Langfuse MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes targeting specific resources (e.g., get_comment vs. get_metrics), but some overlap exists between get_observations and get_trace_detail (since traces include observations) and between get_daily_metrics and project_overview (both provide aggregated metrics). Descriptions help clarify, but agents might occasionally misselect between these related tools.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, with 'get_' for retrieving specific items, 'list_' for listing collections, and descriptive nouns (e.g., get_trace_detail, list_datasets). There are no deviations in naming conventions, making the set predictable and readable.
Tool Count3/5
With 24 tools, the count is borderline high for a single server, potentially overwhelming for agents. While the Langfuse domain is broad (covering traces, metrics, datasets, etc.), the tool set feels heavy and could benefit from consolidation or better scoping to reduce cognitive load.
Completeness5/5
The tool surface provides comprehensive coverage for the Langfuse domain, including CRUD-like operations (get/list for traces, observations, datasets, prompts, models, comments), detailed metrics (cost, usage, health), and specialized analyses (top_expensive_traces, usage_by_model). No obvious gaps exist; agents can perform full lifecycle and analytical workflows.
Average 3/5 across 24 of 24 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
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```
{
  "$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?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It mentions 'daily' trends but doesn't specify time granularity, data format, pagination, rate limits, authentication requirements, or whether the operation is read-only. The description doesn't contradict annotations (none exist), but it fails to provide essential behavioral context for a tool that presumably returns time-series data.
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 extremely concise at just five words, which is efficient for a simple concept. However, it's arguably too brief given the tool's complexity (4 parameters, no annotations, no output schema). While there's no wasted language, the description may be under-specified rather than optimally concise.
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?
For a tool with 4 parameters, no annotations, no output schema, and multiple similar sibling tools, the description is insufficiently complete. It doesn't explain what metrics are returned, in what format, with what time granularity, or how it differs from related tools. The agent would need to guess about the tool's behavior and appropriate use cases based on this minimal description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what's already documented in the schema (which has 100% coverage). While the description implies date-range functionality through 'daily trends,' it doesn't explain parameter relationships, default behaviors, or provide examples. With complete schema documentation, the baseline score of 3 is appropriate since the schema already provides adequate parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Daily usage trends and patterns' indicates the tool provides trend data but is vague about what specific metrics it returns. It mentions 'usage' but doesn't specify whether this refers to API calls, compute resources, user activity, or other metrics. It doesn't clearly distinguish from siblings like 'get_metrics', 'usage_by_model', or 'usage_by_service' which also appear to provide usage-related data.
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 no guidance on when to use this tool versus alternatives. With multiple sibling tools that appear related to metrics and usage (get_metrics, usage_by_model, usage_by_service, top_expensive_traces), there's no indication of what makes this tool unique or when it should be preferred over those alternatives. The description offers no context about appropriate use cases 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 mentions 'flexible filtering options' but doesn't describe key behaviors: whether this is a read-only operation, if it requires authentication, rate limits, pagination (beyond the 'limit' parameter), or what the return format looks like. For a tool with 11 parameters and no output schema, this is a significant gap in transparency.
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 a single, efficient sentence that gets straight to the point without fluff. It's appropriately sized for a tool with many parameters, though it could be more front-loaded with critical context. There's no wasted verbiage, earning it a high score for conciseness.
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 (11 parameters, no annotations, no output schema, and many sibling tools), the description is incomplete. It doesn't explain what 'traces' are, how results are returned, or when to use it versus alternatives. For a data-fetching tool with rich filtering, more context is needed to guide effective use, especially without annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal value beyond the input schema, which has 100% coverage with detailed parameter descriptions. It implies filtering capabilities ('flexible filtering options') but doesn't explain semantics like how 'tags' filtering works (e.g., AND/OR logic) or the relationship between parameters. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Fetch traces with flexible filtering options' which identifies the verb ('fetch') and resource ('traces'), but it's vague about what 'traces' are and doesn't differentiate from sibling tools like 'get_trace_detail', 'top_expensive_traces', or 'get_observations'. It provides a basic purpose but lacks specificity.
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 offers no guidance on when to use this tool versus alternatives. With many sibling tools like 'get_trace_detail' (for single traces), 'top_expensive_traces' (for cost-focused queries), or 'get_observations' (potentially related), there's no indication of context, prerequisites, or exclusions. It merely states the tool's function without usage context.
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 states the tool retrieves detailed information but doesn't describe what 'detailed information' includes, whether it's a read-only operation, potential error conditions, or any performance or permission aspects. This leaves significant gaps in understanding the tool's behavior.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action, making it easy to parse quickly, and there's no wasted verbiage.
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 lack of annotations and output schema, the description is incomplete for a retrieval tool. It doesn't explain what 'detailed information' entails, such as the structure or fields returned, which is crucial for an agent to understand the tool's output. This gap makes it inadequate for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'commentId' parameter well-documented as 'The unique identifier of the comment to retrieve'. The description adds no additional semantic context beyond this, such as format examples or constraints, so it meets the baseline for high schema coverage without extra value.
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 ('Get') and resource ('detailed information about a specific comment'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_comments', which retrieves multiple comments rather than a specific one, leaving some room for improvement in sibling distinction.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_comments' for listing multiple comments or other retrieval tools, nor does it specify prerequisites or contexts for usage, leaving the agent without clear selection 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?
No annotations are provided, so the description carries full burden. While 'specialized cost breakdowns' implies a read-only analytical function, it doesn't disclose important behavioral aspects like whether this requires specific permissions, how results are formatted, whether there are rate limits, or what happens with large date ranges. The description is too minimal for a tool with 7 parameters.
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 extremely concise - a single sentence that efficiently communicates the core purpose. Every word earns its place, and there's no wasted verbiage or redundancy.
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?
For a tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what format the 'breakdowns' take, how multiple breakdown types interact, what the 'environment' filter means, or what the tool returns. The minimal description leaves too many open questions for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description mentions 'by model, user, and daily trends' which aligns with the includeModelBreakdown, includeUserBreakdown, and includeDailyBreakdown parameters, but adds no additional semantic context beyond what's already in the schema 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 tool provides 'specialized cost breakdowns' with specific dimensions (by model, user, and daily trends), which is a specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like 'usage_by_model' or 'top_expensive_traces', which appear to be related cost/usage tools.
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 no guidance on when to use this tool versus alternatives. With multiple sibling tools that appear related to usage/cost metrics (usage_by_model, usage_by_service, top_expensive_traces, get_daily_metrics), there's no indication of when this specialized breakdown tool is preferred over those alternatives.
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 implies a read operation ('Get detailed information'), but doesn't specify whether it requires authentication, has rate limits, returns structured data, or handles errors. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 a single, efficient sentence: 'Get detailed information about a specific dataset by name.' It is front-loaded with the core purpose, has no wasted words, and is appropriately sized for a simple tool. Every part of the sentence earns its place.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, the return format, or any behavioral traits like error handling. For a tool with no structured support, the description should provide more context to be fully helpful to an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'datasetName' fully documented in the input schema. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.
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: 'Get detailed information about a specific dataset by name.' It specifies the verb ('Get'), resource ('dataset'), and scope ('by name'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'list_datasets' or 'get_dataset_item', 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_datasets' for listing all datasets or 'get_dataset_item' for dataset items, nor does it specify prerequisites or exclusions. This leaves the agent without context for 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), implying it's non-destructive, but doesn't mention any behavioral traits like error handling, permissions required, rate limits, or what 'detailed information' includes. For a tool with zero annotation coverage, this is inadequate.
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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and scope.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' includes (e.g., structure, fields), error cases, or how this tool differs from siblings. For a tool with no structured behavioral or output data, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'itemId' fully documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain what an 'itemId' is or where to find it). Baseline 3 is appropriate when the schema does the heavy lifting.
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 with a specific verb ('Get') and resource ('dataset item'), and specifies the scope ('detailed information about a specific dataset item'). However, it doesn't explicitly differentiate from sibling tools like 'get_dataset' or 'list_dataset_items', 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_dataset' (for dataset-level info) or 'list_dataset_items' (for listing items). It lacks any context about prerequisites, timing, or exclusions, leaving the agent to infer usage from the name alone.
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 states the tool retrieves detailed information, implying a read-only operation, but does not mention any behavioral traits such as authentication requirements, rate limits, error handling, or what 'detailed information' entails. This leaves significant gaps in understanding how the tool behaves.
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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick understanding.
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 lack of annotations and output schema, the description is incomplete. It does not explain what 'detailed information' includes, how the data is returned, or any prerequisites for use. For a tool that likely returns complex data, this leaves the agent without sufficient context to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'observationId' clearly documented. The description adds no additional semantic information beyond what the schema provides, such as format examples or constraints. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here.
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 ('Get') and resource ('detailed information about a specific observation by ID'), making the purpose unambiguous. However, it does not differentiate this tool from potential siblings like 'get_observations' or 'get_trace_detail', which might retrieve similar types of data, so it falls short of a perfect score.
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 no guidance on when to use this tool versus alternatives. For example, it does not specify if this is for a single observation versus a list from 'get_observations', or if it should be used over other detail tools like 'get_model_detail'. This lack of context leaves the agent without clear usage instructions.
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 the full burden of behavioral disclosure but only states the basic action without details on permissions, rate limits, error handling, or response format. It doesn't address whether this is a read-only operation or if it has side effects, leaving significant gaps for an agent to understand how to use it safely.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured 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 complexity of a retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'detailed information' includes, how results are formatted, or any behavioral traits like pagination or error cases, leaving the agent with incomplete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema fully documents the three parameters (promptName, version, label). The description adds no additional meaning beyond what's in the schema, such as examples or constraints, resulting in the baseline score of 3 for adequate but minimal value addition.
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 ('Get') and resource ('detailed information about a specific prompt template'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'list_prompts' or 'get_model_detail', which would require explicit comparison to achieve a score of 5.
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 no guidance on when to use this tool versus alternatives such as 'list_prompts' for a broader view or 'get_model_detail' for related resources. It lacks any context about prerequisites, exclusions, or typical scenarios for application.
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 the full burden of behavioral disclosure. It states it 'gets' information, implying a read-only operation, but doesn't clarify if it requires authentication, has rate limits, returns paginated results, or what format the 'detailed information' includes. This leaves significant behavioral gaps for a tool with no annotation coverage.
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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, with every word contributing to understanding the purpose.
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 no annotations and no output schema, the description is incomplete for a tool that retrieves 'detailed information'. It doesn't explain what 'detailed information' entails, how observations are included, or the response structure. For a read operation with rich expected output, this lack of detail is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'traceId' documented as 'The trace ID to retrieve'. The description adds no additional meaning beyond this, such as format examples or sourcing details. Since schema coverage is high, the baseline score of 3 is appropriate.
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 ('Get') and resource ('detailed information about a specific trace including all observations'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_traces' (which likely lists traces) or 'get_observation_detail' (which focuses on individual observations), missing full sibling differentiation.
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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'get_traces' for a list or 'get_observation_detail' for observation-specific details, nor does it specify prerequisites or exclusions for usage.
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 the full burden of behavioral disclosure. It mentions filtering but doesn't describe key behaviors like pagination handling, rate limits, authentication needs, or what happens with invalid filters. For a read operation with 5 parameters, this is insufficient to guide safe and effective use.
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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes essential context about filtering. Every word earns its place, making it highly concise and well-structured.
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 complexity of 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks behavioral details (e.g., pagination, error handling), usage context, and output expectations. For a list tool with filtering, this leaves significant gaps for an AI agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds marginal value by hinting at filtering for 'objects and users', which loosely maps to 'objectType', 'objectId', and 'authorUserId', but doesn't provide additional syntax or usage details beyond what the schema already specifies.
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 'List' and resource 'comments', specifying the action and target. It adds value by mentioning 'filtering options for objects and users', which distinguishes it from a simple unfiltered list. However, it doesn't explicitly differentiate from sibling tools like 'get_comment' or other list tools, keeping it from a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_comment' for single comments or other list tools for different resources, nor does it specify prerequisites or exclusions. This leaves the agent without context for 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'filtering and pagination', which hints at read-only list behavior, but fails to describe key traits: whether this is a safe read operation (implied but not stated), what the return format looks like (e.g., array of items with metadata), default behaviors (e.g., sorting order), or any rate limits. For a list tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 a single, efficient sentence that front-loads the core purpose ('List items in datasets') and adds key features ('with filtering and pagination'). There's no wasted verbiage or redundancy. However, it could be slightly more structured by explicitly separating purpose from capabilities (e.g., with a colon or bullet points), but this is minor.
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 (5 parameters, list operation with filtering/pagination), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'items' are (e.g., data records, observations), how results are structured, pagination details (e.g., total count, next page tokens), or error conditions. For a list tool with multiple filters and no structured output, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters (datasetName, sourceTraceId, sourceObservationId, page, limit). The description adds no parameter-specific details beyond what's in the schema—it only generically mentions 'filtering and pagination', which the schema already covers with individual parameter descriptions. This meets the baseline of 3 for high schema coverage without additional value from the description.
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 'List' and resource 'items in datasets', which is specific and actionable. It distinguishes from siblings like 'get_dataset' (single dataset) and 'list_datasets' (datasets themselves) by focusing on items within datasets. However, it doesn't explicitly differentiate from 'get_dataset_item' (single item) or 'get_observations' (related but 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'filtering and pagination' but provides no explicit guidance on when to use this tool versus alternatives. For example, it doesn't specify when to prefer 'list_dataset_items' over 'get_observations' (which might overlap in data) or 'get_dataset_item' (for single items). There's no mention of prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from context alone.
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 states the action ('List all prompt templates') but lacks details on permissions, rate limits, pagination behavior (beyond what the schema implies), or response format. This is insufficient for a mutation-free tool with zero annotation coverage.
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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't address key aspects like what the returned data looks like, error conditions, or usage constraints. For a tool with three parameters and no structured output information, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the three parameters (limit, page, name). The description adds no additional meaning beyond what the schema provides, such as explaining filter logic or default behaviors, meeting the baseline score for high schema coverage.
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 ('List') and resource ('all prompt templates in the Langfuse project'), making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_prompt_detail' or 'list_models', which would be needed for a perfect score.
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 no guidance on when to use this tool versus alternatives. For example, it doesn't mention when to choose 'list_prompts' over 'get_prompt_detail' or other list tools like 'list_datasets', leaving the agent without context for 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a summary but doesn't cover critical aspects like whether it's read-only (implied by 'Get'), potential rate limits, authentication needs, error handling, or the format of the returned data. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 a single, efficient sentence that front-loads the core purpose ('Get a summary') and specifies key metrics (cost, tokens, traces) and scope (project, time window). There is no wasted verbiage or redundancy, making it highly concise and well-structured 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 complexity of summarizing project data with 3 parameters and no output schema or annotations, the description is incomplete. It doesn't explain the return format, potential limitations (e.g., data availability), or how it differs from sibling tools. For a tool without structured output information, more detail is needed to ensure the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions a 'time window', which aligns with the 'from' and 'to' parameters in the schema, but adds no additional meaning beyond what the schema provides (e.g., it doesn't explain the 'environment' parameter or provide examples like 'production'). With 100% schema description coverage, the baseline is 3, as the schema already documents parameters adequately without extra insights from the description.
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 with a specific verb ('Get') and resource ('summary of total cost, tokens, and traces for a project'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_cost_analysis' or 'get_daily_metrics', which might also provide cost or metric summaries, leaving some ambiguity about uniqueness.
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 no guidance on when to use this tool versus alternatives. It mentions a 'time window' but doesn't specify scenarios, prerequisites, or exclusions, such as whether it's for high-level overviews or detailed breakdowns compared to siblings like 'get_metrics' or 'usage_by_model'. This lack of context makes it harder for an agent to choose appropriately.
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 full burden. It mentions 'find' and 'most expensive traces by cost', implying a read-only query, but lacks details on permissions, rate limits, pagination, or output format. For a tool with no annotations and four parameters, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or fluff, making it easy to parse quickly while conveying essential information.
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 no annotations, no output schema, and a tool with four parameters that queries trace data, the description is insufficient. It doesn't explain what 'expensive' means (e.g., monetary cost, latency), how results are ordered, or what the output contains. For a ranking/query tool in this context, more completeness is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all four parameters. The description adds minimal value beyond the schema by implying time-based filtering and cost ranking, but doesn't explain parameter interactions or default behaviors beyond the schema's 'limit' default. Baseline 3 is appropriate given high schema coverage.
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 'find' and resource 'most expensive traces by cost', specifying the ranking criterion. It distinguishes from siblings like 'get_traces' by focusing on cost ranking, though it doesn't explicitly name alternatives. The purpose is specific and actionable.
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 no guidance on when to use this tool versus alternatives like 'get_traces' or 'usage_by_model'. It mentions a time period requirement but offers no context about prerequisites, exclusions, or comparative use cases with sibling tools. Usage is implied only by the description's scope.
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 states the tool breaks down usage and cost but doesn't describe what 'break down' entails (e.g., aggregated metrics, detailed logs, cost calculations), whether it requires specific permissions, rate limits, or what the output format looks like. For a tool with no annotations and no output schema, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates what the tool does without redundancy or fluff, making it easy to parse quickly.
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 (analyzing usage and cost data), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'break down' means in terms of output structure, data granularity, or behavioral traits like error handling. For a tool with four parameters and no structured output information, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all four parameters. The description adds minimal value beyond the schema, only implying that parameters define a 'time period' and optional filtering. It doesn't provide additional context like how 'environment' relates to models or what 'limit' affects in practice. With high schema coverage, the baseline score of 3 is appropriate.
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: 'Break down usage and cost by AI model over a time period.' It specifies the verb ('break down'), resource ('usage and cost'), and scope ('by AI model over a time period'). However, it doesn't explicitly differentiate from its sibling 'usage_by_service', which appears to be a similar breakdown tool but by service instead of model.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'usage_by_service' or 'get_cost_analysis', nor does it specify prerequisites, exclusions, or optimal use cases. The only implied context is needing usage and cost data by model over time.
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 the full burden of behavioral disclosure. While it indicates this is an analysis/read operation (implied by 'analyze'), it doesn't disclose important behavioral aspects like whether this requires specific permissions, what format the analysis returns, whether there are rate limits, or how the 'limit' parameter affects results. The description is too minimal for a tool with 5 parameters and no output schema.
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 extremely concise - a single sentence that efficiently communicates the core purpose. Every word earns its place with no redundancy or unnecessary elaboration. The structure is front-loaded with the main action and scope clearly stated upfront.
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?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the analysis returns, how results are structured, what 'usage and cost' metrics are included, or how the tool behaves with different parameter combinations. The minimal description leaves too many questions unanswered for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, providing clear documentation for all 5 parameters. The description adds minimal value beyond the schema - it mentions 'by service/feature tag' which relates to the 'serviceTagKey' parameter, and 'over a time period' which relates to 'from' and 'to' parameters. However, it doesn't provide additional context about parameter interactions or usage patterns beyond what's already in the schema 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 tool's purpose: 'Analyze usage and cost by service/feature tag over a time period.' It specifies the verb ('analyze'), resource ('usage and cost'), and scope ('by service/feature tag over a time period'). However, it doesn't explicitly distinguish this from sibling tools like 'usage_by_model' or 'get_cost_analysis', which would require more specific differentiation.
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 no guidance on when to use this tool versus alternatives. It mentions analyzing 'by service/feature tag' but doesn't explain when this is preferable to other cost/usage analysis tools like 'usage_by_model' or 'get_cost_analysis'. There are no explicit when/when-not statements or alternative recommendations.
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 mentions 'query aggregated metrics' and 'flexible filtering and dimensions', which implies a read-only operation, but doesn't clarify permissions, rate limits, pagination, or response format. For a tool with 6 parameters and no output schema, this leaves significant behavioral aspects undocumented.
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 a single, efficient sentence that front-loads the core purpose ('Query aggregated metrics') and adds qualifying details without waste. Every word earns its place, making it easy to parse quickly while conveying essential information.
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's complexity (6 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks guidance on usage, behavioral details, and output expectations. With no annotations to fill gaps, the description should do more to compensate, but it at least provides a clear starting point.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by hinting at 'flexible filtering and dimensions', which loosely relates to the 'dimensions' and 'environment' parameters, but doesn't provide additional syntax or usage details. This meets the baseline for high schema coverage.
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: 'Query aggregated metrics (costs, tokens, counts) with flexible filtering and dimensions.' It specifies the verb ('query'), resource ('aggregated metrics'), and scope ('costs, tokens, counts'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_daily_metrics' or 'usage_by_model', which appear to be related metrics tools.
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 no guidance on when to use this tool versus alternatives. It mentions 'flexible filtering and dimensions' but doesn't specify scenarios, prerequisites, or exclusions. With multiple sibling tools that seem related to metrics (e.g., 'get_daily_metrics', 'usage_by_model'), the lack of differentiation leaves the agent guessing about appropriate use cases.
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 states the tool retrieves detailed information but does not specify what 'detailed' includes, whether it's a read-only operation, any authentication requirements, rate limits, or error handling. This leaves significant gaps in understanding the tool's behavior.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for a simple tool, with no wasted information.
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's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks details on what 'detailed information' entails, behavioral traits, or usage context, which could hinder an agent's ability to use it effectively without additional exploration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting the single required parameter 'modelId'. The description adds no additional semantic context beyond what the schema provides, such as examples of model IDs or format details, so it meets the baseline for high schema coverage.
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 with a specific verb ('Get') and resource ('detailed information about a specific AI model'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'list_models' or 'usage_by_model', which might provide related but different information.
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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'list_models' for listing models or 'usage_by_model' for usage data, leaving the agent without context for tool selection in this server's ecosystem.
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 the full burden of behavioral disclosure. It mentions 'details and filtering' but lacks critical information: it doesn't specify if this is a read-only operation (implied by 'Get' but not explicit), describe pagination behavior (though 'limit' parameter hints at it), or explain return format. For a tool with 8 parameters and no annotations, this is insufficient.
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 a single, efficient sentence: 'Get LLM generations/spans with details and filtering.' It's front-loaded with the core purpose and includes key capabilities without waste. Every word earns its place, making it highly concise and well-structured.
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's complexity (8 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It covers the basic purpose and filtering aspect, but without annotations or output schema, it misses behavioral details like safety, response format, or error handling. This leaves the agent with incomplete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the schema. The description adds minimal value beyond the schema—it mentions 'filtering' which aligns with the parameters but doesn't provide additional context like how filters combine or default behaviors. Baseline 3 is appropriate when the schema does the heavy lifting.
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: 'Get LLM generations/spans with details and filtering.' It specifies the verb ('Get') and resource ('LLM generations/spans'), and mentions filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'get_traces' or 'get_observation_detail', which appear related but have different scopes.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_traces' (which might retrieve broader trace data) or 'get_observation_detail' (which might fetch a single observation), leaving the agent without context for 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 states it's a list operation but doesn't mention whether it's read-only, safe, paginated (though implied by parameters), rate-limited, or requires authentication. This leaves significant behavioral gaps for the agent.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for the agent to parse 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?
For a simple list tool with 2 parameters and 100% schema coverage, the description is minimally adequate. However, without annotations or output schema, it should ideally mention the return format (e.g., list of model objects) and any behavioral constraints to be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (limit and page). The description adds no additional parameter information beyond what's in the schema, meeting the baseline for high coverage but not providing extra value.
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 ('List') and resource ('all available AI models in the Langfuse project'), making the purpose immediately understandable. However, it doesn't distinguish this tool from similar sibling tools like 'get_model_detail' or 'usage_by_model', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'list_models' over 'get_model_detail' for specific model information or 'usage_by_model' for usage statistics, leaving the agent without contextual usage instructions.
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 mentions 'available to this MCP server', hinting at access scope, but fails to describe key behaviors like pagination, sorting, filtering, or response format. This is inadequate for a list operation with zero annotation coverage.
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 a single, efficient sentence that directly states the tool's purpose without redundancy. It is appropriately sized and front-loaded, with no wasted words, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It does not explain what 'configured' means, how projects are returned (e.g., list format, fields), or any behavioral constraints. For a list tool with no structured support, 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 does not add parameter details, but this is acceptable given the schema's completeness. A baseline of 4 is appropriate for a tool with no parameters.
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 ('List') and resource ('configured Langfuse projects'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_projects' or 'project_overview', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives such as 'get_projects' or 'project_overview'. It lacks context on use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
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 the full burden of behavioral disclosure. It states it 'gets' information, implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns real-time vs. cached data, or details the response format. This is a significant gap for a tool with zero annotation coverage.
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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a simple tool with no parameters.
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's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral details and usage context, making it incomplete for optimal agent guidance despite the simple structure.
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, earning a baseline score of 4 as it doesn't need to compensate for any gaps.
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 ('Get') and resource ('system health status and availability information'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_metrics' or 'get_daily_metrics', which might also provide health-related data, so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives like 'get_metrics' or other sibling tools. It lacks explicit context, exclusions, or prerequisites, leaving the agent to infer usage based on the name alone.
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 of behavioral disclosure. It mentions 'pagination support', which is useful context beyond basic listing, but doesn't cover other important aspects like rate limits, authentication requirements, error handling, or what the output looks like (e.g., format, fields).
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 a single, efficient sentence that front-loads the core purpose ('List all datasets in the project') and adds a key behavioral trait ('with pagination support') without any wasted words. Every element earns its place.
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's moderate complexity (listing with pagination), no annotations, and no output schema, the description is minimally adequate. It covers the basic action and pagination but lacks details on output format, error cases, or integration with sibling tools, leaving gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents both parameters (page and limit). The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced documentation.
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 ('list') and resource ('datasets in the project'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_projects' or 'list_models', which would require mentioning it's specifically about datasets rather than other project resources.
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 no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'get_dataset' (for single dataset details) or 'list_dataset_items' (for items within datasets), nor does it specify prerequisites or contextual triggers for listing datasets.
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 states this is a list operation (implying read-only), which is appropriate, but doesn't disclose any behavioral traits like pagination, rate limits, or what 'available' means (e.g., active vs. archived projects). The description adds minimal behavioral context beyond the implied read operation.
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 a single, efficient sentence that conveys all essential information: the action, resource, and alias relationship. There's zero waste, and it's perfectly front-loaded with the core purpose.
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 (0 parameters, no output schema, no annotations), the description is reasonably complete. It clearly states what the tool does and its relationship to list_projects. However, without annotations or output schema, it could benefit from mentioning what information is returned (e.g., project names, IDs, status) or any constraints, keeping it from a perfect score.
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 already fully documents the lack of parameters. The description doesn't need to add parameter information, and it appropriately doesn't mention any parameters. A baseline of 4 is appropriate for zero-parameter tools with complete schema coverage.
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 with a specific verb ('List') and resource ('Langfuse projects'), and explicitly distinguishes it from its sibling 'list_projects' by noting it's an alias. This provides perfect clarity about what the tool does and its relationship to alternatives.
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 explicitly states when to use this tool by indicating it's an 'alias for list_projects', providing clear guidance on alternatives. This directly tells the agent that this tool and list_projects are interchangeable, which is ideal usage guidance.
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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