langfuse-mcp

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v1.25.1
Disambiguation4/5
Most tools have clearly distinct purposes with detailed descriptions. However, the sheer number of tools and some overlapping functions (e.g., fetch_trace vs get_prompt, find_exceptions vs get_exception_details) may cause confusion, though descriptions help distinguish them.
Naming Consistency3/5
Tool names are not fully consistent. They mix verb styles: create_, fetch_, get_, list_, find_, query_, update_, delete_, etc. While each group is internally consistent (e.g., all creation tools start with 'create'), the use of different verbs for similar actions (e.g., fetch_trace vs get_prompt) creates inconsistency.
Tool Count2/5
48 tools is far above the typical 3-15 range for a well-scoped server. While each tool serves a distinct purpose, the count is excessive and overwhelming for an agent, especially since many are for advanced analytics (exceptions, route decisions) that may not be needed in typical workflows.
Completeness3/5
The tool set covers a wide range of Langfuse functionalities, but there are notable gaps: no tool for creating or updating datasets (only create and read), no direct trace/observation ingestion (likely done via SDK), and no deletion for annotation queues or prompts. Basic CRUD is incomplete for some entities.
Average 3.5/5 across 48 of 48 tools scored. Lowest: 1.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 9 of 9 community issues answered or closed in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on August 6, 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It fails to mention side effects, idempotency, validation, or any operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is too sparse and does not convey essential information, making it under-specified rather than concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and related sibling tools, the description is highly incomplete. It omits output details, relationships, and any usage context.
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 coverage is 100%, and the description does not add meaning beyond the schema. Baseline score of 3 is appropriate.
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?
Description is a tautology, merely restating the tool name without specifying the resource or distinguishing it from sibling tools like create_annotation_queue or create_annotation_queue_assignment.
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 on when to use this tool versus alternatives, such as creating a queue or updating an item. The description lacks context for appropriate 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, the description carries the full burden of behavioral disclosure. It only states the action 'create' without revealing potential side effects, authentication requirements, or what happens upon creation (e.g., returns the created queue). This is insufficient for an agent to predict 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of four words, which is concise but not informative. It restates the tool name without front-loading significant details. While it lacks wasted words, it also lacks substance, so it does not earn its place fully.
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 simplicity, the description is too minimal. It does not mention return values even though an output schema exists, nor does it differentiate from sibling tools or provide behavioral context. For a create tool, more guidance on the object being created (e.g., queue properties, persistence) would be expected.
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 baseline is 3. The tool description does not add any additional meaning to the parameters (name, description, score_config_ids) beyond what the schema already provides. Thus, no improvement over the baseline.
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 'create' and resource 'annotation queue', but it is essentially a restatement of the tool name. It does not differentiate this tool from sibling tools like create_annotation_queue_assignment or create_annotation_queue_item. The purpose is clear but generic.
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 (e.g., create_annotation_queue_assignment for assignments, list_annotation_queues for retrieval). There is no mention of prerequisites or context for invocation.
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, the description must fully disclose behavior. It mentions 'flush the ingestion write', hinting at a side effect, but does not clarify what that means or any implications (e.g., data persistence, performance impact). It lacks details on required permissions, reversibility, or response behavior.
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 with two clauses, which is relatively concise but not optimally structured. The phrase 'flush the ingestion write' is tacked on without explanation, reducing clarity. Could be reordered for better front-loading.
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?
Despite a full schema and output schema, the description lacks context about the relationship between dataset runs, items, and the 'ingestion write' concept. It does not explain when this tool is appropriate or what the side effect entails, leaving gaps for 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?
Schema description coverage is 100%, so baseline is 3. The description adds no additional parameter meaning beyond what the schema already provides, such as format or context for 'run_name' or 'dataset_item_id'.
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 creates a dataset run item, specifying the verb and resource. It distinguishes from siblings like 'create_dataset_item' by adding 'flush the ingestion write', though this phrase is somewhat cryptic. It's specific enough to avoid confusion with other create 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?
No guidance is provided on when to use this tool versus alternatives like 'create_dataset_item' or 'create_dataset'. The description does not mention prerequisites, context, or situations where this tool should not be used.
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?
Without annotations, the description must convey behavioral traits. It indicates that this tool performs a deletion (destructive action), but no details about side effects, reversibility, authorization requirements, or concurrency implications are provided.
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 short sentence, making it concise and front-loaded. However, it is arguably too minimal, lacking any contextual detail that would be helpful. It is not verbose, but also fails to provide substantive 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 that the tool has an output schema and two required parameters, the description is incomplete. It does not describe the return value or any aspects of the deletion process, leaving the agent without critical context for proper 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 already provides descriptions for both parameters (queue_id and item_id), giving schema description coverage 100%. The description adds no additional meaning beyond the schema, so it meets the baseline for high coverage but does not enhance understanding.
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 action (delete) and the resource (annotation queue item), distinguishing it from sibling tools like create or update. However, it lacks details about what an annotation queue item is, but the verb+resource combination is specific enough.
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 usage guidelines are provided. The description does not indicate when to use this tool over alternatives like update_annotation_queue_item or list_annotation_queue_items, nor does it mention any prerequisites or context for deletion.
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 provided; description only states basic function. Does not disclose read-only nature, rate limits, ordering, or any behavioral traits beyond listing. The output schema may define structure but description adds no transparency.
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?
Single sentence is concise and front-loaded, but at the expense of omitting crucial details like pagination or filter behavior. Acceptable but not optimal for a complex tool.
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?
With 17 optional parameters and no annotations, the description is insufficient. It fails to explain filter interactions, defaults, or result format, relying entirely on the input schema for context.
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 coverage is 100% with descriptions for all 17 parameters. The description adds no extra meaning beyond 'optional filters', so it neither harms nor improves parameter understanding compared to schema alone.
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 states 'List scores from the score v2 API with optional filters,' which clearly identifies the verb (list) and resource (scores). It distinguishes from sibling 'get_score_v2' by implying multiple vs single scores, but does not mention pagination explicitly.
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 on when to use this tool versus alternatives like 'get_score_v2'. The description merely says 'optional filters' without explaining filter semantics or providing examples.
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, and the description is minimal. It does not disclose whether the tool aggregates data, how pagination works, what the summary contains, or any side effects. The existence of an output schema is not mentioned.
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, which is concise but lacks sufficient detail to be fully informative. It fronts the main verb and resource but omits necessary context about the summary output.
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 9 optional parameters and an output schema, the description is too sparse. It does not explain what the summarized output looks like, how filters interact, or the purpose of the 'max_confidence' parameter. The agent lacks information to properly invoke the tool.
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 for all 9 parameters, so each parameter's meaning is clear from the schema. The description adds no extra semantics beyond listing the filtering dimensions, meeting 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 uses a specific verb 'summarize' and specifies the resource 'route decisions' with filtering dimensions (trace, session, router, provider, capability). It clearly indicates the tool's purpose but does not explicitly differentiate from siblings like 'find_route_decisions'.
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, nor does it specify prerequisites or when not to use it. It simply states the 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?
With no annotations provided, the description bears full responsibility for behavioral traits. It fails to disclose that the tool is read-only, does not mention performance implications of filters like include_observations, and lacks any information about side effects or constraints.
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, concise sentence that is easy to read and front-loaded. However, it could be slightly more structured to include key points like 'Read-only' or 'Returns traces in specified format'.
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?
Despite the presence of an output schema and comprehensive parameter descriptions, the tool's complexity (10 parameters) is not matched by the description. Missing context includes when to use different modes, pagination behavior beyond schema, and typical use cases.
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?
All parameters have detailed schema descriptions (100% coverage), so baseline is 3. The description adds the clarification that all non-required parameters are optional, which is helpful but not extensive.
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 finds traces based on filters, which accurately conveys its purpose as a search/query tool. However, it does not explicitly distinguish between this and sibling fetch tools like fetch_trace (single trace) or fetch_sessions, which limits 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 mentions 'All filter parameters are optional', which implies flexibility but offers no guidance on when to use this tool versus alternatives like fetch_trace or fetch_observations. No exclusionary or contextual advice is provided.
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 indicates the tool returns run items but does not disclose whether it is read-only, required permissions, or error behavior (e.g., when run not found). The output schema is present but not described.
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 concise sentence that efficiently conveys the core purpose. There is no redundancy or unnecessary 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?
With three parameters (including an enum for output_mode) and an output schema, the description is too minimal. It fails to clarify the output_mode choices, the relationship between dataset_name and run_name, or what 'run items' means. Additional context is needed 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%, so the baseline is 3. The description does not add any additional meaning beyond what the schema already provides for the three parameters. No parameter details are given.
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 'Get a dataset run' with a specific verb and resource. It adds context by mentioning 'including any run items returned by the SDK', which indicates the scope of the response. However, it does not explicitly differentiate from sibling tools like list_dataset_runs or get_dataset_item.
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?
There is no guidance on when to use this tool over alternatives, such as list_dataset_runs for listing runs or get_dataset_item for individual items. No prerequisites or exclusions 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?
The description only states 'List annotation queues with pagination'. It does not disclose any behavioral traits such as whether it is read-only, any rate limits, authentication needs, or ordering. With no annotations provided, the description should compensate but fails to do so.
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 very concise at one sentence. It is front-loaded with the action and resource. No unnecessary words, though a bit more context could be added without harming conciseness.
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, an output schema exists, and parameters are well-documented, the description is mostly adequate. However, it lacks any explanation of what annotation queues are or typical use cases, which could aid 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?
The input schema has 100% coverage with descriptions for page and limit. The description's mention of 'pagination' is generic and does not add new meaning beyond the schema. Thus, it meets the baseline but adds no 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 action 'List annotation queues' and adds 'with pagination', which specifies the resource and a key feature. The name aligns well, and it distinguishes from sibling tools like get_annotation_queue or list_annotation_queue_items by focusing on queues themselves.
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. For instance, it does not mention that this tool lists all queues while get_annotation_queue retrieves a single queue. The description lacks context about use cases or prerequisites.
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, the description fails to disclose key behavioral traits such as read-only nature, pagination behavior, or output mode implications. The agent cannot tell if this operation is safe or if it triggers side effects.
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 concise sentence with no wasted words. However, it lacks structure or front-loading of critical information that would help an agent quickly assess relevance.
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 5 parameters and an output schema, the description is underspecified. It omits pagination limits, output mode details, and use case context (e.g., listing items for review or export), making it only moderately 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 input schema already documents all parameters. The description adds no additional meaning or context beyond the schema, meeting the baseline but not exceeding it.
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 it lists items from a dataset run, specifying the two key identifiers (dataset ID and run name). However, it does not differentiate from similar siblings like list_dataset_items or list_dataset_runs, which could cause confusion for an agent.
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 (e.g., list_dataset_items, list_dataset_runs), nor any prerequisites or exclusion criteria. The agent must 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 provided. Description only states the action without disclosing side effects, error handling, or reversibility. Lacks behavioral context beyond the basic 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?
Single sentence, no wasted words. Perfectly concise for the information provided.
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 simple parameters and no annotations, the description is adequate but minimal. Could mention idempotency or that it does nothing if assignment doesn't exist. Output schema exists, so return value detail is not required.
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 already provides clear descriptions for both parameters (queue_id and user_id) with 100% coverage. Description adds no additional meaning, so baseline 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?
Description clearly states verb 'Unassign' and resource 'user from an annotation queue'. Distinguishes from creating assignments but does not explicitly differentiate from other delete tools like 'delete_annotation_queue_item'.
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 on when to use this tool versus alternatives. For example, does not mention that this is for removing user assignments, while deleting items is for queue items.
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 provided, so the description must disclose behavior. It only implies a read operation but doesn't mention rate limits, auth requirements, or what happens if decision_id doesn't exist. The age and output_mode parameters affect behavior but are not described.
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 sentence with no filler words. It is front-loaded with the core purpose. However, it could include more context without being verbose.
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?
Despite an output schema existing, the description omits important context about the age parameter (time window), output_mode options, and how this tool relates to siblings. It is insufficient for a 3-parameter tool with enums.
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 coverage is 100% with descriptions for each parameter. The description adds no extra meaning beyond the schema; it only references decision_id. Baseline score of 3 is appropriate.
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 verb 'Fetch', the resource 'one route decision', and the identifier 'by metadata decision_id'. It distinguishes from sibling tools like find_route_decisions which imply multiple results.
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 on when to use this tool versus alternatives like find_route_decisions or fetch_traces. The description does not mention prerequisites or 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 provided, so description must self-disclose. It only states the core action without mentioning idempotency, side effects, error handling, or rate limits.
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?
One efficient sentence with no wasted words. Action is front-loaded.
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 1-param tool with an output schema, the description is adequate but lacks error handling notes and response format mention beyond what the schema provides.
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 coverage is 100% with the parameter 'score_id' described as 'Score ID'. The description adds version context but does not enhance parameter meaning beyond the 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 it retrieves a score by ID and mentions the underlying API versions. It implicitly distinguishes from sibling 'list_scores_v2' via the verb 'get' vs 'list', but does not explicitly differentiate.
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 on when to use this tool versus alternatives like 'list_scores_v2'. No prerequisites or context provided.
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 present, and the description does not disclose behavioral details like pagination handling, rate limits, or behavior on empty queues. The schema hints at pagination but the description itself is silent.
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, concise sentence with no unnecessary words, effectively stating the tool's purpose.
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 presence of an output schema and full parameter descriptions, the description is minimally adequate but lacks context on output format, ordering, or edge cases.
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 coverage is 100%, so the description adds no extra meaning beyond what the schema already provides. 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 lists items in an annotation queue, using a specific verb and resource. However, it does not distinguish from sibling tools like get_annotation_queue_item or create_annotation_queue_item.
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, such as when to list vs. fetch a single item or handle pagination.
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?
The description claims an 'ctx' argument that is not present in the input schema, creating a discrepancy. This misleads agents about the required input. No annotations are provided, so the description must carry the full burden. It does not state whether the operation is read-only or has any side effects.
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 has a clear structure with Args and Returns sections, and the first sentence provides the purpose. However, the Args section redundantly repeats information already present in the schema, making it longer than necessary. It could be more concise.
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?
The description explains the return value structure (list of prompt metadata and pagination info), which is useful since no output schema is defined in the input schema. However, it misses error conditions, rate limits, or authentication requirements. The extra 'ctx' param issue reduces completeness.
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 coverage is 100%, so baseline is 3. The description's parameter descriptions largely repeat the schema (e.g., 'Filter by exact prompt name'). The addition of the 'ctx' parameter not in schema is confusing and does not add meaningful semantic value beyond what the schema already provides.
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 begins with 'List and filter prompts in the project', clearly stating the verb (list/filter) and resource (prompts). It explains the returned metadata (versions, labels, tags, last updated time), which distinguishes it from sibling tools like get_prompt that likely return full details. However, it does not explicitly contrast with siblings.
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 lists filter parameters (name, label, tag, page, limit), implying when to use the tool (filtering prompts). But it does not provide explicit guidance on when to use this versus alternatives like get_prompt or create_prompt, nor does it mention when not to use it.
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?
The description discloses that the 'age' parameter is capped by a configured maximum and details the three output modes. However, it does not mention whether the operation is read-only, has side effects, or requires specific permissions. With no annotations, this gap reduces 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 well-structured with 'Args' and 'Returns' sections, though it is somewhat lengthy. The Returns section is informative and justified, but it could be slightly more concise.
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 (3 parameters, output schema exists), the description adequately covers return formats and the age cap. It does not address error handling or prerequisites, but it provides sufficient context for most usage scenarios.
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 for all three parameters, so the description adds minimal extra meaning. It repeats the schema's parameter descriptions without enhancing them, resulting in the baseline score.
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 'Get detailed exception info for a specific file,' specifying the verb and resource. However, it does not differentiate from the sibling tool 'find_exceptions', which likely serves a similar but broader purpose.
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 'find_exceptions' or other tool. The description lacks context about appropriate scenarios, 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?
With no annotations, the description must disclose behavioral traits. It only states what it finds, not that it is a read-only query, how results are returned, or any side effects. Minimal disclosure.
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?
A single, well-formed sentence of nine words that captures the tool's core function with no unnecessary information. Perfectly concise.
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 existence of an output schema and full parameter descriptions, the short description combined with the schema provides sufficient context for a straightforward filtered query. It could mention that results are a list, but overall 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?
The input schema covers 100% of parameters with detailed descriptions, so the tool description adds no extra param meaning. The description's brief mention of 'low-confidence' aligns with the 'max_confidence' parameter, but no additional insight is provided. Baseline 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 specifies the verb 'find' and the resource 'route decisions' with qualifiers 'low-confidence or explicitly uncallable'. This distinguishes it from sibling tool 'find_route_decisions', though it could explicitly contrast the two.
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 'find_route_decisions' or 'find_exceptions'. No context on prerequisites or filters beyond the name.
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, the description provides minimal behavioral disclosure. It does not mention side effects (e.g., replacing existing assignments), required permissions, or error conditions. The action is clear but lacks depth.
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?
Single sentence, 8 words, no redundancy. Every word adds value, and the description is well front-loaded.
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 simplicity of the tool and presence of an output schema, the description is minimally adequate. However, it omits behavioral details like idempotency and validation, which would be helpful for 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?
Input schema has 100% description coverage, adequately describing the parameters. The description does not add extra semantic value beyond what the schema already provides, such as format constraints or where to find IDs.
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?
Description clearly states the action ('Assign') and the resources ('user to an annotation queue'), distinguishing it from sibling tools like create_annotation_queue (creating the queue) and delete_annotation_queue_assignment (deleting an assignment).
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 on when to use this tool versus alternatives, nor any prerequisites or exclusions. For example, it does not specify that the queue and user must already exist, or whether the tool can reassign a user.
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, the description carries full burden. It states 'delete' implying destruction, but lacks details on reversibility, cascading effects, permissions, or rate limits.
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?
Single sentence with no fluff, directly stating the action and required identifiers.
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 two-parameter delete operation with full schema coverage and an output schema, the description is adequate but could mention consequences or prerequisites.
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 descriptions cover both parameters (dataset_name and run_name) with 100% coverage. The description adds no extra semantics beyond confirming the parameters' roles.
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 action 'delete' and the resource 'dataset run' with the method 'by dataset name and run name'. It distinguishes from sibling tools like delete_dataset_item and delete_annotation_queue_item.
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 explicit guidance on when to use this tool vs alternatives. Does not mention when not to use it, prerequisites, or context for deletion.
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, description must disclose behavioral traits. It only states 'list runs' with no mention of pagination, ordering, error handling, or side effects. Pagination parameters exist in schema but description does not clarify 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?
Single sentence with no unnecessary words. Efficiently conveys the core purpose.
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?
Output schema exists (not shown), so explanation of return values is not required. However, the description lacks details on pagination behavior and iteration process, which are important for a listing tool. Could 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 coverage is 100%, so baseline is 3. Description adds no additional meaning beyond what the schema already provides for the three parameters.
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?
Description clearly states the action (list), resource (runs), and the constraint (by dataset name). It effectively distinguishes from sibling tools like 'get_dataset_run' (single run) and 'list_dataset_items' (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?
No guidance on when to use this tool versus alternatives (e.g., 'get_dataset_run' for a single run, 'list_dataset_items' for items). Does not 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. It only states 'update the status' without disclosing important behavioral details such as valid status transitions, error conditions, or permission requirements. This is insufficient for a mutation tool.
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, which is concise, but it lacks necessary detail to be fully effective. Every word is earned, but the content is minimal.
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 that the input schema is fully described and an output schema exists, the description is still incomplete. It does not specify allowed status values or constraints, making it adequate but with clear gaps.
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 each parameter. The description does not add any meaning beyond the schema, such as valid status values. Baseline score of 3 is appropriate.
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 action ('Update'), the resource ('annotation queue item'), and the attribute being updated ('status'). It distinguishes itself from sibling tools like get_annotation_queue_item (read) and delete_annotation_queue_item (delete).
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 changing the status of a queue item, but it provides no explicit guidance on when to use this tool versus alternatives, 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly notes that the deletion is permanent and cannot be undone, which is important behavioral information. However, with no annotations provided, it carries the full burden but still lacks details on authorization, side effects, or impact on related 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 concise and front-loads the main action. The Args and Returns sections add some redundancy but are structured and easy to parse. Could be slightly more terse.
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 single parameter and the presence of an output schema, the description covers the basic functionality. However, it lacks contextual completeness regarding permissions, preconditions, or integration with sibling tools, warranting only a moderate score.
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 coverage is 100% and the description repeats the parameter description from the schema without adding new semantic information. The Args section provides minimal added value beyond what the schema already contains.
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 action (Delete), the resource (dataset item), and the identifier method (by ID). It is specific and distinguishes from sibling tools like delete_annotation_queue_item.
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, such as other delete operations. There is no mention of prerequisites or context for appropriate use.
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. It only says 'Get a single annotation queue by ID', which implies read-only, but it does not disclose error behavior (e.g., 404 if not found), permissions, or rate limits. For a simple getter, minimal transparency is acceptable but not sufficient.
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 sentence, front-loaded, and efficient. It could be slightly more informative (e.g., mentioning the output), but given the simplicity, it is concise and to the point.
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?
The tool has an output schema (not shown), so the description does not need to explain return values. However, it does not provide context on what an 'annotation queue' is or how the queue relates to other entities. Adequate for a simple retrieval but could 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% for the single parameter. The description adds no additional meaning beyond what the schema already provides (queue_id is an Annotation queue ID). Baseline 3 is appropriate.
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 action ('Get') and the resource ('a single annotation queue by ID'). It distinguishes from sibling tools like 'list_annotation_queues' (which returns multiple) and 'create_annotation_queue' (which creates).
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 for retrieving one specific queue by ID, but it does not explicitly guide when to use this tool versus alternatives like 'list_annotation_queues' or when not to use it. Basic usage context is missing.
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 provided, so description bears full burden. Describes return fields (id, name, description, etc.) but does not disclose behavior on missing datasets (error vs. null) or side effects. Adequate but not comprehensive.
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?
Structured with a two-line summary plus Args/Returns sections. All content is relevant, though the Returns section could be omitted if output schema is present. Concise enough for a simple tool.
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 (one parameter, read-only) and presence of an output schema, the description covers purpose and return structure adequately. Lacks usage guidance but is otherwise 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% for the single parameter 'name'. Description repeats the schema description ('The name of the dataset to fetch') without adding new context, so baseline 3 applies.
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?
Description explicitly states 'Get a specific dataset by name' with clear verb and resource. Distinguishes from siblings like list_datasets, get_dataset_item, and create_dataset by focusing on retrieving a single dataset 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?
No guidance on when to use this tool vs. alternatives (e.g., list_datasets for browsing, get_dataset_item for items). No mention of prerequisites or exclusion criteria.
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 must cover behavioral traits. It mentions the age parameter is 'capped by the configured maximum' but doesn't explain behavior on cap exceedance (e.g., silent cap or error). Missing details on side effects, authentication, or performance.
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 mostly concise but includes a mention of 'ctx' argument not present in the input schema, causing confusion. The Args/Returns structure is clear, but the extra context detracts from conciseness.
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 tool with one parameter and an output schema, the description is adequate but lacks details on exact return field names, error handling, and edge cases (e.g., empty traces). The output schema might compensate, but it is not shown.
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 coverage is 100% with a clear description for the single parameter 'age'. The description adds 'capped by the configured maximum' which provides extra context, but no further semantic value beyond the schema.
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 'Get number of traces with exceptions in last N minutes,' which is a specific verb+resource+scope. This distinguishes it from siblings like find_exceptions or get_exception_details.
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?
No explicit guidance on when to use this tool versus alternatives like find_exceptions or get_exception_details. The description implies usage for aggregated error counts but lacks when-not or alternative recommendations.
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?
No annotations are provided, so the description carries the full burden. It explains output modes (compact, full_json_string, full_json_file) and mentions pagination (page, limit), which are key behavioral traits. However, it does not explicitly state that the operation is read-only or idempotent, which is acceptable for a fetch operation.
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 somewhat verbose with a Python docstring style (Args/Returns). The first sentence is clear, but the rest is lengthy with parameter listings that mirror the schema. Could be more concise and front-loaded.
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 complexity (9 parameters, output schema exists), the description covers filtering, pagination, and output modes well. The Returns section provides a useful breakdown of output based on output_mode. It is nearly complete, though it lacks mention of potential performance implications or large result set handling.
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 baseline is 3. The description's Args section largely repeats the schema's parameter descriptions without adding significant new meaning beyond summarizing them. The Returns section adds some value by explaining output modes, but this is partially covered in the output_mode schema description.
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 'Get observations filtered by type and other criteria.' It specifies the verb (get) and resource (observations), and the filtering criteria differentiate it from siblings like fetch_observation (which retrieves a single observation by ID).
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 vs alternatives, such as fetch_observation for a single observation or other fetch tools. It lacks explicit 'when to use' or 'when not to use' context.
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, the description must disclose behavior. It mentions the age cap ('capped by the configured maximum') and grouping options, which adds some transparency. However, it does not state that the operation is read-only, nor does it describe side effects or permissions. The inclusion of an undocumented 'ctx' param in the description may confuse agents.
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 relatively concise with a clear purpose sentence and structured Args/Returns sections. However, it includes 'ctx' which is not in the input schema, introducing potential confusion and unnecessary content. The information is front-loaded but marred by the irrelevant parameter.
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?
For a simple tool with two parameters, the description covers the grouping behavior, return type, and parameter constraints. The presence of an output schema fills gaps about return structure. The only shortcoming is the unexplained 'ctx' parameter, which slightly reduces completeness.
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 coverage is 100%, so baseline is 3. The description adds value by explaining the age cap and elaborating on the group_by enum options (e.g., 'file' groups by filename). This clarifies the schema descriptions, earning a 4.
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 retrieves exception counts grouped by file path, function, or type. The verb 'Get' and resource 'exception counts' are specific, and it distinguishes from sibling tools like 'find_exceptions_in_file' which likely focuses on a single file.
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 does not provide any guidance on when to use this tool versus alternatives such as 'find_exceptions_in_file' or 'get_error_count'. No usage context, prerequisites, or exclusions are mentioned, leaving the agent to infer without support.
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 provided, so description must disclose behavior fully. It covers the core upsert logic and return value, but omits side effects, error conditions, permissions, or rate limits. Minimal but acceptable for a create tool.
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?
Well-structured with a clear summary, Args list, and Returns section. Each sentence is informative, but the Args list is somewhat redundant with the schema. No unnecessary text, but could be trimmed slightly.
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 8 parameters and an output schema, the description explains the operation, all parameters, and return type. Lacks examples or edge cases, but the presence of an output schema reduces the need to detail return structure. Fairly comprehensive.
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 descriptions already cover all 8 parameters (100% coverage). The description's 'Args' section largely reiterates schema info, adding only slight context like the upsert behavior for item_id, which is already in schema. No significant extra meaning.
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?
Description clearly states the tool creates or updates a dataset item with upsert behavior, using specific verb+resource. It distinguishes from siblings like delete_dataset_item by describing both create and update functionality.
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?
Explains upsert behavior when item_id is provided, implying when to update vs create, but does not explicitly compare to sibling tools or provide exclusions (e.g., when to use delete_dataset_item instead). Lacks explicit when-to-use/ when-not-to-use guidance.
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, the description effectively reveals behavior: it paginates, accepts an age filter, and offers three output modes. It does not mention side effects or authorization, but for a read-only listing tool, this is sufficient.
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 Args and Returns sections. Each sentence adds value, though it could be slightly more concise. Still efficient and front-loaded.
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 (4 params, enum, output schema), the description covers the core functionality, pagination, and output modes. It does not explain 'current project' scope or connection to other tools, but overall it provides enough context for correct invocation.
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 coverage is 100%, so baseline is 3. The description adds significant value by detailing the output_mode parameter and its three options, plus clarifying pagination parameters (page, limit) and the purpose of age. This exceeds the 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 'Get a list of sessions in the current project', identifying the resource and action. However, it does not differentiate from sibling tools like get_session_details or get_user_sessions, which could cause ambiguity.
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 for listing sessions but provides no explicit guidance on when to use this tool versus alternatives, nor any conditions or exclusions. Agents must infer based on the tool name.
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 provided, so description carries full burden. It indicates retrieval ('Get') but does not explicitly state read-only nature, potential errors (e.g., item not found), or permissions required. Adequate but could be more explicit.
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?
Single sentence that is concise, clear, and front-loaded with the purpose. No redundant 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?
Output schema exists, so return values are documented elsewhere. Tool is simple retrieval; description adequately covers functionality. Could mention preconditions (e.g., queue exists) but not critical for 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?
Input schema provides 100% coverage with descriptions for both parameters. Description echoes the schema ('by queue and item ID') without adding new meaning. Baseline 3 is appropriate.
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?
Description clearly states verb 'Get', resource 'specific annotation queue item', and method 'by queue and item ID'. Distinguishes from sibling tools like list_annotation_queue_items (list) and update_annotation_queue_item (update).
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?
Implies usage context (knowing queue_id and item_id) but does not explicitly state when to use this tool versus alternatives like list_annotation_queue_items or update. No when-not-to-use guidance.
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?
Without annotations, the description partially covers behavior by stating it returns a string with schema definitions and noting the dummy parameter is unused. However, it lacks details on side effects, error handling, or performance implications.
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 concise at about 50 words, front-loading the purpose before Args and Returns. Every sentence is informative, with no unnecessary repetition.
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 optional param, output schema available), the description is mostly complete. It could mention that no authentication or side effects exist, but the core functionality is well-covered.
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 meaning beyond the schema by explaining the 'dummy' parameter as 'Unused parameter for API compatibility (can be left empty)', which prevents confusion. The schema only provides type and default.
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 'Get schema of trace, span and event objects', using a specific verb and resource. It distinguishes the tool from siblings like 'get_prompt' or 'get_dataset' which retrieve specific objects, not schemas.
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 on when to use this tool versus alternatives, nor any context on prerequisites or exclusions. It only describes what the tool does without advising on usage scenarios.
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?
No annotations provided, so description carries full behavioral burden. It clearly describes a read-only operation (listing items) and specifies return format (dictionary with data and metadata). Does not mention side effects, which is appropriate. No contradictions.
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?
Description includes docstring-style Args and Returns sections, making it somewhat lengthy. First sentence is effective, but subsequent lines often echo schema information. Could be more concise without losing clarity.
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?
With 6 parameters, full schema coverage, and an output schema, the description adequately covers tool behavior and return structure. It explains pagination, filtering, and output modes. Slightly lacking in details about return object contents, but output schema compensates.
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 coverage is 100%, so baseline is 3. Description repeats some schema descriptions (e.g., dataset_name, page) but adds minor context for output_mode (explains enum values). Does not significantly enhance parameter meaning beyond schema.
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 it lists dataset items with pagination and optional filtering, using specific verb 'List items in a dataset'. It distinguishes from sibling tools like 'list_datasets' (which lists datasets) and 'get_dataset_item' (single item).
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 mentions pagination and filtering but does not provide explicit guidance on when to use this tool versus alternatives like 'create_dataset_item' or 'get_dataset_item'. An agent must infer context from the tool's purpose.
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 exist, so description must disclose behavior. It accurately states it returns metadata and pagination info, but lacks details on side effects, rate limits, or authentication. Adequate for a 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?
Concise with a clear opening sentence, docstring-style args, and return structure. No unnecessary words, well-organized.
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?
Description explains return structure (data and metadata), which is sufficient given the output schema exists. Could mention that pagination starts at 1, but that's in schema. Overall 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 coverage is 100%; description repeats parameter details already in schema. No additional semantics or constraints added beyond what 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?
Clearly states 'List all datasets in the project with pagination,' specifying the verb, resource, and scope. Distinguishes from siblings like 'get_dataset' and 'create_dataset'.
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?
Does not provide when-to-use or when-not-to-use guidance. No mention of alternatives like 'get_dataset' for specific datasets or pagination best practices.
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 provided. The description mentions uniqueness constraint for 'name' but does not disclose other behavioral traits like failure modes on duplicate names, required permissions, or side effects. For a creation tool, more context would be beneficial.
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?
Well-structured with clear sections. The Args/Returns format adds clarity but could be slightly more concise given schema coverage.
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 low complexity (3 simple parameters) and the presence of an output schema, the description covers essential aspects. The explanation of return fields is redundant due to output schema but still helpful.
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 coverage is 100% with descriptions for each parameter. The description adds context like 'must be unique' for name and clarifies optionality, which is helpful beyond the schema.
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 'Create a new dataset in the project' and explains the purpose of datasets. It distinguishes from sibling tools like create_dataset_item and list_datasets by specifying it creates the dataset container.
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?
No explicit guidance on when to use this tool versus alternatives (e.g., when to use create_dataset_item after). The description implies usage for initial dataset creation but lacks explicit when-not or alternative mentions.
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 must disclose behavioral traits. It describes output modes and return types but does not explicitly state if the operation is read-only, requires permissions, or has any side effects. The output schema covers return values, but behavioral context is lacking.
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 structured with clear sections (Args, Returns) and is appropriately sized for a simple fetch tool. However, it uses docstring formatting that is slightly verbose for a tool description.
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 simple nature (2 params, output schema exists), the description is fairly complete. It explains the purpose, parameters, and return types for each output mode. Minor gaps include missing prerequisites or error handling, but these are not critical for a basic fetch operation.
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 coverage is 100%, so baseline is 3. The description adds value by explaining the three output_mode options in detail ('compact' returns summarized JSON, 'full_json_string' returns raw JSON as string, 'full_json_file' returns summarized object and saves file), which goes beyond the enum list in the schema.
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: 'Get a single observation by ID'. This is a specific verb+resource combination that distinguishes it from sibling tools like 'fetch_observations' (plural) and 'fetch_trace', which operate on different entities.
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 explains the output_mode parameter but does not explicitly state when to use this tool versus alternatives like 'fetch_observations' (for multiple observations) or other fetch tools. No when-to-use or when-not-to-use guidance is provided.
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 fully carries the burden. It discloses caching behavior, mutual exclusivity validation, and error conditions (ValueError, LookupError), but does not cover authorization or side effects.
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 structured with Args, Returns, Raises sections and is front-loaded with the primary action. It is slightly verbose but not unnecessarily so, given the detail needed.
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?
The description covers all essential aspects: purpose, parameters with constraints, return values with a detailed dict, and errors. With an output schema and clear sibling context, it is fully 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 coverage is 100%, so the baseline is 3. The description adds the mutual exclusivity note and raises section, but the schema already documents mutual exclusivity. Minimal added value beyond schema.
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 'Fetch a specific prompt by name with resolved dependencies,' using a specific verb and resource. It distinguishes from the sibling tool 'get_prompt_unresolved' by explicitly mentioning that dependencies are resolved.
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 explains that label and version are mutually exclusive and that caching is used, but it does not explicitly contrast with sibling tools or specify when to use this tool vs. 'get_prompt_unresolved'.
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?
The description discloses that include_observations significantly increases response time, and that full_json_file saves data to a file. No other behavioral traits like authentication or rate limits are mentioned, but given no annotations, this is acceptable.
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 sections, but it is relatively long. However, every part contributes value, so it is appropriately sized.
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 of the tool (4 parameters, 3 output modes) and the existence of an output schema, the description covers all aspects: purpose, parameters, returns, and usage tips, making it complete.
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 coverage is 100%, so baseline is 3. The description adds detailed explanations for each parameter and usage tips, providing extra context beyond the schema.
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 explicitly states 'Get sessions for a user within a time range', which is a clear verb+resource+scope. It distinguishes from siblings like 'fetch_sessions' by focusing on a specific user.
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 on when to use this tool versus alternatives like 'fetch_sessions' or 'get_session_details'. The usage tips only cover parameter combinations, not tool selection context.
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 provided, so description carries burden. Describes read-only nature and notes dummy parameter is unused. Lacks details on side effects, permissions, or rate limits, but adequate for a simple getter.
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?
Two sentences, front-loaded with main purpose, no fluff. Efficiently conveys key 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 output schema exists, description need not detail return values. Mentions what schema includes (views, dimensions, etc.), which is sufficient. Complete for a simple getter tool.
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?
Only one optional parameter 'dummy' with default. Schema coverage 0%. Description clarifies dummy is unused and for API compatibility, adding value over schema. Adequate but minimal.
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?
Clearly states it retrieves the query schema for the metrics API, describing views, dimensions, measures, aggregations. Distinguishes itself as companion to query_metrics.
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?
Explicitly states it is a companion to query_metrics and describes schema contents, implying it should be used before making queries. Does not provide when-not or alternatives, but role is clear.
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, the description fully explains behavior: returns raw prompt with dependency tags intact, notes fallback when SDK doesn't support resolve=false (returns resolved and sets metadata.resolved=True), and lists exceptions raised (ValueError, LookupError). This provides good insight into the tool's operation.
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?
Description is well-structured with a clear first sentence stating purpose, followed by behavioral explanation, then structured Args/Returns/Raises sections. It is concise and front-loaded with the key differentiator.
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 output schema exists, description doesn't need to detail return values. It covers behavior, failure modes, and parameter constraints comprehensively for a tool of this complexity.
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 coverage is 100%, so baseline is 3. The description adds minor reinforcement of mutual exclusivity between label and version, but doesn't provide significant meaning beyond the schema. The Args section largely repeats schema content.
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?
Description clearly states 'Fetch a specific prompt by name WITHOUT resolving dependencies.' It specifies the verb (fetch), resource (prompt by name), and the key differentiator (unresolved) which distinguishes it from sibling tools like get_prompt.
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?
Description states 'Useful for analyzing prompt composition and debugging dependency chains,' clearly implying use cases. While it doesn't explicitly list when not to use, the sibling 'get_prompt' is naturally understood as the alternative for resolved 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?
With no annotations, the description discloses key behaviors: immutability and label uniqueness. However, it omits other relevant traits like idempotency, permissions, or rate limits, leaving some 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 three sentences long, front-loaded with the primary action, and contains no extraneous information. Every sentence serves a 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?
For a 6-parameter tool with output schema, the description covers the essential context (immutability, versioning, label behavior). It could mention optional parameters like config or tags, but the schema handles that.
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 coverage is 100% (baseline 3), but the description adds value by explaining the prompt format (arrays of role/content) and that labels are unique across versions, aiding understanding beyond the schema.
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 creates a new chat prompt version in Langfuse. It specifies the resource type ('chat prompt') and distinguishes it from siblings like 'create_text_prompt' by noting the structure (arrays of role/content messages) and immutability.
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 explains that prompts are immutable and a new version must be created to update content, which guides when to use this tool. It does not explicitly list alternatives but context suffices.
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, the description discloses immutability and that labels move between versions, which are key behavioral traits. However, it could elaborate on side effects or prerequisites.
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?
Three concise sentences with no wasted words. Every sentence provides essential context: creation, immutability, and label behavior.
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?
The description covers purpose and key behaviors. It does not mention that the prompt name must already exist (if that is a prerequisite), but with an output schema present, return values are covered.
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 6 parameters. The description adds no extra meaning beyond the schema's descriptions.
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 'Create a new text prompt version in Langfuse' with a specific verb and resource, and the name 'create_text_prompt' distinguishes it from sibling like 'create_chat_prompt'.
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?
It explains immutability and label uniqueness, guiding when to use (to update content) and warning about label movement, but does not explicitly mention alternatives like 'update_prompt_labels'.
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 the full burden. It discloses that the tool treats observations with a specific schema_version and filters on metadata fields, which are behavioral traits beyond what the input schema shows. However, it does not mention performance characteristics or side effects, though the read-only nature is implied.
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 concise with three sentences, front-loading the purpose. Every sentence adds value: the first states the main function, the second clarifies router-neutrality, and the third explains the filtering logic. No wasted words.
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?
The description covers the tool's purpose and filtering behavior, and the input schema fully documents all 10 optional parameters. The presence of an output schema (not shown but indicated) may reduce the need to describe return values. However, it could briefly mention pagination or default output handling, but overall it is 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?
Given 100% schema description coverage, the baseline is 3. The description adds value by explaining that parameters are filters on 'route-decision metadata' and that the tool filters on 'fields inside observation metadata'. This clarifies the origin and purpose of the parameters beyond the schema descriptions.
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 finds 'generic agent route decisions emitted as Langfuse SPAN observations', specifying the verb 'Find', the resource ('route decisions'), and the context ('Langfuse SPAN observations'). It distinguishes itself from siblings by noting it is 'router-neutral' and filters on metadata schema version, providing a clear purpose.
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 implicitly guides usage by explaining the tool's router-neutral nature and the filtering criteria (metadata.schema_version). While it does not explicitly state when to use versus alternatives or provide exclusion criteria, it offers sufficient context for an agent to understand the tool's scope among siblings like find_exceptions or get_route_decision.
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. It explains behavior well by detailing parameters and return values based on output_mode. It does not disclose potential side effects or rate limits, but for a read operation the transparency is strong.
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 well-structured with 'Args' and 'Returns' sections. It is concise (approximately 6 sentences) and every sentence provides essential information without redundancy.
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 has 3 parameters, a clear output schema implied by the returns, and no nested objects, the description fully covers all aspects. It explains all output modes and addresses both optional and required parameters.
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?
The input schema has 100% coverage with descriptions for all three parameters. The description adds significant value by explaining the return values per output_mode, which goes beyond the schema's parameter definitions. Enumerations for output_mode are clearly described.
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 verb 'Get' and the resource 'detailed exception info for a trace/span'. It distinguishes from siblings like 'find_exceptions' and 'find_exceptions_in_file' by focusing on a specific trace/span rather than searching or file operations.
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 for retrieving exception details of a specific trace/span, and mentions optional filtering by span_id and output mode. However, it does not explicitly state when to use this tool versus alternatives like 'find_exceptions' or 'find_exceptions_in_file', nor does it provide when-not-to-use guidance.
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?
The description discloses that include_observations 'significantly increases response time' and explains output mode behaviors (compact, full_json_string, full_json_file). Since no annotations are provided, the description carries the full burden and does well, though it omits potential side effects like file overwriting or permission requirements.
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 Args, Returns, and Usage Tips sections. It is appropriately sized for a tool with three parameters and multiple output modes. Every sentence adds value, though it could be slightly shorter without losing 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 (three parameters, multiple output modes, no output schema), the description covers all essential aspects: purpose, parameter details with trade-offs, output format descriptions, and usage scenarios. It is self-contained and leaves no obvious gaps.
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 coverage is 100%, providing baseline 3. The description adds value beyond schema by grouping parameters into usage scenarios, offering context on how include_observations and output_mode interact, and clarifying the purpose of output_mode values beyond the schema's enum descriptions.
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 'Get detailed information about a specific session,' which is a specific verb+resource. It distinguishes from siblings like fetch_sessions (plural) and get_user_sessions by focusing on a single session identified by session_id.
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 usage tips provide clear guidance on when to use different parameter combinations (e.g., 'For quick browsing: use include_observations=False with output_mode=compact'), and the description implies this tool is for detailed single-session retrieval. However, it does not explicitly compare to alternatives or state when not to use it.
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, the description carries the full burden. It discloses that labels are added (not replaced), are unique across versions, and that the 'latest' label is managed by Langfuse. This provides valuable behavioral context beyond the schema. Could mention idempotency or behavior on duplicate labels.
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?
Three sentences, each providing essential information without redundancy. The verb and resource are front-loaded, and every sentence earns its place.
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?
For a simple tool with three straightforward parameters and an output schema, the description covers the key behaviors: additive labels, uniqueness, and Langfuse-managed 'latest'. It could add a note about idempotency but is largely complete.
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?
Even with 100% schema coverage, the description adds meaning: 'Provided labels are added to the version (existing labels are preserved)' and 'Labels are unique across versions'. This clarifies the behavior of the labels parameter beyond the schema's description.
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?
Clearly states the verb 'Update' and the resource 'labels for a specific prompt version'. It also distinguishes this as the only supported mutation for existing prompts, setting it apart from sibling tools like create_chat_prompt or create_text_prompt.
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?
Explicitly states it is the only supported mutation for existing prompts, providing context for when to use it. However, it does not explicitly list when not to use it or name alternative tools for other operations.
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?
Without annotations, the description details the return fields (input, expectedOutput, etc.), implying a read-only retrieval. However, it does not explicitly state read-only behavior or prerequisites. The structured return info compensates somewhat.
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 concise with front-loaded purpose, followed by a list of parameters and returns. No redundant sentences; every line adds value.
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?
With a simple 2-parameter tool and an output schema referenced (though not shown), the description covers all necessary aspects: purpose, parameters, and return format in detail. It is sufficient for correct tool invocation.
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 coverage is 100%, baseline 3. The description adds context for 'output_mode' by explaining the effect of each enum value (compact truncates, full_json_string returns full data), which is beyond the schema description. 'item_id' is clearly explained.
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 'Get a specific dataset item by ID', providing a specific verb and resource. It distinguishes from sibling tools like 'get_dataset' or 'list_dataset_items' by focusing on a single item retrieval.
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 implicitly indicates usage for fetching a single known item via ID, but does not explicitly compare with alternatives or state when not to use it. Sibling tools exist that list items, but no exclusion is provided.
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, the description carries full burden and discloses critical behavioral traits: the tool wraps the Langfuse v2 metrics endpoint (self-hosted instances may 404), recently ingested data can lag up to 10 minutes, and high-cardinality fields must be filters not dimensions. These go beyond basic function description and prevent misuse.
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 highly concise: three short paragraphs. The first paragraph states purpose and examples, the second wraps what it does, the third lists key limitations in bullet points. No filler; every sentence adds value.
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 (10 parameters, 2 required, no annotations, has output schema), the description is complete. It covers purpose, use cases, API wrapper details, constraints, and parameter guidance. The return format is not described, but an output schema exists, so that is acceptable.
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 coverage is 100%, so baseline is 3. The description adds overall context (e.g., high-cardinality warning) and notes 'Per-parameter contracts live in the Field descriptions above,' but does not add significant meaning beyond what the schema already provides for each parameter.
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 queries aggregated metrics (cost, latency, token usage, counts, score values) from Langfuse via its v2 metrics endpoint. It distinguishes from sibling tools by positioning it for analytics aggregation rather than raw observation fetching, as in 'instead of pulling raw observations and aggregating client-side.'
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 explicitly says 'Use this for analytics questions' and provides examples ('what did inference cost in the last 24h grouped by model'). It implies when not to use (raw observations) and notes limitations (cloud-only, ingestion lag, high-cardinality field constraints). However, it does not explicitly state alternatives or when to prefer sibling tools like fetch_observations.
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 fully carries the burden. It explains the performance impact of include_observations, the behavior of dropping observations to avoid IO, and the different output formats. This is comprehensive and beyond minimal.
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 Args, Returns, and Usage Tips sections. It is somewhat lengthy but every sentence adds value. Front-loads the purpose effectively.
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 presence of an output schema and rich parameter information, the description is complete. It covers return types, parameter behavior, and use cases without gaps.
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 coverage is 100%, so baseline is 3. The description adds significant context about performance trade-offs for include_observations and provides usage tips for output_mode, which go beyond the schema descriptions.
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 'Get a single trace by ID with full details.' It uses a specific verb and resource, and it distinguishes itself from siblings like 'fetch_traces' (multiple) and 'fetch_observation' (specific sub-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 Usage Tips for different combinations of parameters (e.g., 'For quick browsing: use include_observations=False with output_mode="compact"'). It does not explicitly say when not to use this tool vs alternatives, but the context is clear enough for selection.
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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