{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-meilisearch","slug":"meilisearch","name":"Meilisearch","type":"mcp","url":"https://github.com/meilisearch/meilisearch-mcp","page_url":"https://unfragile.ai/meilisearch","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-meilisearch__cap_0","uri":"capability://search.retrieval.full.text.search.with.typo.tolerance.and.ranking","name":"full-text search with typo tolerance and ranking","description":"Executes full-text search queries against indexed documents using BM25-based ranking with built-in typo tolerance (Levenshtein distance). The MCP server translates natural language search requests into Meilisearch API calls, handling query parsing, filter application, and result ranking without requiring users to understand Meilisearch's query syntax directly.","intents":["Search a document collection for relevant results despite user typos","Rank search results by relevance using BM25 scoring","Apply filters to search results based on document metadata","Get paginated search results with configurable result limits"],"best_for":["AI agents building search-augmented applications","Teams integrating semantic + full-text hybrid search into LLM workflows","Developers needing typo-tolerant search without implementing fuzzy matching"],"limitations":["Typo tolerance is configurable but adds latency — default 1 typo per word up to 5 characters","BM25 ranking is language-agnostic but not optimized for all languages equally","Search performance degrades with very large result sets (100k+ documents) without proper indexing strategy"],"requires":["Meilisearch instance running (v1.0+)","Documents pre-indexed in Meilisearch with searchable fields defined","API key or host URL for Meilisearch server"],"input_types":["text query string","filter expressions (JSON-like syntax)","pagination parameters (offset, limit)"],"output_types":["structured search results with document objects","relevance scores per result","total hit count and pagination metadata"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_1","uri":"capability://search.retrieval.semantic.vector.search.with.embedding.integration","name":"semantic vector search with embedding integration","description":"Performs semantic similarity search by converting queries to embeddings and matching against pre-indexed document vectors using cosine similarity or other distance metrics. The MCP server accepts natural language queries, optionally calls an embedding model (OpenAI, Ollama, or local), and returns semantically similar documents ranked by vector distance without requiring users to manage embedding pipelines.","intents":["Find documents semantically similar to a natural language query","Search across documents using meaning rather than keyword matching","Combine semantic search with full-text filters for hybrid retrieval","Use LLM-generated embeddings to search without explicit embedding model calls"],"best_for":["RAG systems needing semantic document retrieval","AI agents performing knowledge base lookups with natural language","Teams building semantic search without managing vector database infrastructure"],"limitations":["Embedding quality depends on the embedding model used — no built-in model selection optimization","Vector search latency increases with index size; Meilisearch is optimized for <10M vectors per index","Requires pre-computed embeddings for all documents — no on-the-fly embedding generation during indexing","No built-in reranking — results are ordered purely by vector distance, not by relevance to query intent"],"requires":["Meilisearch instance with vector search enabled (v1.3+)","Documents indexed with embedding vectors in a designated field","Embedding model API key (OpenAI, Anthropic, Ollama) or local embedding service","Vector dimension matching between query embeddings and indexed vectors"],"input_types":["text query string","pre-computed embedding vector (optional)","similarity threshold (0-1)","result limit"],"output_types":["ranked list of documents with similarity scores","vector distance metrics","document metadata and content"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_10","uri":"capability://search.retrieval.search.query.suggestions.and.autocomplete","name":"search query suggestions and autocomplete","description":"Generates search query suggestions and autocomplete results based on indexed documents and query history, allowing agents to provide search suggestions to users or refine queries. The MCP server analyzes document content and popular search terms to generate contextually relevant suggestions without requiring external suggestion services.","intents":["Generate autocomplete suggestions as users type search queries","Suggest refined queries based on popular searches","Provide spelling corrections for misspelled queries","Recommend related search terms based on document content"],"best_for":["Search interfaces with autocomplete functionality","Agents refining user search queries","Systems improving search experience through suggestions"],"limitations":["Suggestion quality depends on indexed document content and query history","No built-in personalization — suggestions are global, not user-specific","Autocomplete latency increases with suggestion count and index size","Spelling correction is limited to typo tolerance — no semantic query correction"],"requires":["Meilisearch instance with suggestion/autocomplete support","Sufficient query history or document content for meaningful suggestions","Indexed documents with searchable fields"],"input_types":["partial query string","suggestion count limit","context filters (optional)"],"output_types":["list of suggested queries","suggestion relevance scores","suggestion source metadata (document-based vs history-based)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_11","uri":"capability://search.retrieval.search.result.highlighting.and.snippet.generation","name":"search result highlighting and snippet generation","description":"Generates highlighted search result snippets that show query terms in context, allowing agents to display search results with visual emphasis on matching terms. The MCP server extracts relevant text passages around matching terms, applies highlighting markup, and generates concise snippets suitable for search result display without requiring agents to implement snippet generation logic.","intents":["Generate highlighted snippets showing search terms in context","Display search results with visual emphasis on matching terms","Extract relevant passages from long documents","Improve search result readability and relevance perception"],"best_for":["Search result display interfaces","Agents generating search result summaries","Systems improving search result presentation"],"limitations":["Snippet generation is limited to indexed text fields — binary content not supported","Highlighting accuracy depends on tokenization — complex queries may produce inaccurate highlights","Snippet length is fixed — no dynamic snippet sizing based on context","No built-in HTML escaping — highlighting markup must be carefully handled to avoid XSS"],"requires":["Meilisearch instance with highlighting support","Indexed documents with text content in searchable fields","Highlighting configuration (tag names, snippet length)"],"input_types":["search query","document ID or content","highlighting configuration (tag names, snippet length)"],"output_types":["highlighted snippet with markup","matching term positions","snippet metadata (source field, relevance)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_2","uri":"capability://search.retrieval.hybrid.search.combining.full.text.and.semantic.ranking","name":"hybrid search combining full-text and semantic ranking","description":"Executes queries that simultaneously perform full-text BM25 search and semantic vector search, then combines rankings using a configurable fusion algorithm (e.g., reciprocal rank fusion or weighted score blending). The MCP server orchestrates both search paths in parallel and merges results, allowing agents to leverage keyword precision and semantic understanding in a single query.","intents":["Search for documents using both keyword matching and semantic similarity","Improve search recall by combining exact matches with semantically related results","Tune the balance between keyword precision and semantic understanding per query","Reduce irrelevant results by requiring both keyword and semantic relevance"],"best_for":["RAG systems needing high-quality document retrieval","Search applications where keyword precision and semantic understanding are both important","Teams optimizing search quality without implementing custom ranking algorithms"],"limitations":["Fusion algorithm performance depends on relative weighting — no automatic tuning of keyword vs semantic balance","Latency is sum of full-text + semantic search times; parallel execution adds ~50-200ms overhead","Requires both full-text indexed fields and pre-computed embedding vectors for all documents","No built-in A/B testing framework for fusion algorithm tuning"],"requires":["Meilisearch instance with both full-text and vector search enabled","Documents indexed with both searchable text fields and embedding vectors","Embedding model for query embedding (same as semantic search)","Fusion algorithm configuration (weights, ranking strategy)"],"input_types":["text query string","fusion weights (keyword vs semantic balance)","filters for both full-text and vector search","result limit"],"output_types":["merged ranked result list","per-result component scores (full-text + semantic)","fusion algorithm metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_3","uri":"capability://data.processing.analysis.document.indexing.and.schema.management.via.mcp","name":"document indexing and schema management via mcp","description":"Manages document indexing operations and index schema configuration through MCP tools, allowing agents to create indexes, define searchable fields, set embedding field mappings, and configure ranking rules without direct API calls. The MCP server translates high-level indexing requests into Meilisearch API operations, handling schema validation and index creation workflows.","intents":["Create a new search index with custom field definitions","Configure which document fields are searchable vs filterable","Define embedding field mappings for semantic search","Update ranking rules to prioritize certain fields or attributes","Batch index documents from external sources (files, databases, APIs)"],"best_for":["Agents automating search infrastructure setup","Teams building dynamic indexing pipelines","Developers prototyping search applications without manual Meilisearch configuration"],"limitations":["Schema changes require index recreation for some field type changes — no zero-downtime schema evolution","Batch indexing latency scales linearly with document count; no built-in batching optimization","No transaction support — partial failures during batch indexing may leave index in inconsistent state","Field type changes (e.g., text to integer) require full reindexing"],"requires":["Meilisearch instance with admin API access","Admin API key with index creation permissions","Document schema definition (field names, types, searchability)"],"input_types":["index name and configuration","field definitions (name, type, searchable, filterable, sortable)","embedding field mapping (field name, embedding model)","document batch (JSON array or streaming)"],"output_types":["index creation confirmation","indexing task status and progress","schema validation results","document count and indexing statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_4","uri":"capability://search.retrieval.faceted.search.and.filtering.with.metadata","name":"faceted search and filtering with metadata","description":"Enables filtering search results by document metadata (facets) using a declarative filter syntax, allowing agents to narrow results by categories, tags, dates, or custom attributes. The MCP server translates filter expressions into Meilisearch filter queries, supporting complex boolean logic (AND, OR, NOT) and range queries without requiring users to understand Meilisearch's filter DSL.","intents":["Filter search results by category, tag, or custom metadata","Apply date range filters to find recent documents","Combine multiple filters with boolean logic","Get facet counts to display filter options to users","Build drill-down search experiences with hierarchical filters"],"best_for":["E-commerce and product search applications","Content discovery systems with rich metadata","Agents building filtered search workflows"],"limitations":["Filter performance degrades with high cardinality fields (millions of unique values)","Facet count computation adds latency — no built-in caching for facet results","Complex nested filters (deep boolean logic) may be harder to express than SQL WHERE clauses","No built-in facet ordering or ranking — results returned in arbitrary order"],"requires":["Meilisearch instance with filterable fields defined in schema","Documents indexed with metadata fields marked as filterable","Filter expression syntax understanding (Meilisearch filter DSL or MCP abstraction)"],"input_types":["filter expressions (e.g., 'category = \"electronics\" AND price < 100')","facet field names","range query bounds (min, max)"],"output_types":["filtered result set","facet counts per filter value","applied filter metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_5","uri":"capability://data.processing.analysis.real.time.index.updates.and.document.mutations","name":"real-time index updates and document mutations","description":"Supports real-time document updates, deletions, and partial field modifications through MCP tools, allowing agents to mutate indexed documents without full reindexing. The MCP server batches mutations and applies them to the Meilisearch index with configurable commit strategies (immediate vs batched), maintaining index consistency while optimizing throughput.","intents":["Update a document's fields without reindexing the entire document","Delete documents from the index by ID or filter criteria","Perform bulk mutations on multiple documents","Track mutation status and index consistency","Implement soft deletes or status updates on indexed content"],"best_for":["Real-time search applications with frequently changing data","Agents managing document lifecycle (create, update, delete)","Systems requiring immediate search index consistency"],"limitations":["Bulk mutations are not atomic — partial failures may leave index in inconsistent state","Delete-by-filter operations are slower than delete-by-ID; no built-in optimization","Real-time updates add latency to search queries during high mutation rates","No built-in mutation versioning or rollback — changes are permanent once committed"],"requires":["Meilisearch instance with write API access","Admin API key with document mutation permissions","Document IDs for targeted updates/deletes"],"input_types":["document ID and updated fields","delete filter expressions","batch mutation operations (array of updates/deletes)","commit strategy (immediate, batched, delayed)"],"output_types":["mutation confirmation with task ID","index update status","affected document count"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_6","uri":"capability://search.retrieval.search.result.ranking.customization.and.sorting","name":"search result ranking customization and sorting","description":"Allows agents to customize result ranking through configurable ranking rules that prioritize fields, apply custom scoring functions, or sort by multiple attributes. The MCP server translates ranking preferences into Meilisearch ranking rule configurations, supporting field-based sorting, custom attribute weighting, and multi-field sort orders without requiring agents to understand Meilisearch's ranking rule syntax.","intents":["Sort search results by relevance, date, price, or custom attributes","Apply custom ranking rules that prioritize certain fields over others","Implement multi-field sorting (e.g., sort by relevance, then by date)","Boost or penalize results based on document attributes","Create personalized ranking rules for different user segments"],"best_for":["E-commerce search with price/rating-based ranking","Content discovery with temporal relevance (recent content prioritized)","Agents implementing custom ranking strategies"],"limitations":["Custom scoring functions are limited to field-based weighting — no arbitrary function support","Ranking rule changes require index rebuild for optimal performance","No built-in A/B testing for ranking rule comparison","Complex multi-field ranking may be slower than simple relevance-only sorting"],"requires":["Meilisearch instance with ranking rules enabled","Sortable fields defined in index schema","Ranking rule configuration (field weights, sort order)"],"input_types":["sort field names and order (ascending/descending)","ranking rule definitions (field weights, custom attributes)","boost/penalty factors per field"],"output_types":["ranked result list with applied sorting","ranking rule metadata","sort order confirmation"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_7","uri":"capability://data.processing.analysis.search.analytics.and.performance.monitoring","name":"search analytics and performance monitoring","description":"Exposes search analytics and index performance metrics through MCP tools, allowing agents to monitor query performance, track popular searches, identify slow queries, and optimize index configuration. The MCP server queries Meilisearch analytics endpoints and presents metrics in a structured format suitable for monitoring dashboards or automated optimization workflows.","intents":["Monitor search query latency and identify slow queries","Track most popular search terms to optimize index configuration","Analyze search result click-through rates and user engagement","Get index size and memory usage statistics","Identify optimization opportunities based on query patterns"],"best_for":["Teams optimizing search performance and relevance","Agents implementing automated index tuning","Search applications requiring performance monitoring"],"limitations":["Analytics data retention is limited — historical data may be purged after retention period","No built-in anomaly detection for query performance degradation","Analytics granularity is limited to query-level metrics; no field-level performance breakdown","Real-time analytics may lag behind actual query execution by seconds"],"requires":["Meilisearch instance with analytics enabled","Admin API key with analytics read permissions","Sufficient query volume to generate meaningful analytics"],"input_types":["time range for analytics queries","filter criteria (query term, user, result count)","metric type (latency, popularity, engagement)"],"output_types":["query performance metrics (latency percentiles, throughput)","popular search terms with frequency","slow query logs with execution details","index statistics (size, document count, memory usage)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_8","uri":"capability://search.retrieval.multi.language.search.with.language.specific.tokenization","name":"multi-language search with language-specific tokenization","description":"Supports full-text search across multiple languages with language-specific tokenization, stemming, and stop word removal. The MCP server automatically detects document language or accepts explicit language hints, applying appropriate linguistic processing to improve search accuracy across language boundaries without requiring agents to manage language-specific configurations.","intents":["Search documents in multiple languages with appropriate tokenization","Improve search accuracy for non-English content","Handle multilingual document collections transparently","Apply language-specific stemming and stop word removal"],"best_for":["Global applications serving multilingual user bases","Content platforms with documents in multiple languages","Teams building search without language-specific expertise"],"limitations":["Language detection is not 100% accurate for short queries or mixed-language content","Stemming quality varies by language — some languages have better support than others","No built-in language-specific ranking adjustments","Multilingual search may be slower than single-language search due to additional processing"],"requires":["Meilisearch instance with language support enabled","Documents indexed with language metadata or auto-detected language","Language-specific tokenizer and stemmer support in Meilisearch"],"input_types":["text query in any supported language","explicit language hint (optional)","language-specific filter criteria"],"output_types":["search results with language-aware ranking","detected language metadata","language-specific tokenization details"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-meilisearch__cap_9","uri":"capability://search.retrieval.search.result.pagination.and.cursor.based.navigation","name":"search result pagination and cursor-based navigation","description":"Implements pagination and cursor-based result navigation through MCP tools, allowing agents to retrieve large result sets efficiently without loading all results into memory. The MCP server supports both offset-based pagination (for small result sets) and cursor-based pagination (for large result sets), with configurable page sizes and result ordering.","intents":["Retrieve search results in manageable pages","Navigate large result sets without loading all results","Implement cursor-based pagination for efficient result streaming","Get total result count and pagination metadata","Handle result set changes during pagination (cursor stability)"],"best_for":["Search applications with large result sets","Agents implementing paginated search workflows","Systems requiring efficient result streaming"],"limitations":["Offset-based pagination is inefficient for large offsets (>10k) due to full result set scanning","Cursor-based pagination requires stable result ordering — dynamic ranking may cause inconsistencies","No built-in result set caching — pagination across large offsets may be slow","Cursor expiration may occur if result set changes between pagination requests"],"requires":["Meilisearch instance with pagination support","Stable result ordering (required for cursor-based pagination)","Page size configuration"],"input_types":["page number or cursor token","page size (results per page)","sort order (for cursor stability)"],"output_types":["paginated result list","next/previous cursor tokens","total result count","pagination metadata (current page, total pages)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Meilisearch instance running (v1.0+)","Documents pre-indexed in Meilisearch with searchable fields defined","API key or host URL for Meilisearch server","Meilisearch instance with vector search enabled (v1.3+)","Documents indexed with embedding vectors in a designated field","Embedding model API key (OpenAI, Anthropic, Ollama) or local embedding service","Vector dimension matching between query embeddings and indexed vectors","Meilisearch instance with suggestion/autocomplete support","Sufficient query history or document content for meaningful suggestions","Indexed documents with searchable fields"],"failure_modes":["Typo tolerance is configurable but adds latency — default 1 typo per word up to 5 characters","BM25 ranking is language-agnostic but not optimized for all languages equally","Search performance degrades with very large result sets (100k+ documents) without proper indexing strategy","Embedding quality depends on the embedding model used — no built-in model selection optimization","Vector search latency increases with index size; Meilisearch is optimized for <10M vectors per index","Requires pre-computed embeddings for all documents — no on-the-fly embedding generation during indexing","No built-in reranking — results are ordered purely by vector distance, not by relevance to query intent","Suggestion quality depends on indexed document content and query history","No built-in personalization — suggestions are global, not user-specific","Autocomplete latency increases with suggestion count and index size","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=meilisearch","compare_url":"https://unfragile.ai/compare?artifact=meilisearch"}},"signature":"L/dcdMUKFBy7L0U3gvk6NEwagLlv8qB2Rp3QQ0yWt5pUV0mZWhZCyCa9yk/XbhuK7tvwCFbno9F8GrP+0oEsDA==","signedAt":"2026-06-19T21:01:21.717Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meilisearch","artifact":"https://unfragile.ai/meilisearch","verify":"https://unfragile.ai/api/v1/verify?slug=meilisearch","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}