py-har-mcp

by wdvipa

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clear, distinct purpose: loading data, listing URL/method combos, filtering request IDs, fetching request details, aggregating by domain or status code, and searching content. No two tools overlap in function.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: load_har, list_urls_methods, get_request_ids, get_request_details, get_domain_stats, get_status_code_stats, search_har. The verbs are uniform and descriptive.
Tool Count5/5
With 7 tools, the set is well-scoped for HAR analysis. Each tool covers a distinct aspect of the workflow, and the count is neither sparse nor bloated.
Completeness4/5
The surface covers the main HAR analysis workflow: load, browse, filter, inspect, search, and statistics. A minor gap is the absence of a direct 'get all requests' tool, but this can be worked around by combining list_urls_methods with get_request_ids.
Average 3.6/5 across 7 of 7 tools scored.
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
- Last stable release on March 23, 2026
- 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.
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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

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.
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.
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 for behavioral disclosure. The description only says what the tool lists but does not mention whether it requires a previously loaded HAR, what happens if none is loaded, or whether it is a safe read-only operation. This is a significant gap 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, concise sentence that directly states the tool's function. It is front-loaded with the main action and resource, containing no extraneous words or redundant details.
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's simplicity (one optional parameter, complete schema coverage, and an output schema), the description is mostly sufficient. It clearly states what the tool returns (URL and method combinations). However, it lacks any guidance on when to use this tool relative to its siblings, preventing a perfect score.
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 description covers the single 'source' parameter fully, explaining its optionality, default value, and behavior. The tool description adds no parameter-level information, but the schema provides complete semantics, so the baseline 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 a specific verb '列出' (list) and clearly identifies the resource as 'URL 与 HTTP 方法组合' (URL and HTTP method combinations). This distinguishes it from sibling tools like get_request_ids or get_domain_stats, making the tool's 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives like search_har or get_status_code_stats. The intended usage is implied by the purpose, but no clear context or exclusions are given.
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: automatic redaction of authentication-related headers. However, it does not explicitly state whether the operation is read-only or non-destructive, nor describe any side effects. The redaction disclosure is valuable but incomplete 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, compact sentence that front-loads the primary action and adds the redaction behavior without redundancy. Every word contributes value.
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 description is concise and mentions the key redaction behavior, and an output schema exists to define return structure. However, it does not explicitly place the tool in the flow of using sibling tools (e.g., 'Use get_request_ids first to obtain an ID'), which would enhance completeness for an agent deciding when to invoke it.
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 provides 100% coverage for both parameters, including descriptions for request_id and source. The description text adds no additional parameter-specific meaning beyond reaffirming that it retrieves details by ID. Baseline 3 is appropriate since the schema already documents the parameters thoroughly.
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 a specific verb ('获取' - retrieve) and resource ('完整请求详情' - full request details), scoped by request ID. It also adds a distinct feature (auto-redaction of auth headers) that differentiates it from sibling tools like list_urls_methods or get_status_code_stats, which focus on listing or statistics.
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 request ID but does not explicitly say when to use this tool versus alternatives like get_request_ids or search_har. The schema's request_id description mentions it can be obtained from list_urls_methods or get_request_ids, which provides context, but the description text itself offers no direct guidance on selecting this tool over siblings.
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 of disclosure. It states the aggregation behavior and included metrics, but does not clarify whether results are counts, percentages, or raw lists, nor does it explicitly confirm read-only behavior or handle empty-data scenarios. The name 'get_' and statistical nature imply safety, but the description alone offers limited depth.
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 that directly states the verb and resource. It contains zero redundant text and fully conveys the core functionality in a compact form.
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's simplicity and the presence of an output schema, the description adequately covers the purpose and grouping scope. The only gap is the lack of explicit differentiation from sibling tools like get_status_code_stats, but the 'by domain' phrase provides sufficient context for an agent to select this tool.
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% because the only parameter, 'source', has a full description explaining its behavior. The tool description does not add parameter-specific meaning beyond the schema, but the baseline of 3 is appropriate given the high coverage.
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 verb '汇总' (summarize/aggregate), the resource '请求统计信息' (request statistics), and the grouping key '按域名' (by domain), while specifying included dimensions (method and status code distribution). This distinguishes it from sibling tools like get_status_code_stats, which likely provides overall stats.
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 use case is implied: '按域名汇总' suggests using this tool when domain-level breakdowns are needed. However, there is no explicit guidance about when to use this tool versus alternatives like get_status_code_stats or list_urls_methods, and no mention of when not to use it.
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 accurately describes a read-only filtering operation with no harmful side effects, but it does not mention edge cases such as no matches, exact matching rules, or performance implications.
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 that efficiently conveys the core function without any extraneous words or 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?
Given that an output schema exists and the parameter descriptions are complete, this short description is sufficient for a simple list-returning tool. It could mention the optional source parameter, but the schema already covers that detail, making the overall context adequate.
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 already provides detailed descriptions for all three parameters, so the description adds little value beyond restating the roles of URL and method. With 100% schema coverage, the baseline 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 clearly states that the tool returns a list of request IDs filtered by URL and HTTP method. This specific verb+resource combination distinguishes it from sibling tools like get_request_details, which likely focuses on the details of a single request.
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 the tool should be used when you have a specific URL and HTTP method and need to find the corresponding request IDs. However, it does not explicitly mention when to prefer this over siblings or provide any exclusions or alternative tool references.
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 side effects. It states the tool saves the HAR as the 'default analysis dataset', but does not mention whether it overwrites existing data, validates the HAR format, or handles network authentication for URLs. This lack of behavioral detail is a significant gap for a mutating 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 one sentence, front-loaded with the action ('load HAR'), and contains no filler or redundant information. Every word 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?
The tool is simple with a single fully-described parameter and an output schema. The description covers the core behavior, and the sibling list makes its role clear. However, it would benefit from a note about being a prerequisite for analysis tools, so it is not a 5.
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 provides 100% coverage for the 'source' parameter, including supported path types and the side effect of saving as default dataset. The tool description adds no additional semantic meaning beyond what the schema already documents, so it stays at the baseline.
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 'loads HAR files from file path or HTTP URL and saves as default analysis dataset'. This is a specific verb+resource combination, and it distinguishes itself from the sibling analysis tools (list_urls_methods, get_request_details, etc.) which operate on already-loaded data.
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 context makes it obvious this is the ingestion tool used before analysis tools, since siblings are all query/analysis functions. However, it does not explicitly state 'use this first' or mention exclusions/alternatives, so it falls 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.

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