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LangSmith MCP Server

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by langchain-ai

read_example

Retrieve specific examples from LangSmith datasets using example IDs and optional version timestamps for data analysis and model evaluation.

Instructions

Read a specific example from LangSmith.

Args: example_id (str): Example ID to retrieve as_of (Optional[str]): Dataset version tag OR ISO timestamp to retrieve the example as of that version/time ctx: FastMCP context (automatically provided)

Returns: Dict[str, Any]: Dictionary containing the example details, or an error message if the example cannot be retrieved

Example in case you need to create a separate python script to read an example: ```python from langsmith import Client

client = Client()
example = client.read_example(example_id="example-id-here")
# Or with version:
# example = client.read_example(example_id="example-id-here", as_of="v1.0")
```

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
example_idYes
as_ofNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns a dictionary or error message, which adds some context beyond the input schema. However, it lacks details on permissions, rate limits, error types, or what 'example details' include. For a read operation with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, but includes extensive example code that may be redundant for an AI agent. The 'Args' and 'Returns' sections are structured but verbose. Some sentences (like the script example) don't earn their place for tool selection, reducing efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (true), the description doesn't need to detail return values. It covers input parameters well despite 0% schema coverage. With no annotations, it could improve by adding behavioral context like error handling or permissions, but it's largely complete for a read operation with structured output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate fully. It explicitly documents both parameters: 'example_id' as 'Example ID to retrieve' and 'as_of' as 'Dataset version tag OR ISO timestamp to retrieve the example as of that version/time'. This adds crucial meaning beyond the bare schema, clarifying data types and purposes effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Read' and resource 'a specific example from LangSmith', making the purpose unambiguous. It distinguishes from siblings like 'list_examples' (which lists multiple) and 'read_dataset' (which reads datasets rather than examples). However, it doesn't explicitly contrast with 'update_examples' or other siblings beyond the inherent 'read' vs 'write' distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when you need to retrieve a specific example by ID, possibly with versioning via 'as_of'. It doesn't provide explicit when-not-to-use guidance or name alternatives like 'list_examples' for browsing. The example code suggests typical use cases but doesn't articulate contextual boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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