Regulated AI Compliance

by uchit

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinctly unique purpose: classification, cross-referencing, anti-pattern lookup, regulation listing, control lookup, and playbook generation. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with lowercase and underscores (e.g., classify_use_case, list_regulations). Even 'crosswalk' as a verb works similarly. No mixing of conventions.
Tool Count5/5
With 6 tools, the server covers key compliance workflows without being bloated or sparse. Each tool serves a clear role in the domain of regulated AI compliance.
Completeness4/5
The tool set covers core tasks: classification, cross-mapping, anti-pattern identification, regulatory discovery, control lookup, and implementation playbooks. Minor gaps like compliance reporting or audit trail tools are absent but not critical for the stated purpose.
Average 4.3/5 across 6 of 6 tools scored.
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 is passing
This repository is licensed under Apache 2.0.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description is the sole source. It discloses the output structure (slug, label, jurisdiction, row count) but does not mention behavioral traits like idempotency, rate limits, or permissions. For a simple list tool, this is adequate but not exceptional.
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?
Two well-structured sentences. The first states the main purpose, the second details output fields and usage. No wasted words, front-loaded with key action.
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?
For a simple list tool with full schema coverage and no output schema, the description adequately covers what the tool does, when to use it, and what the response contains. It is complete given the tool's complexity.
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%, so baseline 3. The description does not add additional meaning for the parameters beyond what is already in the schema, focusing instead on output fields.
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 returns regulations covered by the dataset, using the verb 'return' and specifying the resource. It distinguishes itself from siblings by highlighting its role as a discovery step for other tools like lookup_control and crosswalk, which adds context.
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?
Explicitly advises calling this tool first before other tools, mentioning lookup_control and crosswalk by name. This provides clear usage context, though it lacks explicit 'when not to use' guidance.
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 details the return structure (where, why, what to do, diagnostic tell) and provides examples, effectively disclosing the tool's read-only lookup behavior.
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?
Every sentence serves a purpose: purpose, return structure, examples, and usage. No fluff, well-organized, and front-loaded with the core action.
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 no output schema, the description explains return fields. Parameters are fully covered. Minor gap: no mention of permissions or authentication, but this is not critical for a lookup 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 coverage is 100%, and the schema already describes both parameters well. The description adds value with example queries, but this is not essential beyond the schema.
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 explicitly states the tool looks up anti-patterns from a specific catalogue, provides examples of anti-pattern names, and implicitly distinguishes from siblings focused on regulations and controls.
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 clearly states when to use the tool—'when reviewing an architecture description or codebase' and 'when you need to name a problem precisely'—but does not specify when not to use it 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.
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 the tool is not a substitute for legal counsel and mentions it recommends a next-step sequence. However, it does not explicitly state that the tool is read-only or has no side effects. Adequate but not extensive.
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?
Three well-structured paragraphs: function and output, use cases, and a caveat. Information is front-loaded with the key purpose and returns. No redundant phrases; every sentence adds value.
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 no output schema, the description adequately explains the return (risk tier, categories, obligations, next steps). It addresses dual-jurisdiction complexity. Could be slightly more detailed on output format, but sufficient for an AI agent. Context signals (2 params, no annotations) are well-covered.
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 coverage is 100% with detailed parameter descriptions. The description adds value by advising on specificity for the 'description' parameter (e.g., what the AI does, data used) and explaining the 'jurisdictions' default and dual-framework option. This enhances schema 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 clearly states the tool classifies AI use-cases under EU AI Act and optionally AU AI Safety Standard, listing specific outputs (risk tier, categories, obligations, etc.). It distinguishes from sibling tools like crosswalk or list_regulations by focusing on initial classification and dual-jurisdiction analysis.
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?
Explicitly lists use cases: initial classification, dual-jurisdiction analysis, and structured input for human-counsel-review. Includes caveat about not being a substitute for legal counsel and notes borderline cases needing review. Lacks direct comparison to siblings but provides clear context.
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?
No annotations are provided, so the description bears the full burden. It discloses result contents (control, regulations, tooling, evidence shape, sector relevance, practitioner note) and data source (hellouchit.com, CC BY 4.0). It does not mention any non-obvious behaviors like rate limits or permissions, but as a read-only lookup, this is acceptable.
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 moderately long but well-structured: first sentence states purpose, followed by result details, source, and usage examples. It is front-loaded with the key action. Minor redundancy (listing regulations again at the end) could be trimmed, but overall effective.
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 6 parameters, no required fields, and no output schema, the description adequately explains the return format ('Each result includes...') and the scope of data (list of regulations). It is sufficient for an agent to understand what the tool returns and when to use 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?
Schema coverage is 100%, so the schema already describes parameters. The description adds value by giving concrete examples for 'surface' and 'category' and explaining the 'search' field semantics (free-text over control names, notes, evidence shape). This extra context justifies a score above baseline 3.
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 starts with a specific verb ('Look up') and resource ('security/compliance controls'), listing the dimensions (regulation, category, surface, sector). It clearly distinguishes this lookup tool from siblings like classify_use_case or walk_playbook.
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 explicitly states when to use the tool with example queries ('which tool closes X regulator's requirement on Y surface'). It does not explicitly state when not to use, but the examples and sibling names provide sufficient context.
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?
No annotations provided, but the description adequately discloses that the tool retrieves playbook data without side effects. It does not specify authentication or rate limits, but for a read-only retrieval tool, the transparency is sufficient.
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?
Description is concise, front-loaded with the core purpose, uses a bullet list for playbooks, and every sentence adds necessary information. No wasted words.
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 complexity (3 parameters, no output schema), the description fully covers what the tool does, how to use it, and the structure of the data returned. No gaps identified.
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% with descriptions. The description adds extra context: explains the playbook structure (3 phases, 12 gates) and elaborates on the playbook parameter with real-world transformations. Adds value beyond the schema.
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 returns a structured 90-day playbook or a specific week/gate, and lists available playbooks with context. It distinguishes from siblings like classify_use_case and crosswalk.
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?
Explicitly states 'Use to walk a user through implementation sequentially, or to extract a specific gate's requirements.' It does not provide exclusions or alternatives, but the context is clear.
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?
No annotations exist, so description carries the transparency burden. It discloses that mappings include overlap classification (FULL/PARTIAL/NEW) and practitioner notes. It also reveals that source data is maintained externally. However, it does not mention any restrictions, permissions, or more detailed output expectations.
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-structured: purpose first, then framework list, then usage guidance, then source hint. It is somewhat lengthy but each sentence carries important information. A minor trim of the framework list could improve conciseness.
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 tool with 5 parameters and no output schema or annotations, the description covers key aspects: mapping behavior, classification, list_all_frameworks fallback, and data source. It could mention that results are returned as a mapping list, but overall it is sufficiently complete.
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?
With 100% schema coverage, the description adds significant semantic value: it explains the interaction between from_framework and from_reference (searches across all if from_framework omitted with from_reference), that to_frameworks filters targets, and that list_all_frameworks overrides all other parameters. This exceeds the schema's brief descriptions.
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 and resource: 'Map a regulatory requirement to its equivalents across other frameworks.' It enumerates covered frameworks and provides context about mapping classification, which clearly distinguishes it from sibling tools like classify_use_case or list_regulations that perform different functions.
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 explicitly states when to use: 'Use whenever a user has work in one framework and needs to know what carries over to another.' It also identifies high-leverage scenarios and explains the list_all_frameworks fallback for unsure users. This provides strong 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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