io.github.mcpsmiths/tracehub-mcp
PYPI · TRACEHUB-MCP
Query OTel traces across Jaeger, Tempo, Traceloop, Datadog, Sentry, X-Ray, New Relic, Honeycomb.
Available components
Last scanned (5 hours ago)
Scores higher than 74% of PyPI servers
Category Monitoring and Observability
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 3 of 68 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency100
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to mcpsmiths/tracehub-mcp). View diagnostics → Pass
- Clear OSI-approved license (Apache-2.0).Pass
- Actively maintained (last published 4 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability66
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 3610 tokens (~190/item across 19 items; 19 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management87
- Stability observed for 26 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 19 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 19 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 4 Nov 26 +1
- Package version: 0.13.0 → 0.13.1 functional
- 3 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 2 Nov 26 −3
- Stability: pass → 0.77 functional
- 31 Oct 26 +3
- GHSA-7xgj-8392-5q4r no longer affects this package ▲ security
- GHSA-vqqp-x5qm-5qp4 no longer affects this package ▲ security
- Known CVEs: fail → pass ▲ security
- Package version: 0.13.0 → 0.13.1 functional
- 27 Oct 26 +1
- Stability: 0.97 → pass security
- 26 Oct 26 −16
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.97 functional
- 24 Oct 26 −3
- GHSA-vqqp-x5qm-5qp4 affects this package: medium ▼ security
- GHSA-7xgj-8392-5q4r affects this package: medium ▼ security
- Known CVEs: pass → fail ▼ security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/tracehub-mcp@0.13.1
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | mcpsmiths/tracehub-mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/mcpsmiths/tracehub-mcp/.github/workflows/release.yml@refs/heads/main |
| Rekor log index | 3614197111 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:142bd7f5fbd629686b8419cf381eae22eefa3232957fc4720a436a1d2f4b3dca |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 68 packages
| Packages resolved | 68 |
|---|---|
| Stale | 1 |
| No linked repository | 2 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_time_windows Compare Time Windows ~221
Compare aggregated LLM usage metrics between two time windows. Runs the same usage aggregation for both ranges and returns the delta - useful for "this week vs last week" or "before/after a deploy" style comparisons of request/token counts.
| Name | Type | Req | Description |
|---|---|---|---|
| gen_ai_request_model | – | – | Filter by requested model name (applied to both ranges) |
| gen_ai_response_model | – | – | Filter by actual model used (applied to both ranges) |
| gen_ai_system | – | – | Filter by LLM provider (applied to both ranges) |
| limit | integer | – | Maximum number of traces to analyze per range (default: 1000) |
| range_a_end | – | – | Range A end time in ISO 8601 format |
| range_a_start | – | – | Range A start time in ISO 8601 format |
| range_b_end | – | – | Range B end time in ISO 8601 format |
| range_b_start | – | – | Range B start time in ISO 8601 format |
| service_name | – | – | Filter by service name (applied to both ranges) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
correlate_trace Correlate Trace ~180
Try to find the corresponding trace in a second, independently- configured backend (e.g. a Datadog trace and its downstream Sentry error, joined) - given a trace_id known to the primary backend. Tries a direct trace_id match in the secondary backend first (confidence "high"); if that fails, falls back to a time-window + service-name-overlap heuristic search (confidence "low"). This is a best-effort correlation, not a guaranteed join - see the always-present `limitations` in the result for why. Requires a secondary backend to be configured via SECONDARY_BACKEND_TYPE/SECONDARY_BACKEND_URL (and any backend-specific fields) environment variables; raises a clear error otherwise.
| Name | Type | Req | Description |
|---|---|---|---|
| trace_id | string | yes | Trace identifier, as known to the primary (already configured) backend. |
| Name | Type | Req | Description |
|---|---|---|---|
| limitations | array | yes | – |
| matches | array | yes | – |
| primary_trace_id | string | yes | – |
No examples provided.
find_errors Find Errors ~83
Find traces with errors. Including detailed error messages, stack traces, and LLM-specific error information.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| limit | integer | – | Maximum error traces to return (default: 100) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_llm_expensive_traces Get LLM Expensive Traces ~168
Find traces with highest LLM token usage. Useful for cost optimization and identifying inefficient prompts.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_request_model | – | – | Filter by requested model name (e.g., "gpt-4") |
| gen_ai_response_model | – | – | Filter by actual model used (e.g., "gpt-4-0613") |
| limit | integer | – | Maximum number of traces to return (default: 10) |
| min_tokens | – | – | Minimum token count threshold (only return traces above this) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_llm_model_stats Get LLM Model Stats ~179
Get detailed performance statistics for a specific LLM model. Analyzes request count, latency percentiles (p50, p95, p99), token usage statistics, error rates, and finish reason distributions.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| limit | integer | – | Maximum traces to analyze (default: 1000, capped by MAX_TRACES_PER_QUERY / --max-traces-per-query) |
| model_name | string | yes | Model name to analyze (e.g., "gpt-4", "claude-3-opus", "gpt-3.5-turbo") |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_llm_slow_traces Get LLM Slow Traces ~172
Find slowest LLM traces by duration. Useful for performance optimization and identifying latency bottlenecks.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_request_model | – | – | Filter by requested model name (e.g., "gpt-4") |
| gen_ai_response_model | – | – | Filter by actual model used (e.g., "gpt-4-0613") |
| limit | integer | – | Maximum number of traces to return (default: 10) |
| min_duration_ms | – | – | Minimum duration threshold in milliseconds (only return traces above this) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_llm_usage Get LLM Usage ~126
Get aggregated LLM usage metrics (token counts) for a time period. Provides breakdowns by model and service.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_request_model | – | – | Filter by requested model name |
| gen_ai_response_model | – | – | Filter by actual model used |
| gen_ai_system | – | – | Filter by LLM provider |
| limit | integer | – | Maximum traces to analyze (default: 1000) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_prompt_version_stats Get Prompt Version Stats ~170
Get aggregated performance stats grouped by prompt name and version. Groups spans by gen_ai.prompt.name + gen_ai.prompt.version, mirroring Langfuse's shipped per-prompt Metrics tab. Real-world adoption of these two attributes is still thin, so this tool may often return an empty list until more instrumentations populate them.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_system | – | – | Filter by LLM provider (openai, anthropic, etc.) |
| limit | integer | – | Maximum spans to analyze (default: 1000) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_session_stats Get Session Stats ~147
Get detailed statistics for a single conversation/session. Analyzes span count, distinct services, time bounds, LLM request/success/ error counts, latency percentiles, and token usage for every span sharing the given gen_ai.conversation.id.
| Name | Type | Req | Description |
|---|---|---|---|
| conversation_id | string | yes | The gen_ai.conversation.id to analyze |
| end_time | – | – | End time in ISO 8601 format |
| limit | integer | – | Maximum spans to analyze (default: 1000) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
get_trace Get Trace ~119
Get complete trace details by trace ID. Returns all spans with attributes, including parsed Opentelemetry data for LLM operations.
| Name | Type | Req | Description |
|---|---|---|---|
| detail_level | string | – | "full" (default) returns every attribute/event value in full, unchanged from this tool's original behavior. "summary" elides known-large gen_ai.* fields (input/output messages, system instructions, r… |
| trace_id | string | yes | Trace identifier |
| Name | Type | Req | Description |
|---|---|---|---|
| detail_level | string | yes | – |
| duration_ms | number | yes | – |
| has_errors | boolean | yes | – |
| llm_summary | – | – | – |
| root_operation | string | yes | – |
| service_name | string | yes | – |
| span_count | integer | yes | – |
| spans | array | yes | – |
| start_time | string | yes | – |
| status | string | yes | – |
| trace_id | string | yes | – |
No examples provided.
investigate_cost_spike Investigate Cost Spike ~297
Investigate an LLM cost spike: compare a recent window against a baseline and rank which models/services contributed most to the change. On-request/pull-based analysis, not a push alert - mirrors SigNoz's own "investigate telemetry cost" skill. Call this when you suspect (or want to check for) a cost increase, rather than polling get_llm_usage by hand.
| Name | Type | Req | Description |
|---|---|---|---|
| baseline_end | – | – | Baseline window end (ISO 8601) |
| baseline_start | – | – | Baseline window start (ISO 8601). If omitted along with baseline_end, auto-computed as the same duration immediately preceding recent_start. |
| gen_ai_request_model | – | – | Filter by requested model name (applied to both windows) |
| gen_ai_response_model | – | – | Filter by actual model used (applied to both windows) |
| gen_ai_system | – | – | Filter by LLM provider (applied to both windows) |
| limit | integer | – | Maximum number of traces to analyze per window (default: 1000) |
| recent_end | string | yes | Recent window end time in ISO 8601 format |
| recent_start | string | yes | Recent window start time in ISO 8601 format |
| service_name | – | – | Filter by service name (applied to both windows) |
| top_n | integer | – | Maximum ranked contributors to return per breakdown (default: 5, max: 50) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
investigate_error_spike Investigate Error Spike ~285
Investigate an error-rate spike: compare a recent window against a baseline and rank which services/models/error types contributed most. is_spike requires both an absolute error-count floor and a relative rate-multiplier to hold, so a tiny sample (e.g. 1 error becoming 2) doesn't read as a spike.
| Name | Type | Req | Description |
|---|---|---|---|
| baseline_end | – | – | Baseline window end (ISO 8601) |
| baseline_start | – | – | Baseline window start (ISO 8601). If omitted along with baseline_end, auto-computed as the same duration immediately preceding recent_start. |
| limit | integer | – | Maximum number of traces to analyze per window (default: 1000) |
| min_error_count_increase | integer | – | Minimum absolute error-count increase to count as a spike (default: 3) |
| rate_multiplier_threshold | number | – | Minimum error-rate multiplier (recent / baseline) to count as a spike (default: 2.0) |
| recent_end | string | yes | Recent window end time in ISO 8601 format |
| recent_start | string | yes | Recent window start time in ISO 8601 format |
| service_name | – | – | Filter by service name (applied to both windows) |
| top_n | integer | – | Maximum ranked contributors to return per breakdown (default: 5, max: 50) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
list_llm_models List LLM Models ~133
List all LLM models being used with usage statistics. Discovers what models are deployed and tracks their usage patterns.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_system | – | – | Filter by LLM provider (e.g., openai, anthropic, cohere) |
| limit | integer | – | Maximum traces to analyze for model discovery (default: 1000) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
list_llm_tools_tool List LLM Tools ~141
List all LLM tools being used by identifying traceloop.span.kind == tool. Discovers which tools/functions LLM applications are calling, grouped by tool name with usage statistics.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_system | – | – | Filter by LLM provider (openai, anthropic, etc.) |
| limit | integer | – | Maximum spans to analyze (default: 1000) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
list_services List Services ~28
List all available services in the OpenTelemetry backend. Returns: JSON string with list of services
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
list_sessions List Sessions ~165
List conversations/sessions grouped by gen_ai.conversation.id. Groups spans that carry the gen_ai.conversation.id attribute (a real, cross-industry OTel semantic convention for session/conversation grouping) to surface per-conversation span counts, token usage, and time bounds - useful for understanding multi-turn conversation activity.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| gen_ai_system | – | – | Filter by LLM provider (openai, anthropic, etc.) |
| limit | integer | – | Maximum spans to analyze (default: 1000) |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| Name | Type | Req | Description |
|---|---|---|---|
| count | integer | yes | – |
| message | – | – | – |
| sessions | array | yes | – |
No examples provided.
search_spans_tool Search Spans ~358
Search for individual OpenTelemetry spans with optional filters. Unlike search_traces, this returns individual spans rather than grouped traces, which is useful for analyzing specific operations or finding spans with certain characteristics (e.g., LLM tool calls with traceloop.span.kind == tool).
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| filters | – | – | Generic filter conditions - list of filter objects with: - field: Field name in dotted notation (e.g., "traceloop.span.kind") - operator: Comparison operator - value: Single value for most operators… |
| gen_ai_request_model | – | – | Filter by requested model name (e.g., "gpt-4") |
| gen_ai_response_model | – | – | Filter by actual model used (e.g., "gpt-4-0613") |
| gen_ai_system | – | – | Filter by LLM provider (e.g., openai, anthropic) |
| has_error | – | – | Filter spans with errors |
| limit | integer | – | Maximum number of spans to return (1-1000, default: 100) |
| max_duration_ms | – | – | Maximum span duration in milliseconds |
| min_duration_ms | – | – | Minimum span duration in milliseconds |
| operation_name | – | – | Filter by operation/span name |
| service_name | – | – | Filter by service name |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| tags | – | – | Additional tag filters as key-value pairs |
| Name | Type | Req | Description |
|---|---|---|---|
| count | integer | yes | – |
| spans | array | yes | – |
No examples provided.
search_traces Search Traces ~365
Search for OpenTelemetry traces with filters. Supports both simple parameters and advanced generic filter system.
| Name | Type | Req | Description |
|---|---|---|---|
| end_time | – | – | End time in ISO 8601 format |
| filters | – | – | Generic filter conditions (advanced) - list of filter objects with: - field: Field name in dotted notation (e.g., "gen_ai.usage.prompt_tokens") - operator: Comparison operator (equals, not_equals, gt… |
| gen_ai_request_model | – | – | Filter by requested model name (e.g., gpt-4) |
| gen_ai_response_model | – | – | Filter by actual model used (e.g., gpt-4-0613) |
| gen_ai_system | – | – | Filter by LLM provider (e.g., openai, anthropic) |
| has_error | – | – | Filter traces with errors |
| limit | integer | – | Maximum number of traces to return (1-1000, default: 100) |
| max_duration_ms | – | – | Maximum trace duration in milliseconds |
| min_duration_ms | – | – | Minimum trace duration in milliseconds |
| operation_name | – | – | Filter by operation/span name |
| service_name | – | – | Filter by service name (use filters for advanced queries) |
| start_time | – | – | Start time in ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| tags | – | – | Additional tag filters as key-value pairs |
| Name | Type | Req | Description |
|---|---|---|---|
| count | integer | yes | – |
| traces | array | yes | – |
No examples provided.
triage_trace Triage Trace ~273
Synthesize a likely-root-cause diagnosis for a trace, instead of returning raw trace data for the caller to re-derive one from every time. Computes a critical path (the "Last Finishing Child" chain actually responsible for the trace's total latency), ranks spans by self-time (latency contribution net of children, top 10), and - when the trace contains an error anywhere under any root span - identifies the deepest error span in the trace's error chain as the likely root cause. Falls back to the highest self-time span as a pure-latency diagnosis when no error is present. Deterministic (no LLM call); works against any configured backend.
| Name | Type | Req | Description |
|---|---|---|---|
| detail_level | string | – | "summary" (default) returns a compact diagnosis only - this differs from get_trace's own "full"-by-default, since a triage result is already a small synthesized diagnosis rather than a raw data dump,… |
| trace_id | string | yes | Trace identifier |
| Name | Type | Req | Description |
|---|---|---|---|
| critical_path | array | yes | – |
| error_chain | – | yes | – |
| top_latency_contributors | array | yes | Top spans by self-time (latency contribution net of children) across the whole trace, capped at 10 entries. |
| trace_id | string | yes | – |
| verdict | object | yes | The synthesized root-cause diagnosis triage_trace produces. likely_root_cause is None only when the trace has no spans at all (a backend returned an empty TraceData) - every non-empty trace always f… |
No examples provided.
How do I install the io.github.mcpsmiths/tracehub-mcp server?
io.github.mcpsmiths/tracehub-mcp runs locally as a PyPI package, launched with uvx tracehub-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · tracehub-mcp
claude mcp add mcpsmiths-tracehub-mcp -- uvx tracehub-mcp
What is the io.github.mcpsmiths/tracehub-mcp server?
io.github.mcpsmiths/tracehub-mcp is listed in the public MCP registry as io.github.mcpsmiths/tracehub-mcp. Query OTel traces across Jaeger, Tempo, Traceloop, Datadog, Sentry, X-Ray, New Relic, Honeycomb. This page covers its PyPI package (tracehub-mcp).
Is the io.github.mcpsmiths/tracehub-mcp server safe to use?
io.github.mcpsmiths/tracehub-mcp scores 77 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.mcpsmiths/tracehub-mcp server expose?
io.github.mcpsmiths/tracehub-mcp exposes 19 tools: search_traces, get_trace, triage_trace, correlate_trace, get_llm_usage, and 14 more. Their descriptions and schemas cost roughly 3,610 tokens of context every time the server is loaded.
Is the io.github.mcpsmiths/tracehub-mcp server still maintained?
io.github.mcpsmiths/tracehub-mcp is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.mcpsmiths/tracehub-mcp server under?
io.github.mcpsmiths/tracehub-mcp declares the Apache-2.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.