LangSmith MCP Server

Official

by langchain-ai

Overview Schema Related Servers Score Discussions(1)

Python

Hybrid

Server Quality Checklist

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Latest release: v0.0.10
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.
Average 3.6/5 across 13 of 13 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 4 community issues answered or closed in the last 6 months
- 20 commits in the last 12 weeks
- Last stable release on February 25, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'understand how to create' which suggests this might be an informational/read-only tool rather than a mutating creation tool, but this is unclear. It doesn't disclose whether this actually creates datasets, what permissions are needed, what happens on invocation, or any behavioral traits like side effects or response format.
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 a single sentence that's reasonably concise, but it's not front-loaded with clear purpose. The phrasing 'understand how to create' adds unnecessary ambiguity rather than directly stating what the tool does. It could be more efficiently structured to clarify intent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists, the description doesn't need to explain inputs or return values. However, for a tool named 'create_dataset' among siblings like 'list_datasets' and 'read_dataset', the description is incomplete—it fails to clarify whether this tool actually creates datasets or just provides instructions, leaving significant ambiguity about its function in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter information, but that's appropriate here. Baseline is 4 for zero-parameter tools as the schema fully covers the absence of inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'create datasets in LangSmith' which provides a basic purpose, but it's vague about what 'create' entails and doesn't distinguish this tool from sibling tools like 'list_datasets' or 'read_dataset'. It essentially restates the tool name without adding specificity about what dataset creation involves.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Call this tool when you need to understand how to create datasets' which implies usage for learning/understanding rather than actual creation, but this is ambiguous. It provides no guidance on when to use this vs alternatives like 'list_datasets' or 'read_dataset', nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It vaguely suggests the tool helps 'understand' something, which doesn't clarify if it's a read-only operation, performs mutations, requires authentication, or has side effects. This leaves significant gaps in transparency for a tool that might involve creating or pushing prompts.
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 a single sentence that is somewhat front-loaded but inefficiently worded; it could be more direct (e.g., 'Provides guidance on creating and pushing prompts to LangSmith'). While not overly verbose, it doesn't maximize clarity or structure for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's potential complexity (involving prompt creation/pushing) and the presence of an output schema, the description is incomplete. It fails to explain what the tool actually returns or does operationally, relying too much on the output schema without providing enough context for an agent to understand its role among siblings or its behavioral impact.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it adequately handles the lack of parameters without introducing confusion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as helping 'understand how to create and push prompts to LangSmith,' which is vague and instructional rather than specifying what the tool itself does. It doesn't clearly state a specific action the tool performs (e.g., 'creates and pushes a prompt'), making it tautological to the name 'push_prompt' without concrete differentiation from siblings like 'list_prompts' or 'get_prompt_by_name'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance by saying 'Call this tool when you need to understand how to create and push prompts to LangSmith,' which implies usage for learning purposes but doesn't specify when to use it versus alternatives like 'list_prompts' for viewing prompts or 'create_dataset' for related tasks. There's no explicit when/when-not advice or clear context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It implies an informational or tutorial role ('understand how to update'), but doesn't clarify if this is a read-only operation, requires permissions, or has side effects. The ambiguity fails to compensate for the lack of annotations, leaving key behavioral traits undisclosed.
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 a single sentence that is concise but under-specified, as it fails to clearly state the tool's function. While efficient, it lacks front-loaded clarity, making it less helpful for quick comprehension. It could be more structured to directly convey purpose without ambiguity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (implied by its vague purpose) and the presence of an output schema, the description is incomplete. It doesn't explain what the tool returns or how it aids in 'understanding,' leaving gaps despite the output schema. For a tool with no annotations and unclear behavior, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately avoids discussing parameters, aligning with the schema's completeness. A baseline of 4 is applied since no parameters exist, and the description doesn't add unnecessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool is for 'understanding how to update dataset examples in LangSmith,' which is vague about the actual action performed. It suggests a meta-purpose (learning how to update) rather than executing an update operation, creating ambiguity. This differs from clear sibling tools like 'create_dataset' or 'read_example' that specify direct actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance by stating 'Call this tool when you need to understand how to update dataset examples,' but it lacks explicit when-to-use vs. alternatives, prerequisites, or comparisons to siblings like 'list_examples' or 'read_example.' This leaves the agent with insufficient context for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 only mentions understanding how to run experiments, but doesn't reveal whether this tool actually executes experiments, provides documentation, returns configuration templates, or has any side effects. Critical behavioral traits like mutability, authentication needs, or rate limits are completely unspecified.
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 a single sentence that's reasonably concise, but it's not optimally structured. It could be more front-loaded with the tool's actual function rather than framing it as 'understanding how to run experiments'. The sentence earns its place but could be more direct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description doesn't need to explain return values. However, for a tool in a complex ecosystem with many sibling tools, the description is insufficiently complete - it doesn't clarify what the tool actually produces or how it differs from related tools despite the structured data being adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to compensate for any parameter gaps. While it doesn't add parameter-specific information (since there are none), this is appropriate for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'run experiments and evaluations in LangSmith' which gives a general domain but lacks a specific verb+resource combination. It doesn't clearly distinguish what this tool actually does versus siblings like 'list_experiments' or 'fetch_runs'. The purpose is vague rather than tautological, but insufficiently specific for tool selection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Call this tool when you need to understand how to run experiments...' which provides minimal context about when to use it, but offers no guidance on when NOT to use it or what alternatives exist among the sibling tools. There's no comparison to similar tools or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it 'fetches' with filtering. It lacks critical behavioral details such as authentication requirements, rate limits, pagination behavior (beyond the 'limit' parameter), error handling, or whether it's read-only (implied but not explicit). This is inadequate for a tool with potential complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear opening sentence followed by well-organized 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 (returns a dictionary with prompts and metadata), the description doesn't need to detail return values. However, with no annotations and only basic parameter info, it misses behavioral context like auth or pagination. For a simple fetch tool, it's minimally adequate but leaves gaps in operational guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% description coverage. It explains that 'is_public' filters by prompt visibility with specific string values ('true'/'false') and default, and 'limit' sets the maximum number of prompts with its default. This compensates well for the schema's lack of descriptions.
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 ('Fetch') and resource ('prompts from LangSmith') with optional filtering, making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_prompt_by_name' or 'push_prompt', which would require more specific scope definition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_prompt_by_name' for specific prompts or 'push_prompt' for creating prompts. The description mentions filtering but doesn't clarify use cases or prerequisites, leaving the agent without contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool reads a dataset and returns details or an error, but lacks behavioral traits like authentication needs, rate limits, or whether it's idempotent. The example adds some context but doesn't fully compensate for missing annotations.
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 an extensive example that may be redundant for an AI agent. The structure is somewhat cluttered with notes and code, reducing efficiency. Every sentence doesn't fully earn its place, as the example could be trimmed or omitted.
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's moderate complexity (2 parameters, no annotations, but has an output schema), the description is fairly complete. It covers the purpose, parameter usage, and return behavior. The output schema exists, so explaining return values isn't needed, but it could benefit from more behavioral context (e.g., error handling details).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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. It clearly explains the semantics of both parameters: 'dataset_id' and 'dataset_name' are for identifying the dataset, with precedence rules. This adds significant value beyond the bare schema, though it doesn't detail format constraints (e.g., string patterns).
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 tool's purpose: 'Read a specific dataset from LangSmith.' It uses a specific verb ('Read') and resource ('dataset'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'list_datasets' or 'read_example', which would require a 5.
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 provides implied usage guidance by noting that either 'dataset_id' or 'dataset_name' must be provided, with 'dataset_id' taking precedence if both are given. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to 'list_datasets' for browsing datasets), and no exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that it's a read operation ('Fetch'), mentions default behavior, and describes the return format. However, it doesn't address important behavioral aspects like pagination (beyond the limit parameter), rate limits, authentication requirements, or error conditions beyond the generic 'error message' mention.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose statement, note, args, returns) and uses bullet-like formatting for parameters. While comprehensive, it could be slightly more concise by integrating the note about default behavior into the main purpose statement rather than as a separate line.
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's moderate complexity (6 parameters, no annotations, but has output schema), the description is reasonably complete. It covers purpose, usage note, all parameters with semantics, and return format. The output schema existence means the description doesn't need to detail return values, but it still provides a high-level overview ('Dictionary containing the datasets and metadata'), making it adequately comprehensive.
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?
With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all 6 parameters. Each parameter is clearly explained with examples (e.g., 'JSON array string', 'e.g., "chat", "kv"', 'Filter by exact dataset name'), format requirements, and the limit's default value, adding substantial value beyond the bare schema.
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 tool's purpose as 'Fetch LangSmith datasets' with a specific verb ('Fetch') and resource ('LangSmith datasets'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_experiments' or 'list_projects' that likely have similar list/fetch patterns for different resource types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about default behavior ('If no arguments are provided, all datasets will be returned'), which helps guide usage. However, it doesn't explicitly mention when to use this tool versus alternatives like 'read_dataset' (which likely fetches a single dataset) or 'create_dataset', leaving some sibling differentiation incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool retrieves prompt details and templates, and may return an error if the prompt is not found. However, it lacks details on authentication needs, rate limits, or whether the operation is idempotent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by structured sections for Args and Returns. Every sentence adds value: the first states the action, the Args clarify the parameter, and the Returns explain the output and error handling.
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's simplicity (1 parameter, no annotations, but has an output schema), the description is mostly complete. It explains the purpose, parameter, and return behavior. However, it could improve by addressing authentication or error specifics, though the output schema may cover return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description compensates by explaining that 'prompt_name' must be the exact name of the prompt to retrieve. It adds meaningful context beyond the bare schema, though it does not specify format constraints (e.g., case sensitivity).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a specific prompt') and resource ('by its exact name'), distinguishing it from sibling tools like 'list_prompts' (which lists all prompts) and 'push_prompt' (which creates/updates prompts). The verb 'retrieve' reinforces the read-only nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (to retrieve a specific prompt by exact name) versus alternatives like 'list_prompts' (for browsing all prompts). However, it does not explicitly state when NOT to use it or mention other potential alternatives like 'push_prompt' for creating prompts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool fetches with filtering and returns a dictionary or error, but lacks details on permissions, rate limits, side effects, or pagination behavior. The description adds some context (e.g., precedence rules, default behaviors) but doesn't fully disclose behavioral traits for a complex tool with 11 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a purpose statement, usage note, parameter details, and return info. It's appropriately sized for a complex tool, but the parameter list is lengthy (though necessary). Every sentence adds value, and it's front-loaded with key information.
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's complexity (11 parameters, no annotations, 0% schema coverage), the description is quite complete. It covers purpose, usage rules, parameter semantics, and return values. An output schema exists, so return details aren't needed. The main gap is lack of behavioral context like permissions or side effects, preventing a perfect score.
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. It provides detailed semantics for all 11 parameters, including data types, defaults, formats (e.g., JSON strings), and examples. This goes well beyond what the bare schema offers, making parameter usage clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Fetch examples from a LangSmith dataset with advanced filtering options.' It specifies the verb ('fetch'), resource ('examples'), and scope ('LangSmith dataset'), and distinguishes it from siblings like 'read_example' (singular) and 'list_datasets' (different resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Note: Either dataset_id, dataset_name, or example_ids must be provided.' It also clarifies precedence rules for multiple inputs. However, it doesn't explicitly contrast with alternatives like 'read_example' or 'update_examples', which would be needed for a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does an excellent job disclosing behavioral traits. It explains the tool 'wraps client.list_runs()' with complete support for various filtering methods, describes return formats (raw dict objects), mentions output structure changes based on format_type parameter, and provides practical notes about verifying project_name and handling large traces. The only minor gap is lack of explicit rate limit or authentication requirements.
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?
While well-structured with clear sections (PURPOSE, PARAMETERS, RETURNS, EXAMPLES, NOTES), the description is excessively long with redundant information. Some examples could be condensed, and the FQL syntax details might be overly verbose for a tool description. However, the front-loaded summary is effective, and the structure helps navigation despite the length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (12 parameters, no annotations, 0% schema coverage, but has output schema), the description is remarkably complete. It covers purpose, usage, all parameters with semantics, return formats, extensive examples, and agent-specific notes. The output schema existence means the description doesn't need to detail return structures, but it still explains the conditional returns based on format_type parameter.
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?
With 0% schema description coverage and 12 parameters, the description compensates fully by providing extensive parameter documentation. Each parameter gets clear explanations with examples, especially for complex ones like filter (with FQL syntax details), trace_filter, tree_filter, and format_type. The description adds significant value beyond the bare schema by explaining parameter interactions and practical usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'fetches LangSmith runs' with specific resources (traces, tools, chains, etc.) and methods (flexible filters, query language). It explicitly distinguishes this as a 'general-purpose LangSmith run fetcher' for analytics and trace export, differentiating it from sibling tools like list_datasets or list_projects that handle different resource types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives. It states it's for 'analytics, trace export, and automated exploration' and specifically advises 'For conversational AI agents: Use format_type="pretty" to get human-readable message exchanges.' It also distinguishes from sibling tools by focusing on runs rather than datasets, prompts, or experiments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and delivers comprehensive behavioral disclosure. It explains what the tool does (filters experiments by dataset, supports partial name matching, extracts deployment IDs), what it returns (simplified project information with key metrics), and operational constraints (mandatory dataset filtering, either/or parameter logic, default limit of 5). No contradictions exist since annotations are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (PURPOSE, PARAMETERS, RETURNS, EXAMPLES, NOTES) but could be more concise. Some information is repeated across sections (e.g., mandatory dataset filtering appears multiple times). However, every sentence adds value, and the structure helps agents quickly find relevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, 0% schema coverage) and presence of an output schema, the description provides complete context. It explains the tool's purpose, usage guidelines, parameter semantics, return format (with detailed example), and behavioral characteristics. The output schema existence means the description doesn't need to exhaustively document return values, but it still provides helpful context about what metrics are included.
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?
With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. Each parameter gets clear explanations of purpose, constraints (e.g., 'either this OR reference_dataset_name must be provided'), defaults ('limit: int, default 5'), and examples. The description adds significant value beyond the bare schema, explaining the either/or relationship between dataset parameters and partial matching behavior for project_name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List LangSmith experiment projects (reference projects) with mandatory dataset filtering.' It specifies the verb ('List'), resource ('LangSmith experiment projects'), and scope ('with mandatory dataset filtering'). It distinguishes from siblings like 'list_projects' by focusing specifically on experiment projects used for model evaluation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Requires either a dataset ID or dataset name to filter experiments.' It distinguishes from alternatives by noting experiments are 'reference projects associated with datasets' and 'used for model evaluation and comparison.' The 'NOTES FOR AGENTS' section reinforces usage rules like mandatory dataset filtering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does this excellently by explaining: default filtering behavior ('Projects are filtered to exclude reference projects by default'), how filtering works ('uses `name_contains` for filtering, so partial matches work'), and what happens with nested data ('The `agent_deployment_id` field is automatically extracted from nested project data when available').
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (PURPOSE, PARAMETERS, RETURNS, EXAMPLES, NOTES) but is quite lengthy. While every section adds value, some redundancy exists (e.g., the initial summary repeats what's in the PURPOSE section). It could be more concise while maintaining clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, filtering logic, detail levels) and the presence of an output schema, the description is exceptionally complete. It explains the tool's behavior thoroughly, documents all parameters, provides return format examples for both detail levels, includes practical examples, and offers agent guidance. The output schema existence means the description doesn't need to exhaustively document return structures.
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?
With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. Each of the 5 parameters is clearly explained with purpose, default values, examples, and usage constraints (e.g., 'Either this OR `reference_dataset_name` must be provided but not both'). The description adds significant value beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List LangSmith projects with optional filtering and detail level control.' It specifies the exact resource (LangSmith projects) and actions (list, filter, control detail). It distinguishes itself from siblings like list_datasets or list_experiments by focusing specifically on projects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool vs alternatives. The 'NOTES FOR AGENTS' section advises 'Use `more_info="false"` for quick project discovery and listing' and 'Use `more_info="true"` when you need detailed project information.' It also mentions sibling tools implicitly by specifying this is for projects, not datasets or other resources.
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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