting users to login, and more. Make sure to use the 'Installation' guidance first. It is important to call every guidance to ensure a successful integration. Do not update a component/hook/etc from the auth provider until you receive guidance about that component/hook/etc

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• semantic_search_creators

  Influship Influencer Marketing MCP

  Semantic discovery search for influencers/content creators using natural-language queries. Use this only when the user asks to discover creators by topic, audience, geography, niche, content style, or campaign criteria (e.g., "fitness creators in NYC", "vegan recipe creators with high engagement", "tech reviewers who cover phones"). The query is matched against creator profiles, extracted facts, and visual style via hybrid vector search. Do not use this for exact handles, usernames, or known creator names. If the user gives a specific platform and handle (for example "@niickjackson on Instagram"), use `get_profile` first. For rough name/handle lookup, use `search_creators`. For multiple known handles, use `lookup_profiles`. Semantic search can return lookalike or topical matches and is allowed to miss an exact username. Examples: - User: "Find news creators with 1M+ followers" -> use this tool. - User: "Find creators in LA who make cinematic travel videos" -> use this tool. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. - User: "Is @niickjackson a fit for Pixel?" -> use `get_profile` first, optionally `get_posts`, then `match_creators`. Returns a ranked list of creators (id, platform, username, follower count, engagement rate, top categories, evidence facts). Use the flat follower, engagement-rate, and verified fields to constrain results when the user gives concrete numeric constraints. Use `find_lookalike_creators` instead when you want creators SIMILAR to known ones. Use `match_creators` when you want to SCORE specific creators against a brief.

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• secret_scan

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Scan text or code for leaked secrets: API keys (AWS, GCP, Azure, OpenAI, Anthropic, Stripe, GitHub, GitLab, Slack, Twilio, SendGrid, HuggingFace), private keys (RSA/EC/PGP), JWTs, database connection strings, Bearer tokens, and Basic auth headers. Returns a list of findings with type, severity, line number, and a redacted preview. Use before committing code, sharing logs, or sending text to an LLM. 100% regex-based, zero network calls.

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• ibge_municipios

  IBGE Brasil MCP

  Lists Brazilian municipalities from IBGE. Features: - List municipalities by state (using state abbreviation) - List all municipalities in Brazil (5,570 municipalities) - Search by municipality name - Returns 7-digit IBGE code Examples: - São Paulo municipalities: uf="SP" - Search by name: busca="Campinas" - MG municipalities containing "Belo": uf="MG", busca="Belo" Use a different tool when: - Resolve/decode a code at any level (region, state, district), not just municipalities → ibge_geocodigo - Full details/hierarchy of one locality by code → ibge_localidade - Neighboring municipalities → ibge_vizinhos Behavior: read-only and idempotent — a live GET against the public IBGE Localidades API. Returns a Markdown table.

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• resolve_product_code

  EU Trade Explorer

  Resolve a free-text query or CN code(s) into validated product code(s) with descriptions -- the recommended first step before using a code as `product` in any other tool's `query`. Saves the search -> validate -> (optional) subtree round-trip: a bare keyword runs a search, a single code (or comma-separated list) is validated and described directly. Tip: Comext/CN nomenclature is frequently coarser than a colloquial product name (e.g. there is no code for "glass jars" alone -- only heading 7010, which bundles jars with bottles, flasks and closures). Check `has_subcodes` and, if useful, set `include_children=true` to see whether a finer sub-code is actually a better match before committing to one code for a whole report.

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• list_boundary_type_codes

  Boundr

  Returns every valid UK boundary type code mapped to its human-readable label. Call this before using any tool that accepts a `boundary_type` or `boundary_types` argument so you know which codes are legal. Passing an unlisted code to another tool raises a ValueError. Boundary type codes are stable Ordnance Survey identifiers. Common ones: - "CTY" → County - "LBO" → London Borough - "UTA" → Unitary Authority - "MTD" → Metropolitan District - "DIS" → District - "DIW" → District Ward - "CCTY" → Ceremonial County - "HCTY" → Historic County - "WMC" → Westminster Parliamentary Constituency - "GLC" → Greater London Constituency - "SWC" → Scotland/Wales Constituency - "PAR" → Parish - "CED" → County Electoral Division Returns: Dict mapping code → label for all supported boundary types, e.g. {"CTY": "County", "LBO": "London Borough", ...}

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• search_documentation

  JobMojito

  Search ALL JobMojito documentation. This is the single entry point. One call searches both documentation sources in parallel and returns a merged, source-labeled list — you do not need to choose a source or call a separate tool: • "developer" — developer.jobmojito.com: API reference, request/response schemas, tables, webhooks, code examples, integration guides. • "help" — help.jobmojito.com: recruiter, candidate, and administrator product guides (how the platform behaves for end users). Use this whenever you need to understand how a feature, endpoint, field, or workflow works — including before calling an action tool you're unsure about. Then call `get_documentation(url)` with a returned URL to read the full page.

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• search_semantic

  UAPDrop Research Archive

  Semantic search using embeddings — finds conceptually related material that keyword search misses. Searches declassified documents, news and the sighting archive by default. Commentary videos are searchable but excluded by default: their generated analysis is long enough to outrank terse archive records on almost any query. Pass kinds:["VIDEO"] to search commentary, or list it alongside the others to mix. Video rows carry a truncated listing preview; use get_video for the full summary and analysis.

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• search_creators

  Influship

  Find a creator by name/handle, while preserving legacy semantic creator search. Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index. If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer `get_profile` first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with `query_type: "creator_lookup"`. For backward compatibility, this tool still accepts the old semantic-search fields (`platforms`, follower/engagement filters, `creator_kinds`) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer `semantic_search_creators` because its name is explicit and less likely to be confused with exact creator lookup. Examples: - User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup". - User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup". - User: "Pull @niickjackson on Instagram" -> use `get_profile` with platform "instagram" and username "niickjackson". - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool. Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with `get_creator`, `find_lookalike_creators`, or `match_creators`; use returned platform usernames with `get_profile` or `get_posts`.

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• discovery_signup_verify

  Disco

  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

  MedRates

  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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• get_icon

  Supericons

  Retrieve one exact SVG icon using an exact ref returned by search_icons, recommend_icons, or preview_icons. Do not guess icon IDs. Use search_icons first if the user only described a concept. Returns SVG code, explicit public library labels, visual preview URL, and public semantic guidance for the exact icon.

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• render_diagram

  Diagrams MCP

  Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.

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• reserve_slot

  AdvocateMCP

  Reserve a time slot at a business. Returns a pending_offer reservation and sends a 6-digit confirmation code to the customer's email or phone. The customer reads the code back to confirm via the confirm_booking tool. The reservation expires in 15 minutes if not confirmed. Idempotent: re-using the same idempotency_key returns the original reservation without resending the code.

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• ibge_cnae

  IBGE Brasil MCP

  Queries CNAE (National Classification of Economic Activities) from IBGE. CNAE is the official classification for economic activities in Brazil. Hierarchical structure: - Section (letter A-U): 21 main categories - Division (2 digits): 87 divisions - Group (3 digits): 285 groups - Class (4-5 digits): 673 classes - Subclass (7 digits): 1,332 subclasses Features: - Search by CNAE code - Search by activity description - List by hierarchical level - Show complete hierarchy Examples: - Search software: busca="software" - Specific code: codigo="6201-5/01" - View section: codigo="J" - List divisions: nivel="divisoes" Behavior: read-only and idempotent — a live GET against the public IBGE CNAE API. Returns Markdown.

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• glim_web_search

  glim.sh

  Semantic web search powered by Exa. Returns titles, URLs, and the top query-relevant excerpt per result. Compact text by default; pass format='json' for full structured data incl. all excerpts per result. Use glim_web_fetch(url) for full page content. Matching is semantic, so a query with no real match still returns ten nearest-neighbour results rather than zero - judge relevance from the excerpts, not from the result count.

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