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Uses MCP protocol to expose Axiom as a tool-callable service, allowing Claude and other LLM clients to invoke queries without users learning AQL syntax.","intents":["I want to ask 'what errors happened in production last hour' without writing query syntax","I need to investigate a spike in latency by asking natural questions about my logs","I want to drill down into trace data by conversationally refining my search"],"best_for":["DevOps engineers and SREs debugging production issues via conversational interfaces","Teams using Claude or other MCP-compatible LLMs as their primary investigation tool","Organizations wanting to reduce time-to-insight by eliminating query language learning curve"],"limitations":["LLM interpretation of intent may fail on ambiguous or complex multi-step queries requiring domain context","No caching of frequently-asked queries — each natural language question triggers a new LLM inference + Axiom API call","Limited to Axiom's query capabilities — cannot perform cross-dataset joins or custom aggregations beyond AQL support","Latency depends on LLM response time (typically 1-3s) plus Axiom API latency, making real-time debugging slower than direct query writing"],"requires":["Axiom account with API credentials (API token)","MCP-compatible LLM client (Claude, or other LLM with MCP support)","Network access to Axiom API endpoints","Python 3.8+ (if running MCP server locally)"],"input_types":["natural language text (user questions)","optional context (time ranges, dataset names, filter hints)"],"output_types":["structured query results (JSON)","formatted log entries with timestamps and fields","aggregation results (counts, percentiles, distributions)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-axiom__cap_1","uri":"capability://data.processing.analysis.multi.dataset.event.correlation.and.cross.filtering","name":"multi-dataset event correlation and cross-filtering","description":"Enables querying across Axiom datasets (logs, traces, metrics) in a single natural language request by mapping dataset names and field relationships, then executing coordinated queries that correlate events across sources. The MCP server maintains awareness of available datasets and their schemas, allowing the LLM to construct queries that join or filter across multiple event streams.","intents":["I want to find all traces where the associated logs show an error in the same time window","Show me requests that have high latency AND corresponding error logs in the backend service","Correlate user session IDs across logs and traces to understand a customer's full journey"],"best_for":["Platform teams managing multiple observability data sources (logs, traces, metrics) in Axiom","Incident responders needing to correlate signals across services to root-cause issues","SREs building automated runbooks that query multiple event types in sequence"],"limitations":["Correlation relies on shared field names or explicit mapping — no automatic schema inference across datasets","Performance degrades with large result sets from multiple datasets; no built-in pagination or sampling","LLM may struggle to construct correct correlations if dataset relationships are implicit or poorly documented","No transaction-like guarantees — results may be stale if datasets update at different rates"],"requires":["Multiple Axiom datasets configured and accessible via API token","Documented schema or field mappings for correlation (e.g., trace_id, request_id)","MCP-compatible LLM with sufficient context window to handle multi-dataset queries"],"input_types":["natural language questions referencing multiple datasets","optional correlation hints (field names, time windows)"],"output_types":["correlated result sets (merged JSON with matched events)","summary statistics (e.g., '5 traces with 3 associated error logs')"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-axiom__cap_2","uri":"capability://planning.reasoning.time.range.aware.contextual.querying.with.relative.time.expressions","name":"time-range-aware contextual querying with relative time expressions","description":"Parses natural language time expressions ('last hour', 'since 3pm', 'past 7 days') and converts them to absolute Axiom query time ranges, maintaining context across multi-turn conversations so follow-up questions inherit the same time window. The MCP server tracks conversation state to avoid re-specifying time ranges in each query.","intents":["I want to ask 'what happened in the last hour' and have follow-up questions automatically use that same window","Show me errors since 2pm without having to specify exact timestamps","Compare logs from 'yesterday' vs 'today' in a single conversation"],"best_for":["On-call engineers debugging recent incidents who need fast, context-aware queries","Teams using conversational debugging where time context should persist across questions","Non-technical stakeholders who think in relative time ('last night's outage') rather than timestamps"],"limitations":["Relative time parsing is heuristic-based and may fail on ambiguous expressions like 'last week' (does it mean 7 days ago or the previous calendar week?)","Conversation state is session-local — time context is lost if the MCP connection resets","No support for recurring time patterns (e.g., 'every Tuesday at 3pm') — only absolute or relative ranges","Timezone handling depends on Axiom account configuration; mismatches can cause off-by-one-hour errors"],"requires":["Axiom API with time-range filtering support","MCP client that maintains conversation state across turns","Correct timezone configuration in Axiom account"],"input_types":["natural language time expressions","relative or absolute timestamps"],"output_types":["query results scoped to interpreted time range","confirmation of applied time window (e.g., 'querying 2024-01-15 14:00 to 15:00')"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-axiom__cap_3","uri":"capability://memory.knowledge.schema.aware.field.suggestion.and.auto.completion","name":"schema-aware field suggestion and auto-completion","description":"Introspects Axiom dataset schemas to provide the LLM with available fields, data types, and common values, enabling intelligent suggestions when users ask vague questions (e.g., 'show me errors' → suggests filtering by 'level=error' or 'status_code>=400'). The MCP server caches schema metadata and exposes it as context to the LLM for better query generation.","intents":["I want to ask 'show me errors' and have the system automatically suggest the right error field to filter on","I need to know what fields are available in my logs before asking a question","Help me construct a query by suggesting relevant fields based on my question"],"best_for":["Teams with large or unfamiliar datasets where field names are not immediately obvious","New team members onboarding to observability systems who don't know the schema","Automated runbooks that need to discover available fields dynamically"],"limitations":["Schema caching may be stale if new fields are added to Axiom datasets — requires manual refresh or TTL-based invalidation","Field suggestions are based on name heuristics and type inference; may not capture domain semantics (e.g., 'user_id' field might not be recognized as a correlation key)","Large schemas (100+ fields) may exceed LLM context limits, forcing truncation or sampling","No support for nested or dynamic fields — only top-level schema introspection"],"requires":["Axiom API with schema introspection endpoint","MCP server with schema caching layer","LLM with sufficient context window for schema metadata"],"input_types":["natural language questions","optional dataset name hints"],"output_types":["suggested field names and filter values","schema documentation (field types, cardinality)","example queries using available fields"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-axiom__cap_4","uri":"capability://data.processing.analysis.trace.aware.debugging.with.span.level.filtering.and.aggregation","name":"trace-aware debugging with span-level filtering and aggregation","description":"Exposes Axiom's trace data (spans, parent-child relationships, duration metrics) to the LLM for querying and analyzing distributed traces. Enables filtering by span attributes, duration thresholds, and error status, then aggregates results to identify slow or failing spans across traces. The MCP server understands trace structure (trace_id, span_id, parent_span_id) and can correlate spans with logs.","intents":["Show me all traces where the database query span took longer than 500ms","Find traces with failed spans in the payment service and show me the error messages","What's the slowest span type across all traces in the last hour?"],"best_for":["Backend engineers debugging performance issues in distributed systems","Teams using Axiom for trace collection (OpenTelemetry, Jaeger, etc.) who want conversational trace analysis","SREs building automated performance monitoring and alerting"],"limitations":["Trace analysis is limited to Axiom's trace schema — custom span attributes may not be indexed or queryable","Large traces (1000+ spans) may timeout or exceed query limits when aggregating across many traces","No visualization of trace waterfall diagrams — results are text-based, making it hard to understand span relationships visually","Correlation between traces and logs requires shared trace_id field; missing or inconsistent trace IDs break correlation"],"requires":["Axiom configured with trace data ingestion (OpenTelemetry, Jaeger, or similar)","Traces must include standard fields (trace_id, span_id, parent_span_id, duration)","MCP server with trace-aware query generation"],"input_types":["natural language questions about trace performance or errors","optional span attribute filters (service name, operation name, status)"],"output_types":["filtered trace results with span details","aggregated metrics (p50/p95/p99 latencies, error rates by span type)","correlated logs for failed spans"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-axiom__cap_5","uri":"capability://tool.use.integration.mcp.protocol.based.tool.registration.and.function.calling","name":"mcp-protocol-based tool registration and function calling","description":"Implements the Model Context Protocol (MCP) server specification, exposing Axiom query capabilities as callable tools that LLM clients (Claude, etc.) can invoke with structured arguments. Uses MCP's resource and tool definitions to declare available queries, their parameters, and return types, enabling the LLM to autonomously decide when to query Axiom and how to interpret results.","intents":["I want Claude to automatically query my Axiom logs when I ask debugging questions","I need to build an LLM agent that can investigate production issues by querying logs and traces","I want to expose Axiom as a tool in my MCP-compatible LLM application"],"best_for":["Teams building LLM agents and agentic workflows that need observability data access","Claude users who want to integrate Axiom into their conversations","Developers building MCP-compatible applications that need to expose observability tools"],"limitations":["MCP protocol overhead adds ~100-200ms per tool invocation for serialization and deserialization","Tool definitions must be pre-declared — dynamic tool generation is not supported","LLM may invoke tools incorrectly or redundantly if it misunderstands the tool schema or available parameters","No built-in rate limiting or quota enforcement — relies on Axiom API limits to prevent abuse"],"requires":["MCP-compatible LLM client (Claude, or other LLM with MCP support)","MCP server implementation (Axiom's mcp-server-axiom)","Axiom API credentials and network access"],"input_types":["structured tool arguments (query string, dataset name, time range, filters)"],"output_types":["structured query results (JSON)","tool execution status and error messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-axiom__cap_6","uri":"capability://planning.reasoning.conversational.multi.turn.debugging.with.context.preservation","name":"conversational multi-turn debugging with context preservation","description":"Maintains conversation state across multiple turns, preserving query context (selected datasets, time ranges, filters) so follow-up questions can reference previous results without re-specifying parameters. The MCP server tracks conversation history and allows the LLM to refer back to earlier queries (e.g., 'show me more details about the error from the last query').","intents":["I want to ask 'what errors happened?' then follow up with 'show me the stack traces' without re-specifying the time range","I need to drill down into results from a previous query without starting over","I want the system to remember my dataset selection across multiple questions"],"best_for":["On-call engineers conducting multi-step incident investigations","Teams using conversational debugging as their primary troubleshooting method","Scenarios where query context (time range, dataset) should persist across multiple questions"],"limitations":["Conversation state is session-local — context is lost if the MCP connection resets or the session expires","No persistent conversation history — state is not saved to disk or database, only in memory","Large conversation histories may exceed LLM context limits, forcing truncation of earlier queries","Ambiguous references to previous results (e.g., 'the error') may be misinterpreted if multiple errors were found"],"requires":["MCP client that maintains conversation state across turns","LLM with sufficient context window to track conversation history","Axiom API with stateless query support (no session management required)"],"input_types":["natural language questions in multi-turn conversation","optional references to previous results"],"output_types":["query results with inherited context","conversation summary (e.g., 'using dataset=logs, time_range=last_hour')"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Axiom account with API credentials (API token)","MCP-compatible LLM client (Claude, or other LLM with MCP support)","Network access to Axiom API endpoints","Python 3.8+ (if running MCP server locally)","Multiple Axiom datasets configured and accessible via API token","Documented schema or field mappings for correlation (e.g., trace_id, request_id)","MCP-compatible LLM with sufficient context window to handle multi-dataset queries","Axiom API with time-range filtering support","MCP client that maintains conversation state across turns","Correct timezone configuration in Axiom account"],"failure_modes":["LLM interpretation of intent may fail on ambiguous or complex multi-step queries requiring domain context","No caching of frequently-asked queries — each natural language question triggers a new LLM inference + Axiom API call","Limited to Axiom's query capabilities — cannot perform cross-dataset joins or custom aggregations beyond AQL support","Latency depends on LLM response time (typically 1-3s) plus Axiom API latency, making real-time debugging slower than direct query writing","Correlation relies on shared field names or explicit mapping — no automatic schema inference across datasets","Performance degrades with large result sets from multiple datasets; no built-in pagination or sampling","LLM may struggle to construct correct correlations if dataset relationships are implicit or poorly documented","No transaction-like guarantees — results may be stale if datasets update at different rates","Relative time parsing is heuristic-based and may fail on ambiguous expressions like 'last week' (does it mean 7 days ago or the previous calendar week?)","Conversation state is session-local — time context is lost if the MCP connection resets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"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":"2026-06-17T09:51:02.371Z","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=axiom","compare_url":"https://unfragile.ai/compare?artifact=axiom"}},"signature":"6rmrl+oMqWQh7dFGBQhwV5CwWhHe2nNX6Zj5ix8AH4W53wG8bcndlMPdE92hyZbv7BvsRCSkDer4ulgCueg+Dw==","signedAt":"2026-06-20T00:41:06.115Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/axiom","artifact":"https://unfragile.ai/axiom","verify":"https://unfragile.ai/api/v1/verify?slug=axiom","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"}}