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avivsinai

langfuse-mcp

get_error_count

Count traces with exceptions in your LLM applications over a specified time period to monitor error rates and identify issues in Langfuse observability.

Instructions

Get number of traces with exceptions in last N minutes.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    age: Number of minutes to look back (positive integer, max 7 days/10080 minutes)

Returns:
    Dictionary with error statistics including trace count, observation count, and exception count

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageYesNumber of minutes to look back (positive integer, max 7 days/10080 minutes)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool returns a dictionary with error statistics, which is helpful. However, it doesn't mention performance characteristics (e.g., rate limits), authentication needs, or whether this is a read-only operation (though implied by 'Get'). The description adds some context but misses key behavioral traits for a tool with zero annotation coverage.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured but slightly redundant with the schema. Every sentence serves a purpose, though the parameter description could be more concise given the schema coverage.

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

Completeness4/5

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

Given the tool has an output schema (implied by 'Returns' section), the description doesn't need to explain return values in detail. It covers the purpose, parameter, and return type adequately for a simple read operation. However, with no annotations and multiple sibling tools, it could better contextualize usage relative to alternatives.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema fully documents the single parameter 'age'. The description repeats the parameter constraints verbatim from the schema ('positive integer, max 7 days/10080 minutes'), adding no additional semantic meaning. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Get number of traces with exceptions in last N minutes.' This specifies the verb ('Get'), resource ('traces with exceptions'), and temporal scope ('last N minutes'). It distinguishes from siblings like 'find_exceptions' or 'get_exception_details' by focusing on counting rather than listing or detailing. However, it doesn't explicitly contrast with these alternatives, preventing a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'find_exceptions' or 'get_exception_details'. It mentions the temporal parameter but doesn't explain why one would choose counting over listing exceptions or fetching details. Without any when-to-use or when-not-to-use context, the agent lacks decision-making criteria.

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

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