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py-har-mcp

py-har-mcp

by wdvipa
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_status_code_stats

Aggregate request counts by HTTP status code from a HAR file, revealing the distribution of success and error responses for traffic analysis.

Instructions

按 HTTP 状态码汇总请求统计信息。

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_status_code_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
sourceNo可选 HAR 文件来源。为空时使用默认 HAR；传入时直接分析指定 HAR 文件。

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.3/5.0
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 aggregation function and does not disclose whether the tool reads a default HAR, mutates state, or requires prior data loading. This is a minimal disclosure for a read-like stats 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 a single, concise sentence that conveys the core functionality without redundant words or unnecessary detail.

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 stats tool with an output schema and full parameter coverage, the description is minimally adequate. However, it omits contextual details such as the relationship to loaded HAR data (only mentioned in schema) and any step-by-step usage context, leaving some inference to the agent.

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

Parameters3/5

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

The single 'source' parameter is fully described in the schema (100% coverage), including default behavior and meaning. The tool description adds no extra parameter semantics, so the baseline 3 applies.

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

Purpose5/5

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

The description uses a specific verb '汇总' (summarize) and clearly states the resource 'request statistics' grouped by HTTP status code. It distinguishes itself from siblings like get_domain_stats by focusing on status code aggregation.

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, nor does it mention prerequisites like needing a loaded HAR. The presence of sibling analysis tools implies possible alternatives, but the description offers no differentiation or usage context.

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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