foreman

by YashNirwan

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clear, non-overlapping purpose: listing videos, listing alerts, summarizing shifts, listing rejected detections, semantic search, explaining alert evidence, and exposing the taxonomy. There is no ambiguity about which tool to use for a given task.
Naming Consistency4/5
Most tools follow a verb_noun pattern (list_processed_videos, list_alerts, list_rejected_detections, search_timeline, explain_alert), but shift_summary and hazard_taxonomy break the pattern by leading with a noun. The deviation is minor and the naming remains readable.
Tool Count5/5
Seven tools is well-scoped for a video safety analysis and auditing system. Each tool covers a distinct query or audit function without redundancy, and the count is neither too thin nor too heavy.
Completeness4/5
The surface covers the core workflows: enumerating videos, querying alerts, auditing rejections, searching footage, and understanding alerts. A minor gap is the lack of a dedicated get_video detail tool, but list_processed_videos provides sufficient access for most use cases.
Average 3.9/5 across 7 of 7 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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 for behavioral disclosure. It mentions what the summary includes but does not explain side effects, permissions, or behavior when the optional video_id is null. The tool appears to be a read-only summary, but this is not stated.
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 with no wasted words. It efficiently conveys the core purpose and scope of the summary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although an output schema exists, the description fails to provide enough context about the optional parameter and the overall shift aggregation. It lacks usage guidance and behavioral caveats, making it insufficient for a tool with such sparse schema information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage, so the description must clarify the parameter's meaning. It does not explain that video_id identifies a processed shift, nor how the summary changes when video_id is null. The connection between 'video_id' and 'shift' is left ambiguous.
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 action ('Summarise') and resource ('processed shift'), and specifies the summary content (volumes, alert mix, filtered out). This distinguishes it from sibling list tools like list_processed_videos and list_alerts by presenting an aggregated view.
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 is used when a summary of a processed shift is needed, but it does not explicitly state when to prefer this over the sibling tools or provide any exclusions. Guidance is implicit rather than explicit.
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, the description must carry the burden of behavioral disclosure. It does reveal that the output includes the evidence found missing, which is a behavioral detail. However, it does not explicitly state whether the operation is read-only, whether any state is changed, or mention permissions or rate limits. This is a minimal viable level of transparency for a list operation.
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 highly concise: two short paragraphs. The first sentence immediately states the tool's primary action, and the second paragraph adds relevant audit context. No unnecessary words or repetitive details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the tool's purpose and provides useful audit context, but it completely omits any explanation of the sole parameter (video_id). Since the output schema exists, return values are documented, but the input parameter is undocumented in both schema and description. This makes the tool incomplete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one parameter, video_id, with 0% description coverage. The tool description does not mention video_id at all, so an agent has no idea that the parameter exists or how it filters results. This is a critical gap; the description completely fails to compensate for the lack of schema documentation.
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 and resource: 'List detections the verifier rejected, with the evidence it found missing.' This distinguishes it from sibling tools like list_processed_videos and list_alerts, which likely list different entities. The inclusion of 'with the evidence it found missing' further clarifies the tool's unique output.
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 clear use case: 'Useful for auditing whether the system is suppressing real hazards.' It gives context on when to use the tool but does not explicitly mention alternatives or when not to use it. This is more than implied usage but lacks the explicit exclusions that would merit a 5.
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 carries the full burden for behavioral disclosure. It transparently describes the return content (proposing model's claim, confidence, verifier's reasoning, standard cited) and the purpose, providing significant context beyond a simple 'explain.' It does not discuss error behavior or the optional video_id, but the core behavior is well disclosed.
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 exactly two sentences. The first sentence provides a clear, front-loaded summary, and the second adds essential detail about the evidence chain and its purpose. Every word earns its place with no redundancy.
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 tool has an output schema, so return values are covered, and the core purpose and content are well described. However, the optional video_id parameter is not explained, and there is no mention of error behavior or constraints. Given the simplicity of the tool, the description is adequate but has clear gaps in parameter semantics and edge-case behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description mentions neither alert_id nor video_id. The phrase 'behind one alert' implies alert_id is the identifier, but the optional video_id is completely unexplained. The description fails to add meaning beyond the bare type titles in the schema, so it does not compensate for the lack of parameter documentation.
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 opens with a specific verb+resource: 'Return the full evidence chain behind one alert.' It clearly distinguishes itself from sibling listing tools by focusing on a single alert and elaborates on the components it returns (claim, confidence, verifier's reasoning, standard cited). This is explicit and unambiguous.
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 states the intended use case: 'so a supervisor can judge the alert rather than take it on trust,' giving clear context for when to use this tool. It does not explicitly name alternatives or exclusions, which would warrant a 5, but the purpose is clear enough to guide tool selection.
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, the description carries full burden. It discloses that each class has a specific evidence standard and known failure modes, adding meaningful context beyond the tool name. However, it does not explicitly state that the tool is read-only, what the return object looks like, or any side effects. For a simple taxonomy lookup, this is adequate but not rich.
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: the first defines the content, the second explains why to read it. Both sentences earn their place, with no filler or redundancy. It is well-structured and 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?
For a zero-parameter tool with an output schema, the description covers the essential purpose and use context. It doesn't enumerate the taxonomy contents, but the output schema is responsible for that. The description is sufficiently complete for an agent to decide when and how to invoke it.
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 zero parameters, and schema description coverage is 100% (vacuously). The baseline for 0 params is 4, and the description adds no parameter-specific semantics because none exist. No further explanation is needed.
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 identifies the resource as 'the hazard classes this system detects, with evidence bars and known failure modes,' establishing a clear topic. It lacks an explicit verb like 'retrieve' or 'list,' but the noun phrasing is unambiguous and distinguishes it from sibling tools that list videos or alerts.
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?
It provides explicit when-to-use guidance: 'Read this before interpreting alerts.' This gives clear context that the tool is a precursor to alerts, though it does not name alternatives or explicitly say when not to use it. The context is sufficient for an agent to select it appropriately.
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?
The description adds the behavioral condition 'processed and available to query', which is useful context. There are no annotations, so the description carries the full burden, but it doesn't disclose details like pagination, ordering, or access requirements. The output schema may cover return structure, so this is adequate but not rich.
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 short sentence (10 words) that is front-loaded with the main action and resource. There is no unnecessary repetition or filler.
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, zero-parameter list operation with an output schema, the description fully states what the tool lists and the selection criterion. It doesn't mention edge cases or require excessive detail; the output schema handles return value descriptions.
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 zero parameters, so there is no parameter semantics to convey. The schema coverage is trivially 100%, and the baseline for 0 parameters is 4.
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 action ('List') and resource ('videos') with a specific qualifier ('processed and available to query'). This distinguishes it from sibling tools like list_rejected_detections and list_alerts.
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 a usage context: use when you need a list of processed videos that can be queried. However, it provides no explicit guidance on when to prefer this over siblings like search_timeline or list_alerts, nor any exclusions.
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 provided, the description carries the full burden of behavioral disclosure. It reveals a key non-obvious behavior: it searches all analyzed windows, not just those with alerts, and supports natural-language queries. This is substantial transparency, though it does not cover performance or error behavior.
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 well-organized with a clear one-sentence summary, a paragraph elaborating scope with useful examples, and a structured Args list. The examples are valuable and justify the length, though the overall text is slightly longer than minimal.
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 description covers the tool's purpose, unique search scope, and all parameters. Since an output schema exists, it need not explain return values. A minor improvement would be referencing list_processed_videos for users unsure of video_id, but this is not a critical omission.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description's Args section fully explains every parameter: query ('what to look for, in plain language'), video_id ('which processed video; omit if only one'), and k ('how many windows to return'). This adds complete semantics beyond the schema's types and defaults.
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: 'Search the footage in natural language.' It distinguishes itself from siblings by noting it searches every analyzed window, not just alert-producing ones, and explicitly contrasts with the hazard taxonomy. This makes its unique role unambiguous.
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 clear usage context, explaining that the tool answers questions the hazard taxonomy does not cover and giving concrete examples. It also gives parameter-level guidance, such as omitting video_id when only one video is processed. However, it does not explicitly state when to prefer alternative tools like list_alerts, so it stops short of a full 5.
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 carries the full burden. It discloses that only confirmed alerts are returned, that there is a verification process, and that rejected items are excluded. It also explains the default behavior of `min_severity` and the optional nature of `video_id`. It does not discuss side effects, but as a list operation, the read-only nature is reasonably 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 concise and front-loaded with the core purpose, then structured into parameter explanations. Every sentence provides value: the purpose, per-arg guidance, and the pointer to an alternative tool. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 optional params, output schema exists), the description is complete. It explains what is returned, how to narrow results, and how to access a related data set. The existence of an output schema means the description need not detail the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates fully. Each parameter is given added meaning: `video_id` is explained with the 'omit if only one' rule, `hazard_type` is defined as a taxonomy key, and `min_severity` is given allowed values, a default, and what the default means. This goes well beyond the schema's type/default information.
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 ('List') and resource ('verified safety alerts for a processed video'), and it clearly differentiates from siblings by emphasizing 'confirmed alerts only' and pointing to `list_rejected_detections` for the rejected set. This is a precise, non-tautological purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'omit if only one has been processed' for `video_id`, and an alternative tool for a different need: 'Rejected detections are available via `list_rejected_detections` if you want to audit what the verifier removed.' This clearly indicates when to use this tool vs. an alternative.
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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