qa-probe

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by LS-SIEM-LLP

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Latest release: v2.12.1
Disambiguation5/5
Each tool targets a distinct aspect of QA probing: explaining failures, impact analysis, dependency graphs, reports, feedback labeling, live probing, full analysis runs, and fix suggestions. No two tools overlap in purpose.
Naming Consistency5/5
All tools share the 'qa_probe_' prefix and follow a consistent verb_noun pattern in snake_case (e.g., explain_failure, get_blast_radius). The minor deviation of 'label' as a single verb is still semantically clear and consistent with the pattern.
Tool Count5/5
With 8 tools, the server is well-scoped for its domain—covering analysis, probing, reporting, labeling, and fixes—without being too sparse or overwhelming.
Completeness5/5
The tools cover the full workflow: triggering analysis, probing live endpoints, retrieving reports and graphs, explaining failures, assessing impact, suggesting fixes, and recording feedback. No obvious gaps exist for the intended use case.
Average 3.8/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 25 commits in the last 12 weeks
- Last stable release on June 25, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that the label reapplies on future runs and explains verdict groups (suppress vs confirm), but it does not mention permissions, reversibility, or what happens with multiple labels for the same endpoint.
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 two sentences plus a concise example. It is front-loaded with the core purpose and contains no redundant information.
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 the tool has 4 parameters and no output schema, the description covers the main purpose and gives a usage example, but it lacks details on persistence behavior, default signal handling, and prerequisites like needing a prior diagnosis report.
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 coverage is 100%, so the baseline is 3. The description adds value by explaining the verdict groups and providing an example, but it does not elaborate on parameter semantics beyond what the schema already offers.
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 states 'Record feedback on an endpoint diagnosis so qa-probe reapplies it on future runs,' which provides a specific verb and resource. It also distinguishes the tool's purpose from siblings by focusing on labeling/teaching behavior, but it could be more precise about what constitutes a label.
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 when providing feedback on a diagnosis ('mark a result as expected/known or confirm it as a real bug') and gives an example, but it does not explicitly state when not to use this tool or compare it to siblings like qa_probe_explain_failure or qa_probe_run_analysis.
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 full responsibility. It describes the tool as retrieving a fix guide, which implies a read-only operation with no side effects. However, it does not disclose what happens if an invalid root cause is provided or the format of the output.
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 concise, consisting of two sentences and an example query. Every part adds value and the main action is front-loaded.
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 the single enum parameter and no output schema, the description is sufficient for understanding the basic purpose but lacks details about the return value format (e.g., is it a text guide, list of steps?). Additional context about the output would improve completeness.
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. The description adds minimal value beyond the schema, only providing an example usage. It does not clarify additional constraints or format details.
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 'Get a step-by-step fix guide for a root cause category.' It identifies the specific verb (get) and resource (fix guide). It does not explicitly differentiate from sibling tools like qa_probe_explain_failure, but the name and description make the purpose distinct.
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 example query 'What do I do about feature_flag_disabled issues?' implies when to use the tool (when you have a specific root cause and need a fix guide). However, it provides no explicit guidance on when not to use it or comparison to alternatives.
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. It states what the tool does but does not disclose any side effects, required permissions, whether it is read-only, or any other behavioral traits beyond triggering the pipeline.
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 two sentences: a functional statement and a usage example. It is concise and front-loaded with the core purpose, though the example could be seen as slightly redundant.
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 explains the high-level function and usage, but lacks details on output format, whether the pipeline is synchronous, error handling, or any prerequisites. Given the absence of an output schema, more context would be beneficial.
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 coverage is 100% for the single parameter (headless). The description does not add any additional meaning 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 clearly states the tool triggers a full qa-probe pipeline (analyze + probe + report) and returns a summary. This distinguishes it from sibling tools that focus on individual steps like qa_probe_explain_failure or qa_probe_get_blast_radius.
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 description provides a usage example ("Ask: 'Run a full QA check and give me the summary.'"), which implies when to use. It does not explicitly contrast with sibling tools, but the context of the full pipeline vs. individual tools makes the distinction clear.
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 must cover behavior. It states the tool returns a report with route scores and root causes, which is adequate but does not disclose any side effects, limits, or performance characteristics.
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 extremely concise with two front-loaded sentences. No unnecessary words, and the examples provide immediate clarity.
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?
For a simple tool with one optional parameter and no output schema, the description sufficiently covers the report content and typical usage. It lacks only minor details like return format or pagination, but this is acceptable given the tool's simplicity.
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 fully describes the filter parameter with enum values and default. The description does not add additional meaning beyond the schema, so baseline score of 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 clearly states the tool retrieves a QA report with route scores and root causes. It provides example queries, distinguishing it from sibling tools like qa_probe_explain_failure or qa_probe_run_analysis. The verb 'Get' and resource 'QA report' are specific.
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 gives example queries ('Show me all broken routes', 'What is the overall score?') implying typical usage, but lacks explicit guidance on when to use this tool versus alternatives like qa_probe_explain_failure or qa_probe_get_blast_radius.
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 description carries full burden. It indicates a read operation ('status') but does not explicitly state safety (e.g., no side effects), rate limits, or authorization requirements. Some transparency is present but insufficient without annotations.
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 extremely concise with one sentence and a clarifying example. Every word adds value, and the purpose is front-loaded. No wasted text.
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 explains what the tool does but omits details about return format, possible status values, or error handling. With no output schema, this gap reduces completeness for a simple probe tool. Adequate but not thorough.
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 coverage is 100% with descriptions for both parameters. The tool description adds no additional semantic value beyond the existing schema descriptions. Baseline 3 applies as description does not enhance parameter understanding.
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 live-probes a single endpoint and returns status, with a concrete example question. It differentiates from sibling tools like explain_failure or suggest_fix by focusing on immediate status checking.
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 description provides an example question implying real-time probing usage, but lacks explicit guidance on when not to use it or contrast with siblings. It gives context but no formal differentiation or exclusion criteria.
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 must carry full behavioral burden. It only states the read-like operation (find out how many routes break) but does not disclose authorization needs, rate limits, potential side effects, or return format details. This is a significant gap.
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 extremely concise: two sentences, no unnecessary words. The first sentence states the purpose, and the second provides a concrete example. Every sentence 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?
Given the tool's simplicity (one parameter, no output schema), the description is fairly complete. It explains what the tool does and gives an example. However, it could be slightly more explicit about the output (e.g., returns a count or list), which would help the agent set expectations. Still, it is adequate for the use case.
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 single parameter 'endpoint' is described in the schema with a default description, but the tool description adds value by showing an example ('GET /alerts') and indicating format (e.g., 'Backend endpoint key'). This goes beyond the schema, making it easier for the agent to construct valid inputs.
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's purpose: to find the number of frontend routes that would break if a backend endpoint goes down. It uses a specific verb ('Find out') and resource ('blast radius'), and distinguishes from sibling tools like qa_probe_explain_failure.
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?
Provides an example query ('What pages break if GET /alerts goes down?') to illustrate usage context. This gives clear guidance on when to use the tool, but does not explicitly state when not to use or mention alternative tools.
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 responsibility. It only states the tool retrieves a graph but does not disclose behavioral traits like being read-only, auth requirements, or performance characteristics.
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 two sentences with no wasted words. The first sentence gives the action, the second provides an example query. It is efficiently front-loaded.
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 simple tool (one optional param, no output schema), the description is mostly complete. It explains what the tool does and gives an example. A minor gap is the lack of description of the output graph format.
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?
Schema coverage is 100% for the single optional parameter, and the description adds value by providing an example filter ('/dashboard'). This goes beyond the schema's description of 'Optional: filter to a specific frontend route'.
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 it retrieves a dependency graph mapping backend API routes to frontend routes. The verb 'Get' and resource 'dependency graph' are specific, and it distinguishes itself from sibling tools like explanation or blast radius.
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 description includes a concrete example question ('Which backend routes does /dashboard call?'), which implies when to use the tool. However, it lacks explicit guidance on when not to use it or mention of alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description clearly indicates this is a read-only diagnostic tool that generates plain English explanations. It does not mention any destructive actions or side effects. Could add that it does not modify data, but the intent is clear.
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 two sentences with a concrete example. No fluff, every word earns its place. Front-loaded with the purpose, immediately followed by usage guidance.
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 simple input (one path string) and no output schema, the description adequately explains what the tool does and how to invoke it. It could mention that the response is an explanation string, but it's sufficient for an agent to understand the tool's role in the probe suite.
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 coverage is 100% with a single parameter described as 'Frontend route path'. The tool description adds an example ('/rules') but does not provide semantics beyond the schema. Baseline 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 the tool's function: explaining why a frontend route shows no data or is failing. It provides a concrete example ('Why is /rules showing no data?'). It distinguishes from sibling tools like qa_probe_get_blast_radius or qa_probe_suggest_fix, which have different purposes.
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 description tells when to use the tool (to explain route failures) and includes a sample user query. It does not explicitly state when not to use it or mention alternatives, but the context of sibling tools implies their distinct use cases.
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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