A
licenseNot graded
qualityC
maintenanceEnables AI agents to access observability and evaluation data, including run history, span traces, LLM-as-judge evaluation results, and regression reports.
MIT
LangSmith MCP Server
Officialby langchain-ai
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- FlicenseNot gradedqualityAmaintenanceEnables AI agents and developers to query trace, session, exception, routing, prompt, dataset, score, and metrics data for debugging, triage, and observability, with selective tool loading and a read-only mode.-
- FlicenseAqualityDmaintenanceEnables read-only querying of LangSmith traces including runs, children, and URLs without extra instrumentation.51-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and coding assistants to query trace, session, exception, routing, prompt, dataset, score, and metric data from a Langfuse instance for debugging and observability. Offers selective tool-group loading and a read-only mode to control token overhead and write access.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and developers to query traces, sessions, and exceptions for debugging and observability, alongside tools for prompts, datasets, annotation queues, scores, and metrics. Supports selective tool loading and a read-only mode for safer local access from any MCP client.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP clients to debug and observe LLM applications by querying traces, sessions, exceptions, prompts, datasets, scores, and metrics. Supports local stdio/HTTP access, selective tool loading, and read-only operation for safer observability workflows.-
TDQS
A3.5/5.0
Scored across 13 tools
Disambiguation4/5
Most tools have distinct purposes targeting different LangSmith resources like datasets, examples, prompts, runs, projects, and experiments, with clear boundaries. However, 'create_dataset' and 'push_prompt' are described as informational tools ('call this tool when you need to understand how to...'), which could confuse agents about their actual functionality versus other tools that perform operations directly.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, such as 'create_dataset', 'fetch_runs', 'list_datasets', 'read_example', and 'update_examples'. All tools use snake_case with clear, descriptive names, making the set predictable and easy to navigate.
Tool Count5/5
With 13 tools, the server is well-scoped for managing LangSmith resources, covering datasets, examples, prompts, runs, projects, and experiments. Each tool serves a specific function without redundancy, and the count aligns with the complexity of the domain, providing comprehensive coverage without being overwhelming.
Completeness4/5
The tool set offers strong coverage for core LangSmith operations, including CRUD-like actions for datasets, examples, prompts, runs, projects, and experiments. Minor gaps exist, such as no explicit 'delete' tools for resources like datasets or prompts, but agents can likely work around this given the overall robust surface for analytics and management tasks.
Maintenance
ActivityInactive
ResponsivenessUnresponsive