{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-ai-mentoramcp-server","slug":"npm-ai-mentoramcp-server","name":"@ai-mentora/mcp-server","type":"mcp","url":"https://www.npmjs.com/package/@ai-mentora/mcp-server","page_url":"https://unfragile.ai/npm-ai-mentoramcp-server","categories":["mcp-servers"],"tags":["mcp","elasticsearch","ai-mentora","case-law","server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-ai-mentoramcp-server__cap_0","uri":"capability://search.retrieval.elasticsearch.backed.full.text.search.for.canadian.case.law","name":"elasticsearch-backed full-text search for canadian case law","description":"Implements full-text retrieval over Canadian legal cases using Elasticsearch as the backend indexing and query engine. The MCP server exposes an `es-fulltext-retrieve` tool that translates natural language queries into Elasticsearch DSL queries, handling tokenization, stemming, and relevance ranking through Elasticsearch's BM25 algorithm. Results are returned with relevance scores and metadata (case name, jurisdiction, year, citation) for legal research workflows.","intents":["Search for relevant Canadian case law by legal topic or keyword without manual database navigation","Retrieve precedent cases matching specific legal arguments or fact patterns","Build legal research tools that integrate case law search into LLM-powered legal assistants","Batch-query multiple case law databases to support comparative legal analysis"],"best_for":["Legal tech teams building AI-assisted legal research platforms","Law firms integrating case law search into internal LLM agents","Solo developers prototyping legal AI assistants with MCP-compatible clients"],"limitations":["Search limited to Canadian case law corpus only — no international jurisdiction support","Elasticsearch instance must be pre-indexed with case law data — no built-in indexing pipeline provided","Query latency depends on Elasticsearch cluster performance and network round-trip time","No semantic/vector search capability — relies on lexical matching only, may miss synonymous legal concepts","No access control or permission model — all queries have equal access to full case law index"],"requires":["Running Elasticsearch instance (version 7.0+) with indexed Canadian case law documents","MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Network connectivity to Elasticsearch cluster (local or remote)","Node.js 16+ runtime for MCP server process"],"input_types":["text (natural language legal queries)","structured queries (Elasticsearch DSL JSON)"],"output_types":["structured JSON (case metadata, relevance scores, citation information)","text (formatted case summaries or excerpts)"],"categories":["search-retrieval","legal-research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-ai-mentoramcp-server__cap_1","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.for.legal.research.tools","name":"mcp protocol server implementation for legal research tools","description":"Implements the Model Context Protocol (MCP) server specification, exposing legal research capabilities as standardized MCP tools that can be discovered and invoked by MCP-compatible clients (Claude Desktop, custom agents, LLM frameworks). The server handles MCP request/response serialization, tool schema definition, and lifecycle management (initialization, resource listing, tool invocation). Follows MCP conventions for error handling, capability advertisement, and stateless request processing.","intents":["Integrate legal case law search into Claude Desktop or other MCP-compatible LLM interfaces without custom plugins","Enable LLM agents to discover and call legal research tools through standard MCP protocol","Build composable legal AI systems where case law search is one tool among many MCP-exposed capabilities","Deploy legal research as a microservice accessible to multiple MCP clients simultaneously"],"best_for":["Teams building Claude-integrated legal research tools","Developers creating multi-tool LLM agent systems using MCP","Organizations standardizing on MCP for AI tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms latency per request compared to direct API calls","Requires MCP-compatible client — cannot be used with REST-only integrations or legacy LLM APIs","No built-in authentication — relies on client-side MCP configuration for credential management","Stateless design means no session persistence — each query is independent with no conversation context carryover"],"requires":["MCP-compatible client (Claude Desktop 0.1.0+, or custom MCP client implementation)","Node.js 16+ runtime","MCP SDK for Node.js (@modelcontextprotocol/sdk or compatible)"],"input_types":["MCP tool invocation requests (JSON-RPC 2.0 format)"],"output_types":["MCP tool responses (JSON-RPC 2.0 format with structured results)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-ai-mentoramcp-server__cap_2","uri":"capability://tool.use.integration.tool.schema.definition.and.discovery.for.case.law.search","name":"tool schema definition and discovery for case law search","description":"Defines and advertises the `es-fulltext-retrieve` tool schema through MCP's tool discovery mechanism, specifying input parameters (query string, filters, result limit), output format, and tool description. The schema enables MCP clients to understand the tool's capabilities without documentation, validate inputs before invocation, and generate appropriate prompts for LLM agents. Schema includes parameter constraints (e.g., max results, query length limits) and type information for structured input validation.","intents":["Allow LLM agents to understand what parameters the case law search tool accepts without hardcoded knowledge","Enable MCP clients to validate user queries against tool schema before sending to server","Generate contextual prompts that guide LLM agents on how to invoke the search tool effectively","Support tool discovery in multi-tool environments where agents choose between available capabilities"],"best_for":["LLM agent developers building systems that dynamically discover and use tools","MCP client implementers that need to present available tools to users","Teams building flexible legal research systems where tool capabilities may evolve"],"limitations":["Schema is static at server startup — cannot dynamically update tool definitions without server restart","No versioning mechanism for schema changes — clients may cache outdated schema definitions","Limited to JSON Schema for parameter definition — cannot express complex conditional logic or dependencies between parameters"],"requires":["MCP-compatible client that supports tool discovery","JSON Schema understanding in client implementation"],"input_types":["JSON Schema definitions (tool parameters)"],"output_types":["MCP tool schema advertisement (JSON format)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-ai-mentoramcp-server__cap_3","uri":"capability://search.retrieval.query.parameter.filtering.and.result.pagination.for.case.law.retrieval","name":"query parameter filtering and result pagination for case law retrieval","description":"Supports parameterized queries to the Elasticsearch backend, allowing callers to specify filters (jurisdiction, year range, case type), result limits, and pagination offsets. Parameters are validated against schema constraints before Elasticsearch query construction, preventing injection attacks and resource exhaustion. Results are paginated to limit response size and enable iterative result browsing without overwhelming the client or network.","intents":["Narrow case law search results by jurisdiction (provincial, federal) or time period to find relevant precedents","Limit result set size to reduce response latency and bandwidth for large result sets","Implement pagination in legal research UIs to browse results incrementally","Prevent resource exhaustion attacks by enforcing maximum result limits"],"best_for":["Legal research UIs that need to filter results by jurisdiction or date","Batch processing systems that retrieve large result sets incrementally","Security-conscious deployments that need to prevent resource exhaustion"],"limitations":["Filter options are fixed at server configuration time — cannot dynamically add new filter dimensions without code changes","Pagination is offset-based, which becomes inefficient for very large result sets (>10,000 results)","No support for complex filter combinations (e.g., AND/OR logic across multiple dimensions) — only simple equality filters","Filter values must be pre-defined in Elasticsearch mapping — arbitrary filter values will fail silently"],"requires":["Elasticsearch index with filterable fields (jurisdiction, year, case_type, etc.)","Client support for passing filter parameters in MCP tool invocation"],"input_types":["text (query string)","structured parameters (jurisdiction, year_min, year_max, result_limit, offset)"],"output_types":["structured JSON (filtered case results with pagination metadata)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-ai-mentoramcp-server__cap_4","uri":"capability://data.processing.analysis.elasticsearch.connection.management.and.query.translation","name":"elasticsearch connection management and query translation","description":"Manages persistent or pooled connections to the Elasticsearch cluster and translates high-level search requests into Elasticsearch DSL queries. The server constructs appropriate Elasticsearch queries (match, bool, range queries) based on input parameters, handles connection pooling to avoid connection exhaustion, and implements retry logic for transient Elasticsearch failures. Query translation includes text analysis (tokenization, stemming) configuration to match the Elasticsearch index's analyzer settings.","intents":["Maintain efficient connections to Elasticsearch without creating new connections per query","Translate natural language or structured search requests into Elasticsearch DSL without exposing DSL syntax to clients","Handle Elasticsearch failures gracefully with automatic retries for transient errors","Ensure search behavior matches the Elasticsearch index's text analysis configuration"],"best_for":["Production deployments requiring reliable Elasticsearch connectivity","Teams that want to abstract Elasticsearch complexity from client applications","Systems handling high query volume where connection pooling is critical"],"limitations":["Connection pool size is fixed at server startup — cannot dynamically adjust for varying load","Retry logic uses exponential backoff with fixed parameters — not configurable per deployment","Query translation is limited to simple queries — complex Elasticsearch features (aggregations, nested queries) are not exposed","No query caching — identical queries hit Elasticsearch every time, no deduplication across clients"],"requires":["Elasticsearch cluster (7.0+) with network accessibility from MCP server","Elasticsearch client library (elasticsearch npm package or compatible)","Elasticsearch index pre-configured with appropriate analyzers and mappings"],"input_types":["text (search query)","structured parameters (filters, limits)"],"output_types":["structured JSON (Elasticsearch results with metadata)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Running Elasticsearch instance (version 7.0+) with indexed Canadian case law documents","MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Network connectivity to Elasticsearch cluster (local or remote)","Node.js 16+ runtime for MCP server process","MCP-compatible client (Claude Desktop 0.1.0+, or custom MCP client implementation)","Node.js 16+ runtime","MCP SDK for Node.js (@modelcontextprotocol/sdk or compatible)","MCP-compatible client that supports tool discovery","JSON Schema understanding in client implementation","Elasticsearch index with filterable fields (jurisdiction, year, case_type, etc.)"],"failure_modes":["Search limited to Canadian case law corpus only — no international jurisdiction support","Elasticsearch instance must be pre-indexed with case law data — no built-in indexing pipeline provided","Query latency depends on Elasticsearch cluster performance and network round-trip time","No semantic/vector search capability — relies on lexical matching only, may miss synonymous legal concepts","No access control or permission model — all queries have equal access to full case law index","MCP protocol overhead adds ~50-100ms latency per request compared to direct API calls","Requires MCP-compatible client — cannot be used with REST-only integrations or legacy LLM APIs","No built-in authentication — relies on client-side MCP configuration for credential management","Stateless design means no session persistence — each query is independent with no conversation context carryover","Schema is static at server startup — cannot dynamically update tool definitions without server restart","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.45,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:44.193Z","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=npm-ai-mentoramcp-server","compare_url":"https://unfragile.ai/compare?artifact=npm-ai-mentoramcp-server"}},"signature":"Yzc679lAVKxEvgHNAw3VWAx/tKK88I0789xM2TbZuTqwe9MHKNWe07spt5RLrGTGW1TTD4pir/BlWUY++hD2Cg==","signedAt":"2026-06-21T22:27:41.031Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-ai-mentoramcp-server","artifact":"https://unfragile.ai/npm-ai-mentoramcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=npm-ai-mentoramcp-server","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"}}