Skip to main content
Glama

search_har

Search inside HAR files for keywords or fragments in request and response headers, request bodies, and response bodies to locate specific network activity.

Instructions

在 HAR 中搜索请求头、响应头、请求体和响应体内容。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes要搜索的关键字或片段。可用于匹配请求头、响应头、请求体或响应体中的文本。
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。
case_sensitiveNo是否区分大小写。默认 false。
search_headersNo是否搜索请求头与响应头。默认 true。
search_request_bodyNo是否搜索请求体内容,例如 POST/PUT 的 `postData.text`。默认 true。
search_response_bodyNo是否搜索响应体内容,例如 `response.content.text`。默认 true。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 for behavioral disclosure. It does not state whether the operation is read-only (implied by 'search' but not explicit), what the return format is, or how the tool behaves with no matches. It also doesn't describe default toggle states or case sensitivity behavior, leaving these to the schema.

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 in Chinese that concisely states the core function and scope. It wastes no words and covers all main search targets in one line.

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?

Despite having 6 parameters, no annotations, and an output schema, the description is minimally sufficient for a search tool. It conveys the essential purpose but leaves gaps: it does not explain how this search tool compares to sibling tools or what the output represents (e.g., matching requests vs. snippets). The schema fills parameter details, and the output schema covers returns, so overall completeness is average.

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

Parameters3/5

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

The input schema has 100% description coverage for all six parameters, with detailed explanations for query, source, case_sensitive, and the three search toggles. The description does not add any parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

Purpose5/5

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

The description uses the specific verb 'search' with a clear resource ('HAR') and explicitly lists the four search targets (request headers, response headers, request body, response body). This clearly distinguishes it from sibling tools like list_urls_methods or get_request_details.

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

Usage Guidelines3/5

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

The description implies usage: it is a search tool for finding content within HAR files, which is evident from the verb and the listed targets. However, it does not explicitly state when to prefer this over alternatives, 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.

Install Server

Other Tools

Latest Blog Posts

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/wdvipa/py-har-mcp'

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