Langfuse MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation3/5
Many tools serve overlapping purposes (e.g., fetch_traces vs. search_trace_content vs. find_slow_traces; fetch_scores vs. list_scores_v2), but descriptions usually clarify the differences. However, the sheer number of similar tools can still confuse an agent.
Naming Consistency3/5
All names follow a verb_noun pattern, but the verb choices are inconsistent: fetch_, get_, list_, find_, search_, analyze_, compute_, and detect_ are used interchangeably for similar actions (e.g., fetch_traces vs. list_prompts vs. get_dataset). This mixed convention reduces predictability.
Tool Count2/5
With 58 tools, the server is heavily over-scoped compared to typical MCP servers (3-15). Many tools could be consolidated (e.g., multiple trace-querying variants, several annotation queue item operations), making the set feel bloated.
Completeness4/5
The tool surface comprehensively covers Langfuse's core domains: traces, sessions, observations, prompts, datasets, scores, comments, annotation queues, and analytics. Minor gaps exist (e.g., no trace update/delete, no direct observation creation), but the vast majority of workflows are supported.
Average 3.4/5 across 56 of 58 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
With no annotations, the description carries full responsibility for disclosing behavioral traits. It only states a generic action and offers no details about pagination, default sorting, the effect of the 'project' parameter, or response structure. No behavioral information is 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 sentence with no fluff, which is concise. However, it is under-specified for a tool with 3 parameters and an output schema, making it too brief to be appropriately informative. It is concise but lacking necessary structure.
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 tool has 3 parameters, no annotations, and an output schema, the description is highly incomplete. It only covers the basic list operation, omitting any explanation of parameter semantics, pagination behavior, or filtering options. The description is inadequate for guiding correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 3 parameters with 0% coverage (no descriptions), and the description does not explain any of them. The phrase 'in the project' vaguely hints at the project parameter but does not clarify its default or format. The description fails to compensate for the lack of schema parameter documentation.
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 function: 'List all annotation queues in the project.' It uses a specific verb ('list') and resource ('annotation queues'), and distinguishes from sibling tools like get_annotation_queue (single queue) and list_annotation_queue_items (items within a queue).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention the pagination parameters or the project filter, nor does it direct users to other tools for specific scenarios. No exclusions or alternative recommendations are given.
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 without revealing side effects, permission requirements, idempotency, or what happens if a dataset with the same name exists.
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 extremely short (two sentences), which is concise, but the second sentence ('metadata: JSON string') is a fragment and the overall structure is minimal. It is not well-organized for an agent to parse.
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?
Although an output schema exists, the description provides little context. It omits that 'name' is required and gives no semantics for 'project' or 'description', leaving the agent to infer too much for a create operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains 'metadata' as a JSON string but leaves name, project, and description unexplained. The partial coverage is insufficient for the four-parameter schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new dataset') with a specific verb and resource. It distinguishes from siblings like create_dataset_item based on the resource type, though it does not explicitly contrast with them.
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 list_datasets or create_dataset_item. There are no context clues, 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?
The description claims 'List all prompts' but the input schema includes default limit=50 and page=1, implying paginated results. This behavioral nuance is not disclosed. Beyond this, there is no mention of sorting, performance, or other traits. With no annotations, the description carries full burden and falls short.
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, front-loaded sentence with no wasted words. However, it is too sparse and could have included important context (like pagination) without sacrificing conciseness, so it doesn't reach a 5.
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?
While an output schema exists, the description lacks essential context about pagination, filtering by name/project, and the fact that 'all' may not literally mean all. For a tool with four optional parameters and a list operation, this is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for parameter meanings. It only vaguely references 'project' but says nothing about 'name', 'page', or 'limit'. This provides almost no value beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb ('List') and resource ('prompts') with scope ('in the project'). However, it does not explicitly distinguish this from sibling tools like 'get_prompt' or 'list_projects', missing the 'distinguishes from siblings' criterion for a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention filtering, pagination, or when to prefer a more specific tool like 'get_prompt'. This is essentially no usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention pagination behavior (despite page/limit parameters), filter combination semantics, ordering, or whether the operation is strictly read-only. 'Fetch' implies read-only, but the description lacks explicit confirmation or details about 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 that front-loads the core purpose. It is appropriately sized for a simple fetch tool, though it could have added more detail without becoming bloated.
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 having an output schema and 7 optional filter parameters, the description is minimal. It does not explain how filters interact, default pagination behavior, or any use cases. The output schema covers return structure, but the description lacks essential behavioral context for a tool with this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only says 'optional filters' without explaining any of the seven parameters (name, page, limit, project, trace_id, to/from_timestamp). Even though parameter names are somewhat self-explanatory, the description adds no meaning about how they are used or combined.
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 and resource ('Fetch scores/evaluations') and mentions optional filters, making the core action clear. However, it does not differentiate this tool from siblings like list_scores_v2 or get_score_v2, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It simply states that the tool fetches scores with filters, leaving the decision-making entirely to the agent.
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 indicates a write operation ('Update') and provides a label format hint, but it does not explain whether existing labels are overwritten, what happens if the version is missing, or any side effects. This is a significant transparency gap 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise and front-loaded: the first sentence states the core purpose, and the second clarifies the label format. Every word earns its place, though the brevity contributes to the lack of detail elsewhere.
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?
The tool has 4 parameters, no annotations, and an output schema (which covers return values). However, the description leaves critical behavioral and parameter context unclear (e.g., whether labels are replaced or merged, what project signifies). An agent cannot confidently invoke this tool without additional inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds value by clarifying that labels are comma-separated, but prompt_name, version, and project are left undocumented. For a 4-parameter tool, this is insufficient.
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 ('Update labels') and the target ('a specific prompt version'), which distinguishes it from sibling tools like get_prompt or create_text_prompt. However, it does not specify whether labels are replaced or appended, leaving some ambiguity about the exact effect.
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, no prerequisites, and no contextual signals. It simply states what it does, leaving the agent to infer usage scenarios on its own.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'remove', which implies a destructive action, but does not disclose irreversibility, required permissions, side effects, or error handling. This is minimal at best.
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, clear sentence with no filler words. It is appropriately concise for a simple deletion tool, though it lacks any supplementary context. It is not as under-specified as a tautology, so it earns a solid score.
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?
The tool has 3 parameters, no annotations, and an output schema (not described in the text). The description is too minimal to be contextually complete: it omits usage nuances, parameter roles, and behavioral consequences. The presence of an output schema is helpful, but the description does not leverage it to reduce ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description adds no information about the three parameters (item_id, queue_id, project). Since there are parameters, the description must compensate, but it does not mention any of them, leaving the agent to rely solely on parameter names.
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 'Remove an item from an annotation queue' uses a specific verb (remove) and resource (item from annotation queue), clearly distinguishing it from the sibling tool 'delete_annotation_queue_assignment'. It is unambiguous and focused.
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. The description does not mention prerequisites, exclusions, or comparisons to related operations like deleting a queue assignment. It merely states the action without contextualizing its 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'gets' details, implying a read operation, but does not state whether permissions are required, whether it is read-only, or any side effects. This is a minimal disclosure for a tool with no annotation support.
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, direct sentence with no unnecessary words. It is concise and front-loaded with the action verb, making it easy to parse.
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 an output schema, return values are covered, but the description still lacks context about when to use it among many similar session/trace tools. It does not explain the optional project parameter or how it differs from related tools, leaving the overall picture incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining parameter meanings. It does not mention session_id or project at all. While session_id can be inferred from 'session', the optional project parameter is completely unexplained, leaving ambiguity about how to use 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 the tool's function: retrieving full details of a session including traces. It uses a specific verb ('get') and resource ('session'), but does not explicitly distinguish it from sibling tools like fetch_sessions or get_user_sessions, so it falls slightly short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus the many sibling tools (e.g., fetch_sessions, fetch_trace). The description only states what it does, not under what circumstances it should be preferred or avoided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It reveals only that it updates status, but doesn't disclose whether it overwrites existing status, whether the item must exist, whether other fields are affected, or what the response contains. For a mutation tool, this is insufficient behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no wasted words. It fronts the key purpose and includes the most important semantic detail (status values). While more information would be helpful, the description is appropriately concise for a simple tool, and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has four parameters, is a mutation, and has no annotations, the description is not complete. It lacks information about side effects, required privilege, return value (though an output schema exists), and the meaning of non-status parameters. The output schema might cover return values, but the description itself doesn't address the mutation's context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It provides values for 'status' (PENDING or COMPLETED) but ignores 'queue_id', 'item_id', and 'project'. The other parameters are left to their self-explanatory names, which is not enough to understand the data model or constraints.
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 updates a queue item's status and explicitly lists the allowed values (PENDING or COMPLETED). This is specific enough to distinguish it from sibling create/delete tools, though it doesn't explicitly differentiate from other update-like tools. The verb 'Update' and resource 'queue item's status' are precise.
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. It implies usage for changing a queue item's status, but there are no explicit prerequisites, exclusions, or mention of alternative tools like create/delete. The context is minimally clear but lacks any comparative usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits, but it only lists return metrics. It does not explain how sessions are defined, how multi-turn vs single-turn is determined, how the time filters or grouping work, or whether it is read-only. The 'Returns' list is helpful but insufficient for understanding the tool's behavior beyond a high-level summary.
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 and front-loaded with the purpose, followed by a bullet-like list of return values. Every sentence contributes value, and it avoids fluff. It is well-structured for an analysis 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?
Given the tool's complexity (6 optional parameters, aggregation logic, and an output schema), the description is too thin. It does not explain parameter semantics, aggregation criteria, or how multi-turn depth is calculated. The output schema may provide return types, but the description fails to cover necessary behavioral context for a tool that performs non-trivial analysis.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage (all six parameters lack descriptions), and the tool description does not mention any parameters. While names like start_date and end_date are self-explanatory, parameters like group_by, tags, and project are ambiguous. The description adds no meaning beyond the schema's type/default information.
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 'Analyze multi-turn session behavior' with a specific verb and resource, and then enumerates the return metrics (session count, depth distribution, average traces, cost/latency, engagement). This distinguishes it from sibling tools like fetch_sessions (which fetches raw sessions) and get_session_details (which provides details for one session).
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 gives no guidance on when to use this tool versus alternatives like fetch_sessions, get_session_details, or get_user_sessions. It does not mention any exclusions or alternative tools. The usage context is implied by the output list, but there is no explicit direction for selection when a user has a session-related query.
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. It only states the action 'Create a new chat prompt version' without disclosing side effects, whether versions are versioned, permission requirements, or return values. The output schema exists but is not shown, so the lack of behavioral context is a significant gap.
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 with two short sentences, front-loading the core purpose. The messages explanation is directly useful, but it could be structured to include more parameter details without becoming 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?
Given the tool has 5 parameters and no annotations, the description is incomplete. It covers only the messages parameter and omits details about config, labels, and project, which are likely important for creating a chat prompt version. The presence of an output schema does not compensate for missing parameter semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for 5 parameters, and the description only explains 'messages: JSON string of [{role, content}].' The other four parameters (name, config, labels, project) remain undocumented, offering little semantic value beyond the raw 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 chat prompt version,' which specifies the exact action and resource. The mention of 'chat prompt' distinguishes it from sibling create_text_prompt, and the messages format hint adds useful specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives like create_text_prompt. The description implies usage through the name and messages format but does not state any context 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 carries the full burden. It does disclose the 'upsert' behavior, which is a useful behavioral trait, but it fails to explain how upsert works (e.g., which field acts as the key), what happens to existing items, or any side effects. It also omits error cases and return value details.
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 exceptionally concise, containing only two short sentences. Every part serves a purpose: the first states the core action, and the second clarifies parameter types. There is no redundancy or fluff.
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?
This is a mutation tool with 8 parameters, 2 required, and no schema descriptions, no annotations, and no behavioral details. The description only covers a fraction of what an agent needs to invoke it correctly. It fails to mention required parameters, the meaning of fields like item_id or metadata, or any constraints, making it far from complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its 8 parameters, so the description must compensate. It does clarify that 'input/expected_output' are JSON strings, which adds meaning beyond the raw 'string' type. However, the remaining 6 parameters (item_id, project, metadata, dataset_name, source_trace_id, source_observation_id) are left completely unexplained, making this inadequate for an 8-parameter tool.
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: 'Create or upsert a dataset item.' This is a specific verb+resource pairing that distinguishes it from sibling tools like 'create_dataset' (creates a dataset) and 'delete_dataset_item' (deletes an item). The addition of 'upsert' provides extra nuance about the tool's behavior.
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 guidance is provided. The description does not explain when to use this tool versus alternatives, nor does it mention any conditions or prerequisites. There are many sibling tools for datasets and items, but no cross-references or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It only says 'Delete' without explaining whether deletion is permanent, whether special permissions are required, or what side effects occur. No additional context is given.
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, short sentence with no redundant words. It is front-loaded and easy to parse, earning its place as a concise description.
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 low complexity, the description is under-specified for a mutation tool. It does not mention that deletion is likely permanent, what the project parameter does, or any prerequisites. The output schema exists, so return values need not be described, but the tool still lacks important contextual depth.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not compensate. It only mentions 'by ID' without clarifying that this maps to item_id or explaining the optional project parameter. The schema provides no descriptions, so the description adds almost no meaning to the 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?
The description 'Delete a dataset item by ID' clearly states the action (delete), the resource (dataset item), and the method (by ID). It distinguishes itself from sibling tools like get_dataset_item and create_dataset_item, which are different CRUD operations.
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 guidance is provided. The description only states what the tool does, with no mention of when to use it, when not to, or alternatives. There are no 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?
With no annotations, the description's verb 'Get' signals a safe read operation. It also implies a direct fetch of a single item. However, it doesn't disclose error behavior, permissions, or how the optional project parameter affects the 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?
The description is a single, front-loaded sentence with no unnecessary words. It's appropriately concise for a simple getter, though it omits details that could make it more useful.
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?
Although an output schema exists to cover return values, the description is incomplete for invocation: it doesn't mention that both queue_id and item_id are required, nor does it clarify the distinction from list_annotation_queue_items. The tool is simple, but the description under-specifies the usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only suggests that item_id is the identifier for the queue item. It says nothing about queue_id's role or the project parameter, so the agent must interpret the schema without additional context.
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 ('Get') and resource ('a single queue item'), and 'by ID' indicates retrieval by identifier. It differentiates from siblings like list_annotation_queue_items by emphasizing a single item, though it doesn't explicitly mention the need for both queue_id and item_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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention that it should be used for fetching a specific item instead of listing, nor does it state any 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 provided, the description carries the full burden of behavioral disclosure. It only mentions 'total error count' and a time period, but does not disclose what constitutes an 'error', whether the count is aggregated, how timestamp boundaries are handled, or the format of the response. This is minimal disclosure for a tool with no annotation support.
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, clear sentence with no wasted words. It is concise and front-loaded, but it lacks any structure that would break down usage or parameters. It is appropriately sized for a simple tool, though slightly under-specified.
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 presence of an output schema and three optional parameters, the description is incomplete. It does not explain how the count is computed, what filters apply (project), or the semantics of timestamps. The output schema helps, but the description fails to provide sufficient context for an agent to confidently invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions the 'time period' concept, which loosely maps to from_timestamp and to_timestamp, but does not explain the project parameter or the exact timestamp format/default behavior. The parameter names are somewhat self-explanatory, but the description adds little beyond what the schema names already convey.
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 gets a total error count within a time period, with a specific verb and resource. However, it does not explicitly differentiate from siblings like find_exceptions or detect_failures, which also deal with errors, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'within a time period' implies the primary use case is counting errors over a time range, which aligns with the from_timestamp and to_timestamp parameters. However, there is no explicit guidance on when to use this tool versus alternative error-related tools, nor any mention of 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?
There are no annotations, so the description must carry the behavioral disclosure burden. It only says 'Get a single score by ID' and does not mention error handling, return format, auth requirements, or side effects, leaving the agent without essential operational context.
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, front-loaded sentence with no superfluous words. It directly communicates the action and resource without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the tool's simplicity and presence of an output schema, the description leaves the 'project' parameter unexplained and provides no edge-case or error behavior. With no annotations, this lack of detail makes the description insufficient for fully correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain parameters. It references 'ID' for score_id but entirely omits the 'project' parameter, so the description fails to compensate for the missing parameter documentation.
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 function: 'Get a single score by ID via the v2 Scores API.' It uses a specific verb and resource, and distinguishes from siblings like list_scores_v2 or fetch_scores by focusing on a single score retrieval.
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 list_scores_v2 or fetch_scores. The description only defines the action without context on selection, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'List all datasets' without mentioning pagination behavior, how the optional 'project' parameter affects scope, or any other side effects or limitations. This is a significant gap for a tool with pagination parameters.
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 concise sentence and front-loaded with the action, but it is under-specified, lacking necessary parameter context to be fully useful. It is not verbose, but it omits essential details.
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?
While the tool is relatively simple and has an output schema, the description is incomplete because it fails to clarify the pagination parameters and the semantics of the optional 'project' parameter. With no annotations, the description should provide more context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate by explaining parameters. It does not mention 'page,' 'limit,' or explain that 'project' is optional and can be null. The only implicit reference is 'in the project,' which is ambiguous when the parameter is null. No added value over the bare 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 uses the specific verb 'List' with the resource 'datasets' and specifies the scope 'in the project.' This clearly distinguishes it from sibling tools like get_dataset (single dataset) and list_projects (list projects).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you want to enumerate all datasets, but it does not provide explicit guidance on when to use this tool versus alternatives like get_dataset or list_dataset_items, nor does it mention any exclusions 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 provided, the description carries the full burden of behavioral disclosure, but it only says 'Remove'. It does not disclose whether the operation is reversible, idempotent, requires specific permissions, or affects related resources (e.g., queue item status). This is a significant gap 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence with no redundant words. It is appropriately concise, though it could have been slightly more informative without becoming 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?
Even though an output schema exists, the description is too sparse for a delete operation. It lacks context about required parameters, side effects, or whether the deletion is permanent, making it incomplete for an agent to use confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation for the three parameters (project, user_id, queue_id). The schema itself lacks descriptions, so the meaning of each parameter is entirely undocumented. The description does not compensate for this gap.
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 ('Remove') and the resource ('reviewer assignment from an annotation queue'). It distinguishes the tool from its sibling 'create_annotation_queue_assignment' by focusing on removal rather than creation.
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 needing to remove an assignment, but it offers no explicit guidance on alternatives, exclusions, or when to avoid using the tool. For example, it does not mention whether this should be used instead of delete_annotation_queue_item or what happens if the assignment does not exist.
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' which implies a read operation, but does not disclose any behavioral traits such as error handling, authentication, rate limits, or whether it returns null/404 for missing IDs.
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, front-loaded sentence with no wasted words. It is appropriately sized for a simple get-by-ID operation.
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?
This is a minimal viable description. With an output schema present, return values are covered, but it lacks parameter semantics for 'project' and any usage guidance. It is adequate for a simple tool but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description only mentions 'by ID', implying comment_id but providing no meaning for the 'project' parameter. The description fails to compensate for the lack of schema documentation.
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 uses a specific verb 'Get' with a clear resource 'single comment' and scope 'by ID'. It clearly distinguishes from sibling tools like list_comments and create_comment.
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, no context or exclusions. It simply states what it does without any usage scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the action ('Create') without any details about side effects, required permissions, response behavior, or handling of duplicate names. The note about score_config_ids is a parameter detail, not a behavioral trait.
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, consisting of just two sentences: the first clearly states the tool's purpose, and the second provides a necessary parameter format note. There is no filler, redundancy, or unnecessary elaboration.
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?
Although an output schema exists, the description omits crucial context such as the meaning of the project parameter, any prerequisites, and how this tool fits into the broader annotation queue workflow. Given that annotations are absent and schema coverage is 0%, the description leaves significant gaps for a mutation tool with four parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for parameters, so the description must compensate. It explains only score_config_ids as 'comma-separated score config IDs to attach to the queue,' which is helpful, but leaves name, project, and description entirely unexplained. This is insufficient for a four-parameter tool with no other 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 annotation queue' with a specific verb and resource. This unambiguously distinguishes it from sibling tools like get_annotation_queue or create_annotation_queue_item, which operate on different aspects of the queue system. The purpose is immediately understandable.
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. There is no mention of prerequisites, no cross-references to related tools for creating queue items or assignments, and no indication of appropriate contexts. Usage is only implied by the tool's name and the action verb.
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 must disclose behavioral traits such as side effects, idempotency, permissions, or error behavior. It does not address any of these, merely stating the action without explaining what happens when an assignment already exists, whether the user must be valid, or any potential impacts on the queue.
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 filler words. It is front-loaded with the primary verb and resource, and every word contributes to its meaning. It is appropriately sized for a simple action.
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?
Although an output schema exists and reduces the need to explain return values, the description lacks usage guidelines, behavioral transparency, and full parameter semantics. For a mutation tool with no annotations and an unexpained project parameter, this falls short of being a complete guide for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implicitly maps user_id to 'reviewer' and queue_id to 'annotation queue', but the optional 'project' parameter remains unexplained. No additional details about parameter formats, relationships, or constraints are given, leaving a significant gap.
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 with a specific verb ('Assign') and identifies both the resource (annotation queue) and the actor (reviewer/user). This uniquely distinguishes it from sibling tools like delete_annotation_queue_assignment, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, expected call context, or conditions under which one might prefer a different tool. There is no explicit or even implied guidance beyond the basic action.
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 exist, so the description must carry the full burden of behavioral disclosure. It only says 'get metadata' and fails to mention whether the operation is read-only, requires specific permissions, what happens if the dataset does not exist, or what the metadata encompasses. This leaves the agent uncertain about side effects and error handling.
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 parse and front-loads the core purpose. It is appropriately sized for a simple getter, though it could afford a bit more context without becoming bloated.
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 metadata retrieval tool with an output schema present, the description provides the minimum viable purpose. However, it omits context about the optional project parameter and when this tool is preferred over siblings, making it incomplete for fully guiding an agent. Lack of annotations further limits behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the purpose of the two parameters. 'project' (optional, default null) and 'dataset_name' (required) are left entirely to their names, which is insufficient for an agent to correctly construct calls, especially the optional 'project' 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 action ('Get') and the resource ('metadata for a specific dataset'). It distinguishes from siblings like list_datasets by specifying 'a specific dataset' rather than listing all, and from get_dataset_item by targeting the dataset itself, not an 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?
The description provides no guidance on when to use this tool versus alternatives such as list_datasets or get_dataset_item. There is no mention of prerequisites, exclusions, or preferred scenarios, so the agent is left to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'List items in a dataset' and does not mention pagination defaults, project scoping, read-only behavior, or any other operational traits beyond what the tool name implies.
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, front-loaded sentence with no redundant words. It is appropriately concise for the tool's basic purpose, even though other dimensions reveal gaps in completeness.
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 existence of an output schema, the description does not explain pagination parameters, project scope, or how it fits among sibling tools. With four parameters and no annotations, the description is too minimal to be fully contextual for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description provides no meaning for the parameters page, limit, project, or dataset_name. The only hint is 'dataset' which maps loosely to dataset_name, but it adds no value beyond the input schema itself.
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 'List items in a dataset' uses a specific verb ('List') and resource ('items in a dataset'), clearly distinguishing it from siblings like list_datasets (lists datasets) and get_dataset_item (retrieves a 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 implies usage for enumerating items within a dataset, but it does not provide explicit guidance on when to use this tool versus alternatives such as get_dataset_item or list_datasets, nor does it mention any 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?
With no annotations provided, the description must carry the burden of behavioral disclosure. It adds some context by specifying object_type values ('TRACE' or 'OBSERVATION') and stating a status default, but the stated default (PENDING) contradicts the schema's default (null). No permissions, side effects, or return behavior are disclosed, which is a gap for a create 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?
The description is short and front-loaded with the primary purpose, followed by parameter notes. The structure is efficient, but the inaccurate status default and missing project documentation reduce the value of the second sentence. It remains concise and well-organized overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations, the description is incomplete. It fails to explain the project parameter, contains a schema contradiction, and omits any mention of return values or prerequisites. While an output schema exists, it does not excuse the lack of behavioral context and incomplete parameter semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning for object_type (enum-like values) and status (optional, though the default is incorrectly stated as PENDING vs null). However, queue_id, object_id, and project are not explained, and the schema has 0% coverage, so the description needed to compensate much more. The contradiction on status defaults further undermines the parameter guidance.
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 ('Add') and the resource ('trace or observation to an annotation queue'), making the tool's purpose immediately understandable. It also distinguishes itself from sibling tools like create_annotation_queue by specifying it adds an item to a queue.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as create_annotation_queue or create_annotation_queue_assignment. There is no mention of exclusions or prerequisites, leaving the agent to infer usage solely from the tool name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden of disclosing behavior. It only mentions two filter options and offers no details on pagination, sorting, response structure, or side effects (though 'fetch' implies read-only), leaving the agent to guess about default behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, with the first stating the core function and the following two giving concrete filter examples. Every sentence adds value and there is no fluff, making it appropriately concise and front-loaded.
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 8 parameters and no schema descriptions or annotations, the description is incomplete. It fails to clarify pagination semantics, how filters combine, or what the output contains, and the missing explanations for six parameters create a significant knowledge gap for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 8 parameters with 0% description coverage, so the description must compensate. It explains the purpose of observation_type and trace_id, but the other six parameters (name, page, limit, project, from_timestamp, to_timestamp) are left entirely undocumented, which is inadequate.
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 identifies the tool as 'fetch observations' and enumerates the observation types (spans, generations, events), providing a specific verb and resource. It does not explicitly differentiate from the sibling tool 'fetch_observation', relying on the plural form and the filter context to imply a batch/list operation.
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 concrete usage guidance: using observation_type='GENERATION' for LLM calls and trace_id to retrieve observations within a trace. These are actionable directions but the description does not mention when to prefer alternatives or when this tool is not appropriate, earning a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior. It implies a read-only listing operation, but it does not disclose pagination behavior, default ordering, or whether project filtering is included. These are notable gaps for a tool with five optional parameters, but the description is not misleading.
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 phrase that is front-loaded and free of unnecessary words. However, it is too terse to convey important details like pagination and project filtering, sacrificing completeness for brevity. It remains appropriately sized but could be expanded without becoming 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?
For a tool with five optional parameters, no annotations, and no parameter descriptions, this description is incomplete. It fails to explain pagination semantics, the meaning of the project filter, or the scope of 'sessions.' The output schema covers return values, but the input behavior is under-specified for reliable agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'time filters,' which covers from_timestamp and to_timestamp, but it says nothing about page, limit, or project parameters. This leaves the majority of parameters unexplained beyond their names and defaults.
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 uses the verb 'List' with the resource 'sessions' and mentions optional time filters, making its purpose obvious. It distinguishes itself from sibling tools like get_session_details (single session details) and analyze_sessions (analysis) by indicating a general listing operation, though it does not explicitly mention the project filter.
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 versus alternatives. The description does not mention exclusions, prerequisites, or scenarios where another tool like get_user_sessions or fetch_traces would be more appropriate. This lack of differentiation leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it does not mention any behavioral traits such as error handling (e.g., 404 on missing item), whether project is required for tenant isolation, or any data completeness guarantees. For a simple getter, the absence of these details leaves a notable gap.
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 clean sentence with no filler or redundancy. The description is front-loaded with the action and object, making it immediately scannable. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a low-complexity getter, and an output schema exists, which likely handles return value documentation. However, the description is minimal: it leaves the 'project' parameter unexplained, gives no usage context, and does not mention any edge cases. This is minimum viable but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It loosely maps 'ID' to the item_id parameter, but it does not explain the format of item_id or the purpose of the optional 'project' parameter. The description adds minimal value beyond the schema, naming only one of two 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?
The description 'Get a single dataset item by ID' uses a specific verb ('Get') and resource ('dataset item'), clearly distinguishing it from siblings like list_dataset_items (list) and get_dataset (dataset-level fetch). The mention of 'single' and 'by ID' makes the scoop explicit and unambiguous.
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 list_dataset_items or get_dataset. There are no stated prerequisites (e.g., needing the item ID from a list operation) or exclusions. The description simply states the action without 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 provided, the description carries some burden for behavioral disclosure. It adds useful context about the content of the response (managed + custom models), but does not explicitly state read-only behavior, pagination implications, or other operational traits. For a simple listing tool this is partial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no redundancy. The first sentence states the action and resource, and the second adds return-value detail. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema exists, so return values are covered outside the description. However, the lack of parameter explanations and usage alternatives leaves the description incomplete for a tool with three optional parameters, including a project filter that could be ambiguous.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the three parameters (page, limit, project). The agent gets no additional meaning beyond the raw schema names/defaults, so it is left to guess the purpose of 'project' and pagination semantics.
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 uses a specific verb ('List') and identifies a clear resource ('model definitions in the Langfuse models registry'). It further clarifies the scope by stating it returns both Langfuse-managed models and custom configs, distinguishing it from single-model retrieval tools like get_model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives (e.g., get_model). The description does not mention any exclusions, prerequisites, or context for the project parameter, leaving the agent without direction on 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 must disclose behavioral traits, but it only says 'Fetch a specific prompt by name.' It does not mention side effects (likely none since fetch is read-only), behavior on missing prompts, error handling, precedence between version and label, or any permissions. This is a significant transparency gap for a no-annotation tool.
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 two short sentences with the essential action front-loaded: 'Fetch a specific prompt by name.' No redundant words or extra information, so every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the presence of an output schema, the description is too sparse for a tool with 4 parameters and zero schema coverage. It omits 'project', doesn't explain how version and label interact, and offers no usage context relative to siblings. The gaps in parameter semantics and behavior make this incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some meaning by identifying 'name' as the key and indicating label/version are optional, but it omits the 'project' parameter entirely. With schema description coverage at 0%, the description does not fully compensate for the undocumented parameters, leaving the purpose of 'project' unclear.
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' – a specific verb (fetch), resource (prompt), and identifier (name). It distinguishes from list_prompts and get_prompt_unresolved by emphasizing a specific prompt rather than listing all prompts or fetching unresolved ones.
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 phrase 'Fetch a specific prompt by name' implies use when retrieving one prompt, and 'Optionally specify version or label' indicates optional selection criteria. However, it never explicitly mentions when not to use this tool or names alternative tools like list_prompts, so guidance is only implied.
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 carries the burden of explaining behavior. It adds a constraint that object_id requires object_type and lists allowed values, but it does not disclose default pagination (page/limit), the effect of leaving filters empty, or how the response is structured. The output schema exists, but behavior beyond that is underexplained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with the core purpose. The additional lines about object_type and object_id are efficient and add necessary constraints without waste.
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 6 parameters, no annotations, and an output schema, the description is incomplete. It fails to mention pagination behavior, project/author filtering, or how to call the tool effectively beyond object_type. The complexity is moderate, but the description leaves significant gaps in usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains object_type and object_id dependency, but ignores page, limit, project, and author_user_id. This leaves most parameters undocumented, so the description only partially compensates for the schema gap.
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 function with a specific verb ('List') and resource ('comments'), and specifies the types of objects comments can be attached to. This distinguishes it from siblings like create_comment and get_comment.
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 by listing valid object types and indicating a dependency between object_id and object_type, but it does not explicitly state when to use this tool over alternatives or mention any exclusions. There is no guidance on pagination or project filtering, so the context is limited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not describe side effects, authorization requirements, reversibility, or response behavior beyond the implicit fact that a comment is created. The note about project_id and content constraints adds parameter detail, not behavioral 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 concise and front-loaded with the primary purpose. Parameter details are presented in a structured list, though line breaks are used somewhat informally. Every sentence adds relevant information without fluff.
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, so return values are covered. However, with 6 parameters, zero annotation coverage, and no usage guidelines, the description is not fully complete. It provides some parameter constraints but leaves gaps in behavioral context and alternative 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?
Schema description coverage is 0%, so the description must compensate. It explains project_id (distinct from MCP project argument), object_type (with enum values), and content (markdown, 5000 char limit). However, object_id, author_user_id, and project remain undocumented in the description, leaving three of six parameters semantically uncovered.
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 and resource: 'Create a comment on a trace, observation, session, or prompt.' This specific verb+resource structure distinguishes it from related sibling tools like get_comment and list_comments by emphasizing the creation aspect.
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 intended use (to create a comment) is implied, but the description does not explicitly state when to use this tool over alternatives or provide exclusion criteria. Sibling tools like get_comment and list_comments exist, but no cross-referencing 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 must convey behavioral context. It only states the action 'Get' with no mention of error handling, required permissions, or potential edge cases (e.g., not found behavior). For a read operation, it implies non-destructiveness, but fails to disclose anything beyond the basic action.
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 redundant words. It front-loads the action and resource clearly. For a simple retrieval tool, this level of brevity is appropriate and all content earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only two parameters and an output schema exists, so the description need not explain return values. However, with no annotations and zero schema description coverage, the description should provide more context on the project parameter and usage scenarios. It is minimally complete but lacks depth.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero property descriptions (0% coverage), so the description must compensate. It only clarifies `queue_id` implicitly via 'by ID', but leaves `project` completely unexplained. With two parameters and no additional info, the semantics are partially covered at best.
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 "Get a single annotation queue by ID" clearly specifies the verb (Get), resource (annotation queue), and scope (by ID). It naturally distinguishes from sibling tools like list_annotation_queues (which lists 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 via 'by ID', suggesting it is used when a specific queue ID is known. However, it does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions or prerequisites. The guidance is foundational but not explicit.
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 carries the full burden of behavioral disclosure. It discloses that status can be PENDING or COMPLETED, but does not clarify that status is optional, nor does it mention pagination behavior or the project filter. The read-only nature is implied by 'List' but not explicitly stated.
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 front-loads the primary action and includes one additional relevant detail. There is no redundancy, filler, or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has moderate complexity with 5 parameters, but the description is minimal. It fails to mention pagination defaults, optional status behavior, and project filtering. While an output schema exists, the description still lacks important context that would help an agent use the tool correctly, such as what happens when status is not provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only elaborates on the status parameter, providing its allowed values. It does not explain the required queue_id, nor does it provide meaning for page, limit, or project beyond what their names imply. This is insufficient compensation for the complete lack of parameter 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's purpose: 'List items in an annotation queue.' It uses a specific verb ('List') and resource ('annotation queue items'), and even provides the status filter values. This distinguishes it from sibling tools like list_annotation_queues, which list queues rather than items.
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 explicit guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The intended use must be inferred from the tool's name and general context. There is no mention of alternatives or specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the operation and a labels format, but does not disclose any behavioral traits such as whether this creates a new version of an existing prompt, whether project is required, what happens to existing prompts with the same name, or any side effects. This is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with the primary purpose. The second sentence adds a necessary detail about labels. Every word earns its place with no redundant 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 the tool has 5 parameters, a create operation, and no annotations, the two-sentence description is insufficient. It does not explain the purpose of 'config' or 'project', nor does it clarify versioning behavior. Although an output schema exists, the overall context remains incomplete for an agent to fully understand the tool's scope and requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It only provides semantic meaning for the 'labels' parameter ('comma-separated'), while 'config' and 'project' are left unexplained. 'name' and 'prompt' are self-evident from their names, but the description fails to add value for the other 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?
The description clearly states the tool's function with a specific verb and resource: 'Create a new text prompt version.' It distinguishes itself from the sibling tool create_chat_prompt by specifying 'text prompt' rather than 'chat prompt', and the mention of labels hints at a separate functionality from update_prompt_labels.
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 a clear context for use ('Create a new text prompt version') and a formatting hint for labels, but it does not explicitly mention when not to use it or provide alternatives. While the sibling tool create_chat_prompt exists, the description does not guide the agent to use that for chat prompts, so it stops short of full 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?
No annotations are provided, so the description carries the full burden. It discloses that data is pre-aggregated server-side and that the tool returns totals, implying a read-only operation. But it lacks explicit statements about permissions, rate limits, date range constraints, or side-effect-free behavior. It adds some context but not enough for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, tightly focused and front-loaded with the core function. The first sentence states what the tool does, and the second provides usage rationale. No redundant information or filler.
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?
The description covers the core function and use case well, but it leaves the five parameters completely unexplained. With no schema descriptions and all parameters optional, an agent has no guidance on how to construct a valid request. The output schema may document return values, but the input side is severely under-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention any of the five parameters (tags, project, user_id, from_timestamp, to_timestamp). It doesn't explain what these filters do, what formats are expected, or how they affect the rollup. The description completely fails to compensate for the schema's lack of parameter documentation.
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 fetches Langfuse's pre-aggregated daily metrics rollup, returning per-day totals. The verb 'Fetch' plus the specific resource 'daily metrics rollup' makes the purpose unambiguous, and it distinguishes itself from sibling tools by focusing on pre-aggregated server-side 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?
It provides explicit usage context: use when wanting high-level trends over a long window, and notes it's faster than aggregating traces yourself. However, it doesn't name specific alternative tools or state when not to use it, so it stops short of full exclusionary guidance.
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?
There are no annotations, so the description carries the full burden. It only says 'List scores', implying a read-only operation, but does not disclose pagination behavior, default sorting, rate limits, or any side effects. The description adds no meaningful behavioral context beyond what 'List' implies.
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 appropriately short and front-loaded. The first sentence states the purpose, the second differentiates from fetch_scores, and the remaining lines add essential parameter formatting notes. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 20 parameters, no annotations, and no parameter descriptions in the schema, the description is severely under-specified. While an output schema exists, the description fails to explain many filter options, pagination semantics, or usage context. It only touches on a few parameters, leaving most behavior unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 20 parameters with no descriptions, and the description clarifies only three: trace_tags, score_ids (comma-separated values) and operator (comparator). This covers only a small fraction of the parameters, leaving the rest without meaningful semantics. The description provides minimal value beyond the raw 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 'List scores using the v2 Scores API' and differentiates it from the sibling tool 'fetch_scores' by noting it has 'Richer filters'. This provides a specific verb, resource, and a distinguishing feature, fully meeting the criteria for purpose clarity.
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 mentions 'Richer filters than fetch_scores', which implies when to use this tool (when needed richer filtering) and identifies an alternative. However, it does not provide explicit exclusion criteria or other alternative tools, so it falls slightly short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral details. It only says 'Write scores back', indicating mutation but not whether scores are overwritten, if any permissions are needed, or how errors are handled. The example is usage-related, not behavioral, leaving significant 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 concise and front-loaded with the core action. It includes the essential trace_ids format and a useful example, with no wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter write tool with no annotations and 0% schema coverage, the description is too sparse. It fails to cover key parameters like project and comment, and does not clarify side effects or the scoring semantics beyond the example. The presence of an output schema doesn't mitigate the missing parameter guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only clarifies 'trace_ids: comma-separated', while the other parameters (score_name, score_value, comment, project) are not explained. The example hints at score_name ('needs-review') but does not systematically define 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 states 'Write scores back to Langfuse traces' with a specific verb ('write') and resource ('Langfuse traces'). The example 'score failing traces with needs-review' clarifies the use case and differentiates it from read-only sibling tools like fetch_scores or fetch_traces.
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 explicitly says 'Use this after analysis' and provides a concrete example, giving clear temporal context. However, it does not name alternatives or state when not to use, slightly lowering the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds value by detailing the return fields ('input/output, token usage, model, latency, and cost'), but it does not disclose error handling, authentication needs, or any side effects. The verb 'Get' implies a read-only operation, but edge cases are not addressed.
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 two sentences long, front-loaded with the core function, and every sentence adds value. It efficiently communicates the action and the key return fields without unnecessary verbosity.
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 retrieval tool, the description is mostly adequate, especially given that an output schema exists. However, the 'project' parameter is unexplained, and there is no disambiguation from the similarly named 'fetch_observations' (plural). These gaps make the description less complete than it could be.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for parameter meaning. The description explains 'observation_id' implicitly ('by ID') but completely omits the 'project' parameter, leaving its purpose unknown. This is a significant gap for a tool with only two 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?
The description clearly states the tool's function: 'Get a single observation by ID.' This specifies a verb ('Get'), a resource ('observation'), and a scope ('by ID'). It also distinguishes from the sibling tool 'fetch_observations' (plural) by emphasizing 'single observation.'
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 when to use the tool (when you have an observation ID and need full details) but does not explicitly mention alternatives or when not to use it. It lacks references to sibling tools like 'fetch_observations' for listing multiple observations, so the usage guidance is only implied.
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 present, so the description must carry the full behavioral burden. It discloses that the tool returns the trace with all observations and highlights errors, which implies a read-only operation. However, it does not mention the behavior of the optional 'project' parameter, potential filtering, or any side effects beyond returning data.
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 two concise sentences, front-loaded with the core purpose. No fluff or redundant details are present; every sentence contributes meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description does not need to detail return values, but it does anyway. However, it lacks context around the 'project' parameter and does not distinguish this tool from the many sibling trace-focused tools. For a simple get tool, it is adequate but leaves room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the description does not compensate by explaining the 'project' parameter at all. It only alludes to 'trace_id' via 'specific trace.' Thus, the description adds minimal meaning beyond the schema for one of the two 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?
The description clearly states the verb ('Get'), the resource ('full exception/error details'), and the scope ('for a specific trace'). It also adds a meaningful output detail ('Returns the trace with all observations, highlighting errors'), which differentiates it from siblings like get_error_count or find_exceptions.
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 a use case (obtaining complete error details for a specific trace) but does not explicitly state when to choose this over alternatives like fetch_trace or find_exceptions. No exclusions or alternative tool mentions are provided.
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 carries full behavioral burden. It discloses that it returns 'extracted query text with metadata' and mentions the exclude_internal filter, which is useful. But it omits core behavioral traits like read-only status, pagination, default limit, or what metadata precisely appears. The lack of side-effect disclosure is acceptable for a list tool, but the description could add more operational context.
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: a one-sentence purpose, a block of example questions, then parameter-specific notes. It is not overly verbose, and every section earns its place. The example list could be trimmed slightly, but overall it is efficient and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 11 parameters and no schema descriptions, the description is incomplete. It provides good usage context but omits explanations for over half the parameters, making it hard to understand the full range of filtering and grouping options. The output schema helps define return shape, not parameter semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains domain, group_by, and exclude_internal, but leaves 8 other parameters (name, tags, limit, project, user_id, end_date, start_date, time_range) with no explanation. The description adds meaning to only a subset of the parameters, failing to fully compensate for the empty 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 opens with a specific verb and resource: 'List user queries extracted from trace inputs.' This clearly states what the tool does and distinguishes it from sibling tools like list_users (which lists users) or fetch_sessions (which lists sessions). Concrete example questions ('What are merchants asking?') reinforce the intended 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?
It provides clear usage context with example queries and explicit guidance on using the domain parameter: 'Use this when the user asks about a company/org by name instead of a specific user email.' This helps with parameter choice. However, it does not explicitly name alternative tools or exclusionary conditions, so it's not a perfect 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the sorting order, default group_by, and domain extraction behavior, but does not mention permissions, side effects, or performance characteristics. This is adequate but incomplete.
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 bullet points for group_by options, making it scannable. It is slightly verbose for the information provided but not excessively long.
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?
With 8 optional parameters and an output schema present, the description covers the core grouping behavior and return fields, but omits details on filtering parameters and time range semantics. This leaves some gaps for a tool with this many parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains group_by and exclude_internal, but leaves tags, top_n, project, dates, and time_range without explanation. Only a quarter of parameters receive clarification.
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 aggregates trace metrics by user group, listing specific output metrics and group_by options. It distinguishes itself from sibling analytics tools by specifying the grouping capability and output fields.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use each group_by option and notes the exclude_internal caveat for domain grouping. However, it does not explicitly mention alternatives or when not to use this tool, so it falls short of full explicit 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?
With no annotations provided, the description carries the burden. It explains the calculation logic and parameter behavior for group_by, bucket_by, and score_name, but it does not confirm whether the operation is read-only, address edge cases, or describe interactions among date parameters (start_date, end_date, time_range).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using only three short sentences. It front-loads the core purpose and formula, and every sentence adds value. No superfluous content or redundancy.
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 presence of an output schema covers return-value details, but the description is incomplete on parameter semantics for half the parameters. It also lacks context on data prerequisites (e.g., how feedback scores are sourced) and default filtering behavior. For a tool of moderate complexity, it is adequate but has 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?
The schema has 0% description coverage, so the description must compensate. It does add meaningful semantics for group_by, bucket_by, and score_name (e.g., valid values like 'domain', 'week', 'day'), but it leaves tags, project, start_date, end_date, and time_range without explanation. This partial coverage is helpful but incomplete for a tool with 8 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?
The description clearly states the tool computes accuracy from feedback scores and provides the exact formula (correct / (correct + incorrect)). This specific verb+resource distinguishes it from sibling analysis tools like compute_token_percentiles or analyze_latency.
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 by showing typical values for group_by and bucket_by (e.g., 'week', 'day' for trends), but it does not explicitly state when to use this tool versus alternatives like fetch_scores or aggregate_by_group. No exclusions or alternative tool names are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. The 'Find' verb implies a read-only operation with no destructive side effects, and the error status filter is stated. However, it does not disclose other behavioral traits such as filtering behavior, pagination, or how errors are identified. The output schema covers return values, but this is partial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action and an explicit alternative. Every word earns its place, and there is no redundancy or irrelevant 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?
The description is too terse for a tool with four parameters, no schema descriptions, and no annotations. It fails to explain parameter semantics or mention other relevant sibling tools like get_error_count or fetch_observations, which could be alternatives for error-related queries. The output schema exists, but the description alone is insufficient for confident invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description mentions none of the four parameters (limit, project, from_timestamp, to_timestamp). It adds no meaning beyond parameter names, forcing the agent to guess their roles. This is a critical gap given the schema provides no 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's function: 'Find observations with error status.' The verb 'Find' and the specific resource 'observations with error status' make the purpose unambiguous. It also distinguishes itself from detect_failures by pointing to that tool for LLM output quality issues, which helps differentiate among siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides an alternative tool and a when-not-to-use scenario: 'Use detect_failures for LLM output quality issues instead.' This gives the agent direct selection guidance, meeting the criteria for explicit usage guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the source field, grouping options, and output structure, which is helpful. But it lacks details on parameter interactions, default behavior, or edge cases, leaving some behavioral ambiguity.
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, front-loaded with the primary purpose, and uses three short sentences to convey the main functionality, grouping options, and return values. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 optional parameters) and lack of annotations, the description is only partially complete. It states return values but does not explain how filters, time ranges, or grouping interact. The existence of an output schema helps, but the description still leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains possible values for group_by and bucket_by, but leaves tags, project, start_date, end_date, and time_range entirely unexplained. This is insufficient for the tool's 7 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?
The description clearly states the tool computes a cost breakdown from the Langfuse totalCost field. It distinguishes itself from sibling analytics tools by focusing specifically on cost, and mentions grouping options and return values, making the purpose fully explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (when a cost breakdown is needed) and explains grouping capabilities. However, it does not explicitly mention alternatives or exclusions, so it falls just short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses a notable behavioral trait: the tool fetches traces first and extracts unique sessions, implying a potentially expensive operation and deduplication. However, it does not mention other behavioral aspects like sorting, time-range handling, or whether user_id is mandatory (though required in schema). Overall, it adds some value but lacks rich context.
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 two concise sentences. The first sentence is a clear, front-loaded purpose, and the second adds mechanism and intent. There is no filler or redundancy.
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 5 parameters, no annotations, and an output schema (which likely describes return values), the description covers the core purpose but omits key context like how time-range filters or limit apply. The mechanism of fetching traces is valuable, yet the tool's parameter-driven behavior is not explained. This is adequate but clearly incomplete for a parameter-rich tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the 5 parameters except implicitly aligning 'specific user' with user_id. It does not describe the meaning of limit, project, from_timestamp, or to_timestamp, so the agent must rely purely on parameter names. Since the description must compensate for the lack of schema descriptions, this is a significant gap.
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 sessions for a specific user' with a specific verb and resource, and adds implementation detail ('Fetches user's traces and extracts unique sessions') that distinguishes it from sibling tools like fetch_sessions (generic all sessions) or get_session_details (single session). The phrase 'unique sessions' also implies deduplication, further clarifying its 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 gives a clear usage context: use this for a specific user to understand their interaction history. It implicitly differentiates from generic session fetchers by focusing on a user scope, but it does not explicitly state when NOT to use it or name alternative tools. This is clear context without exclusions, matching a 4.
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?
Since there are no annotations, the description carries the full burden. It discloses the underlying mechanism (queries metrics API, groups traces by userId) and a default behavior (last 30 days). However, it does not explain ranking order, exclusion of zero-trace users, or pagination limits, leaving some behavioral ambiguity.
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 with the primary purpose front-loaded. Every sentence adds meaning: the first defines the tool, the second explains the implementation context, and the third gives a usage default. No redundant content.
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, so return values are covered. Yet the description omits details about parameter handling (top_n, project) and does not note whether the result is capped or sorted. It provides a workaround and alternative, but lacks some behavioral context necessary for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only clarifies the timestamp parameters with the default 30-day window, but does not explain top_n (beyond 'top users') or the project parameter semantics, leaving two of four parameters under-specified.
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 a specific action ('List users') and resource ('users in the project') with a concrete output scope ('per-user trace counts', 'top users by trace count'). It also distinguishes the tool from alternatives by explaining that Langfuse lacks a dedicated /users endpoint and that this queries the metrics API.
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 gives an explicit alternative for a scenario: 'Use fetch_traces(user_id=...) for a specific user's traces.' It also clarifies the default time range when timestamps are omitted. However, it does not list exclusions or when not to use this tool compared to other similar siblings like get_user_sessions.
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 transparency burden. It discloses the underlying behavior ('Fetches generation observations to get per-trace token counts') and the performance implication (per-trace fetching can be slow). This is valuable beyond the official title and adds context about how the tool works, though it omits details about return structure and edge cases.
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 compact and front-loaded with the core purpose. Each sentence adds value: the function, the data source, the grouping option, and a performance note. No wasted words.
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?
Although an output schema exists (so returns needn't be described), the tool has seven parameters, no annotations, and 0% schema description coverage. The description only touches on two parameters and omits the rest, making it incomplete for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides almost no parameter semantics. It mentions 'domain' as an example for group_by and 'last_7_days' as a time_range value, but leaves tags, project, start_date, end_date, and percentiles undocumented. Given seven parameters, this is inadequate compensation for the lack of 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's function with a specific verb and resource: 'Compute token usage percentiles (TP50/TP90/TP95/TP99) across traces.' This distinguishes it from sibling analysis tools like estimate_costs or analyze_latency by focusing on token usage percentiles.
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?
Provides clear usage context, including the note about performance ('can be slow for large date ranges') and a recommendation to use 'last_7_days or smaller for real-time results.' It also mentions optional grouping by domain. However, it does not explicitly name alternatives or when-not-to-use conditions, stopping short of a full guidelines treatment.
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 the default threshold (256K tokens), explains the difference between aggregate and per-generation checking, and states the intended outcome (catching overflow). It does not describe return format or other potential side effects, but for a detection tool, this is reasonably transparent.
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 and front-loaded, with the main purpose in the first sentence followed by specific, relevant details. There is no redundant or superfluous text.
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?
The tool has 7 parameters and no annotations, and the description covers only the core detection logic. It lacks crucial information about filter parameters (tags, project, dates) and does not clarify how these interact. While output schema exists, the description is incomplete for a tool with this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains 'threshold' and 'check_per_generation', but leaves the other five parameters (tags, project, start_date, end_date, time_range) entirely unexplained. Their names are somewhat self-explanatory, but the semantics of date filters and time_range are not addressed.
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 function: scanning for traces where token usage exceeds a context window threshold. It uses specific verbs and resources, and the unique focus on context window overflow distinguishes it from sibling tools like detect_failures or find_slow_traces.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (to catch context window overflow causing degraded performance or truncation) and explains the default threshold and the per-generation check flag. However, it does not explicitly mention alternatives or exclusion criteria, so it falls slightly short of a 5.
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 transparency burden. It discloses a key behavioral trait: 'Returns compact metadata (no input/output content).' This hints at read-only semantics (fetch) and avoids the expectation of full trace details. It does not mention auth or rate limits, but given the simple read nature, the disclosure is sufficient for a 4.
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 compact, front-loaded with the main action, and every sentence serves a purpose. It provides essential guidance in three sentences without padding, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description clarifies the tool's niche among many siblings and notes the response output, it leaves the 10 parameters completely unexplained. An agent would not know how to construct queries (e.g., timestamp formats, allowed order_by values, or filter combinations). Output schema exists, so return values are covered, but parameter usage is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and none of the 10 parameters (name, tags, limit, offset, project, user_id, version, order_by, to_timestamp, from_timestamp) are explained in the description. The description adds no meaning to these parameters, failing to compensate for the lack of 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 starts with a specific verb+resource: 'Fetch traces from Langfuse.' It also explicitly states the response scope ('compact metadata, no input/output content'), distinguishing it from fetch_trace which returns full details. This clearly differentiates from sibling tools like fetch_observations and fetch_sessions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use and when-not-to-use guidance: 'For analytical questions... use analytics tools instead. For user queries, use list_user_queries. For keyword search, use search_trace_content. Use fetch_trace(trace_id) to get full details.' This names specific alternatives, making selection easy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The verb 'Get' implies a read-only behavior, and the description discloses the returned content (pricing, tokenizer config), but it does not address edge cases like missing IDs, permission requirements, or error behavior. This is adequate but not rich.
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, front-loaded sentence with no wasted words. Every part adds value: 'single', 'by ID', and the included components.
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 tool is simple with only 2 parameters and an output schema present, so the description does not need to detail return values. However, the project parameter is left unexplained, and there is no pointer to list_models for enumeration, so it is not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only clarifies the model_id parameter indirectly through 'by ID.' The project parameter is not explained at all, leaving the agent to guess its purpose or whether it is needed.
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 uses the specific verb 'Get' with the resource 'single model definition' and scopes by ID, clearly distinguishing this tool from sibling list_models. It also adds context about what the definition includes (pricing, tokenizer config).
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 makes it clear that this tool is for retrieving one model by ID, which implies when it should be used. However, it does not explicitly mention alternatives or exclusion criteria, such as 'use list_models to see all models.'
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 carry the behavioral burden. It discloses that the search is case-insensitive, and that it can target inputs, outputs, or both via the search_in parameter. However, it does not mention pagination, return format, auth requirements, or rate limits, leaving the agent with incomplete behavioral expectations. It provides some transparency but not enough to fully anticipate the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: it states the purpose in the first sentence, gives examples in the second, and then lists parameter explanations. Every sentence serves a purpose, with no fluff.
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 9 parameters and no annotations, the description adequately covers the core usage pattern but omits explanations for many optional filters. Since an output schema exists, return value documentation is not required, but the lack of guidance on limit, tags, and date ranges means an agent may not exploit the full capability. It's a well-structured but incomplete description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning for three key parameters (domain, search_in, query) with explicit explanations and examples. However, with schema description coverage at 0%, it leaves six other parameters (tags, limit, project, start_date, end_date, time_range) undocumented, forcing the agent to rely on parameter names and defaults. This partial coverage raises it above baseline but still leaves significant gaps.
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 opens with a clear statement ('Search trace inputs and outputs for keywords') and provides three concrete example questions. This verb+resource+scope distinguishes it from sibling tools like fetch_traces or analyze_sessions, whose purposes are different. The examples reinforce the intent.
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 to answer:' followed by specific use cases, giving clear context for when to invoke the tool. It does not mention alternatives or exclusion cases, but the examples make the intended usage obvious. This earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It states the action ('Get') but does not explicitly say it is read-only, non-destructive, or what happens on failure. It lacks details like auth requirements or response structure, which is a gap for an agent needing safety expectations.
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 two sentences, front-loaded with the action, and contains no filler. Every word earns its place, and the second sentence adds immediate value by explaining why an agent would call it.
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 zero-parameter input and the presence of an output schema (which covers return values), the description is largely complete. It could briefly mention that the schema describes traces, observations, or other entities, but the current wording is adequate for an agent to understand the tool's role within the broader context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds no parameter-specific details because none exist, which is appropriate. The 'fields and data types' phrasing hints at the output but not parameters, which is fine.
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 function: 'Get the data schema for the Langfuse project.' This is a specific verb+resource combination that distinguishes it from siblings like get_prompt or fetch_traces. The scope is unambiguous.
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 adds 'Useful for understanding available fields and data types,' which implies the intended use case. While it does not explicitly name alternatives or when not to use it, the context is clear for a schema retrieval tool with no obvious competing sibling.
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 burden of behavioral disclosure. It transparently states the detection mechanism (pattern matching on specific failure signals), the inclusion of feedback score = 0 occurrences, and the exclusion of Python exceptions. This gives meaningful behavioral context beyond a simple one-liner.
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 focused sentences, front-loaded with the main purpose. The follow-up sentence clarifies the detection criteria, and the final sentence provides the key alternative. Concise and free of filler.
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?
Despite the 0% schema parameter coverage and lack of annotations, the description effectively frames the tool's purpose, detection logic, and boundary with find_exceptions. An output schema exists, so return values need not be described. It misses some contextual details like how to constrain by time or project, but overall is sufficiently complete for a detection tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for all 8 parameters, yet the description adds no parameter-specific semantics. It mentions 'feedback score' conceptually but doesn't explain how parameters like tags, project, date ranges, group_by, or max_examples affect behavior. The description fails to compensate for the lack of 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 uses a specific verb ('detect') with a clear resource ('LLM output failures') and explains the method (pattern matching and feedback scores). It explicitly distinguishes itself from find_exceptions by clarifying it catches quality failures, not Python exceptions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-not guidance: 'This catches LLM quality failures, NOT Python exceptions.' Directly names the alternative tool: 'Use find_exceptions for code errors.' This clearly tells the agent when to choose this tool over a sibling.
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 both traces are fetched in parallel and that a summary of high-level field differences is returned. It also lists the exact fields compared. This is meaningful behavioral detail beyond simple 'compare'; it lacks only minor details like error handling or permissions.
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 compact and front-loaded. The first sentence states the core action, the second specifies behavior and output fields, and the final sentence adds use cases. Every sentence contributes; there is no fluff.
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 key aspects: action, parallel fetching, output summary with enumerated fields, and typical use cases. An output schema exists to handle return value details. The only minor gap is the 'project' parameter explanation, but the overall context is sufficient.
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 0%, so the description must compensate. The two required parameters (trace_id_a, trace_id_b) are self-explanatory given the tool's purpose, but the optional 'project' parameter is not explained in the description. This partial compensation earns a baseline score.
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 opens with a specific verb+resource: 'Compare two traces side-by-side.' It clearly distinguishes itself from sibling tools like fetch_trace (single trace retrieval) and analyze_sessions by explicitly stating it compares two traces and lists the compared fields (name, user, latency, cost, etc.).
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 gives two concrete use cases ('why did trace X take longer than trace Y?' and 'what's different between these two runs?'), providing clear context for when to use the tool. It doesn't explicitly mention alternatives or exclusions, but the scenarios are specific enough to guide selection.
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 must carry the full burden of behavioral disclosure. It does explain the key behavioral logic: 'If threshold_seconds is set, returns all traces above that threshold. Otherwise returns the top_n slowest traces.' It also explains the group_by options. However, it doesn't disclose behavior for other parameters like time_range or tags, which is a notable gap.
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 compact and well-structured. It starts with the primary purpose, provides example queries, and then explains key parameters in a terse, scannable format. Every sentence adds value without redundancy.
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 gives enough context for the core functionality and typical use cases, including the threshold vs. top_n behavior and group_by semantics. The existence of an output schema means return values need not be described. However, with 9 parameters and no annotations, guidance on the time-range and project filters would improve 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?
The schema has 0% description coverage, so the description must compensate. It explains domain, threshold_seconds, top_n, and group_by, which is valuable. However, it leaves 5 out of 9 parameters (tags, project, start_date, end_date, time_range) completely unexplained, creating a significant gap in parameter understanding.
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 function: 'Find the slowest traces. Returns actual trace IDs and metadata.' This uses a specific verb and resource, and the mention of returning actual trace IDs helps distinguish it from aggregated analytics tools like analyze_latency. Example questions further reinforce the 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 provides clear context for when to use the tool via example queries ('Which traces were slowest?', 'Show me traces taking >30s'), which implies the use cases. However, it doesn't explicitly mention alternatives or when not to use it, so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It clearly discloses the core behavioral trait (unresolved placeholders, raw template intact) and explicitly contrasts with the resolving variant. It does not mention error behavior or permissions, but the primary nuance is well covered.
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 exceptionally concise: two sentences deliver the core action, the use case, and the alternative. No filler words; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 params, 0% schema description coverage), the description covers the core behavior and usage intent but leaves parameter semantics undocumented. The presence of an output schema offsets the need to describe return values, so this is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description provides no details about how `name`, `label`, `project`, and `version` are used. Only the general context of 'fetch a prompt' hints at `name`, leaving the optional parameters unexplained.
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 a specific action ('Fetch a prompt') with a key differentiator ('without resolving placeholders or linked dependencies'). This distinguishes it from sibling get_prompt, which resolves by default, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use guidance is provided: 'Use this for debugging prompt composition' and an explicit alternative: 'For production runtime fetches, use get_prompt which resolves by default.' This leaves no ambiguity about selection.
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 conveys a read-only inspect operation and specifies what 'full details' means (input, output, all observations). It does not mention any side effects or edge cases, but for a simple read tool this is sufficient, though not exhaustive.
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, no filler. The opening sentence states the core action, and the subsequent sentences provide usage context and an alternative. 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 single-trace fetch tool with an output schema and a simple parameter set, the description covers the essential context: what it returns, when to use it, and how it differs from the listing tool. The only gap is the undocumented 'project' parameter, but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description only implicitly references trace_id ('when you have a trace ID'). The optional 'project' parameter is not explained at all. Since coverage is zero, the description should have compensated by explaining parameters, but it only partially does.
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 gets full details of a single trace, including input, output, and observations. It explicitly contrasts with sibling tool fetch_traces for listing traces, making the purpose distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: 'Use this when you have a trace ID' and names the alternative 'fetch_traces' for listing. This clearly communicates when to use this tool versus the most likely sibling.
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 full responsibility. It discloses that the tool returns all project names and identifies the default project, which is useful behavioral detail. It doesn't explicitly state read-only nature or error conditions, but these are largely implicit for a list operation and the description is otherwise transparent.
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 two sentences, front-loading the core purpose and then adding the key detail about the default project. Every word earns its place; no redundancy or fluff.
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?
For a simple, zero-parameter list tool with an output schema, the description is complete. It states the scope, return contents, and the practical significance of the default project, making the tool well-understood even without annotations.
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?
No parameters exist and schema coverage is trivially 100%, so the description needs no parameter details. Per the rubric, 0 params receive a baseline of 4; the description adds no unnecessary parameter information.
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 function with 'List all Langfuse projects configured on this server' — a specific verb and resource. It distinguishes itself from sibling list tools (e.g., list_prompts, list_datasets) by explicitly scoping to projects and adding the server context.
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?
Usage is implied by the tool name and purpose, but there is no explicit 'when to use this' or comparison to alternatives. The note about the default project provides indirect context (if you omit project argument), but it doesn't clearly instruct when to choose this tool over others.
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 transparency burden. It discloses return values (percentiles, average, max), performance impact of include_per_generation, and the ability to identify bottleneck models. It does not discuss authentication, rate limits, or side effects, but as a read-only analysis tool, the disclosed behavior is solid.
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 and well-structured, with bulleted return values and a clear note about the optional flag. Every sentence contributes meaning, and the formatting improves readability without unnecessary verbosity.
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 an 8-parameter tool with no annotations, the description covers the core purpose, key parameters, and a performance caveat. It doesn't address all filter parameters (project, time_range), but provides enough context for a competent agent to use the tool correctly. The presence of an output schema (though not shown) lessens the need to fully describe return structures beyond the already included percentiles.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds value by enumerating allowed group_by values ('domain', 'name', 'model') and explaining include_per_generation semantics (per-LLM-call latency, slower). Other parameters like tags and dates are not explained, but the most distinguishing parameters are clarified, 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 it analyzes latency distribution across traces, with an optional per-generation mode. It names the specific resource (latency) and the action (analyze), and distinguishes itself from sibling tools like analyze_sessions by focusing on latency percentiles and bottleneck identification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool, particularly the trade-off for include_per_generation (slower, fetches observations). It does not explicitly mention alternatives like analyze_sessions, but the focus on latency makes the use case apparent. Absence of explicit exclusions or alternative references prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![LangfuseMCP MCP server](https://glama.ai/mcp/servers/DrishtantKaushal/LangfuseMCP/badges/card.svg)](https://glama.ai/mcp/servers/DrishtantKaushal/LangfuseMCP)

Score Badge

Copy to your README.md:

[![LangfuseMCP MCP server](https://glama.ai/mcp/servers/DrishtantKaushal/LangfuseMCP/badges/score.svg)](https://glama.ai/mcp/servers/DrishtantKaushal/LangfuseMCP)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/DrishtantKaushal/LangfuseMCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server