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PromptSmith

by BlinkVoid
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_usage_stats

Retrieve usage statistics for PromptCore, including total prompts generated, framework and category distribution, and average complexity scores.

Instructions

Get usage statistics for PromptCore.

Shows total prompts generated, distribution by framework and category, and average complexity scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/promptcore/main.py:189-198 (handler)
    The get_usage_stats function serves as the MCP tool handler for retrieving usage statistics. It calls the storage layer via dependency injection.
    @mcp.tool()
def get_usage_stats() -> dict:
    """
    Get usage statistics for PromptCore.

    Shows total prompts generated, distribution by framework and category,
    and average complexity scores.
    """
    deps = get_dependencies()
    return deps.storage.get_stats()

Tool Definition Quality

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what data is returned but doesn't cover important aspects like whether this is a read-only operation, if it requires authentication, rate limits, freshness of data, or error conditions. For a statistics tool with zero annotation coverage, this leaves 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.

Conciseness4/5

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

The description is appropriately concise with three sentences that each add value: stating the tool's purpose, listing key statistics categories, and specifying metrics. It's front-loaded with the core purpose and avoids unnecessary elaboration. Minor deduction for slightly repetitive phrasing ('distribution by framework and category' could be more streamlined).

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

Completeness3/5

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

Given that the tool has no parameters, has an output schema (so return values are documented elsewhere), and provides basic statistics, the description is minimally complete. However, without annotations and with sibling tools that might overlap (like log_execution_feedback), more context about when this tool is appropriate would improve completeness for the agent.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the input requirements. The description appropriately doesn't repeat parameter information, maintaining a baseline of 4 for parameterless tools that don't waste space on nonexistent parameters.

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

Purpose4/5

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

The description clearly states the tool's purpose with specific verbs ('Get usage statistics') and resources ('for PromptCore'), and lists the types of statistics provided (total prompts, distribution by framework/category, average complexity). It doesn't explicitly differentiate from sibling tools, but since no siblings appear to provide similar statistics, this is adequate.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, frequency of use, or how it relates to sibling tools like log_execution_feedback or recommend_strategy. The agent must infer usage context solely from the purpose.

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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