What can you do with this server?

The AI BOM MCP server provides tools to generate, audit, and map AI Bills of Materials (AI-BOMs) for compliance and governance purposes. * Generate AI-BOMs: Create structured AI-BOM documents in CycloneDX ML-BOM or SPDX 3.0 format covering all 10 required field categories, including model name, version, organisation, licence, architecture, parameter count, and training datasets. * Audit AI-BOM Completeness: Analyse an existing AI-BOM JSON document against the 10 required field categories, returning per-category pass/fail results and a gap list of missing or incomplete fields. * Map to Regulation: Map an AI-BOM against regulatory framework requirements, supporting EU AI Act, NIST AI RMF, US EO 14028, and ISO 42001. * List Required Fields: Retrieve the full list of the 10 required AI-BOM field categories and their associated fields as a compliance reference. * Signed Attestations (Pro/Enterprise only): Generate cryptographically signed (HMAC-SHA256) AI-BOM completeness attestations with a unique ID and public verification URL for independent auditor verification.

Which integrations are available for this server?

Generates AI-BOMs in SPDX 3.0 format, providing a standard way to document AI software bill of materials.

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AI BOM MCP

by CSOAI-ORG

Overview Schema Related Servers Score Discussions

Python

Remote

MCP Scorecard: 84/100

Ai Bom MCP

MEOK AI Labs EU AI Act License PyPI

AI Bill of Materials MCP in CycloneDX + SPDX format

AI Bill of Materials MCP in CycloneDX + SPDX format. Required by EU AI Act Article 11. MIT

🚀 Quick Start

# Install via pip
pip install ai_bom_mcp

# Or install via Smithery
npx -y @smithery/cli@latest install ai-bom-mcp --client claude

Related MCP server: scan-your-ai-toolkit

✨ Features

MCP protocol compliant
Easy installation
Well-documented API
Production-ready
Active maintenance

📖 Documentation

🛡️ Compliance

This MCP server is built with EU AI Act compliance built-in:

✅ Article 9 — Risk Management System
✅ Article 13 — Transparency & Instructions for Use
✅ Article 15 — Bias Detection & Testing
✅ Article 26 — FRIA Support (where applicable)
✅ Article 50 — AI Content Watermarking (where applicable)

Need help getting compliant? Book a free 15-min diagnostic →

🏢 Enterprise

Need custom development, SLA guarantees, or white-label deployment?

Pro: £79/mo — Full MCP suite + EU AI Act tracking
Enterprise: £499/mo — Custom dev + SLA + Dedicated support

View Pricing → | Contact Sales →

🤝 Part of the MEOK Ecosystem

This server is part of the MEOK AI Labs ecosystem — 26 PyPI packages · ~16,300 monthly installs.

Domain	Purpose
councilof.ai	EU AI Act compliance marketplace
safetyof.ai	AI safety & monitoring
meok.ai	Sovereign AI platform
cobolbridge.ai	Legacy modernization

📜 License

MIT © CSOAI-ORG

Configuration

Add to your claude_desktop_config.json (Claude Desktop) or your MCP client config:

{
  "mcpServers": {
    "ai-bom-mcp": {
      "command": "uvx",
      "args": ["ai-bom-mcp"]
    }
  }
}

Or: pip install ai-bom-mcp then run the ai-bom-mcp command (stdio transport).

Examples

Once configured, ask your assistant, for example:

"Use generate_ai_bom to …"
"Use audit_ai_bom_completeness to …"
"Use map_to_regulation to …"

Available Tools

4 tools

audit_ai_bom_completenessA

Audit an existing AI-BOM for completeness against the 10 required field categories. Returns per-category pass/fail + gap list.

Behavior: This tool is read-only and stateless — it produces analysis output without modifying any external systems, databases, or files. Safe to call repeatedly with identical inputs (idempotent). Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

Args: ai_bom_json (str): The ai bom json to analyze or process. api_key (str): The api key to analyze or process.

Behavioral Transparency: - Side Effects: This tool is read-only and produces no side effects. It does not modify any external state, databases, or files. All output is computed in-memory and returned directly to the caller. - Authentication: No authentication required for basic usage. Pro/Enterprise tiers require a valid MEOK API key passed via the MEOK_API_KEY environment variable. - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are included in responses (X-RateLimit-Remaining, X-RateLimit-Reset). - Error Handling: Returns structured error objects with 'error' key on failure. Never raises unhandled exceptions. Invalid inputs return descriptive validation errors. - Idempotency: Fully idempotent — calling with the same inputs always produces the same output. Safe to retry on timeout or transient failure. - Data Privacy: No input data is stored, logged, or transmitted to external services. All processing happens locally within the MCP server process.

ParametersJSON Schema

Name	Required	Description	Default
`ai_bom_json`	Yes
`api_key`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.4/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full weight and excels: it covers side effects (read-only, no modifications), authentication (none for basic), rate limits (10/day free), error handling (structured errors), idempotency, and data privacy. This is comprehensive and exceeds typical annotation coverage.

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 with clear sections and front-loaded key information. However, there is redundancy between the 'Behavior' and 'Behavioral Transparency' sections, making it slightly longer than necessary. Overall, it is organized and each part serves a purpose.

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 (2 params, output schema present), the description covers all essential aspects: input, output (pass/fail + gap list), behavioral traits, rate limits, and limitations. The output schema exists, so not detailing return format is acceptable. It is fully sufficient for an agent to use correctly.

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 has 0% description coverage, so the description must compensate. The 'Args' section provides brief descriptions ('The ai bom json to analyze or process') which adds minimal value beyond the title. The overall context helps, but parameter-specific detail is lacking.

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 audits an AI-BOM for completeness against 10 required field categories and returns pass/fail and gap list. This specific verb+resource+scope distinguishes it from siblings like generate_ai_bom (creation) and map_to_regulation (mapping).

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 explicit 'When to use' and 'When NOT to use' sections, offering context for appropriate usage and cautioning against real-time decisions without human review. However, it does not directly compare to sibling tools, missing a chance to differentiate further.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_ai_bomA

Generate an AI-BOM in CycloneDX ML-BOM format (or SPDX 3.0) with all 10 required field categories. Provides the skeleton for compliance submission.

Behavior: This tool generates structured output without modifying external systems. Output is deterministic for identical inputs. No side effects. Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

Args: model_name (str): The model name to analyze or process. model_version (str): The model version to analyze or process. organisation (str): The organisation to analyze or process. licence (str): The licence to analyze or process. architecture (str): The architecture to analyze or process. parameter_count (str): The parameter count to analyze or process. training_datasets (str): The training datasets to analyze or process. format (str): The format to analyze or process. api_key (str): The api key to analyze or process.

ParametersJSON Schema

Name	Required	Default
`model_name`	Yes
`model_version`	No	1.0.0
`organisation`	No	MEOK AI Labs
`licence`	No	Apache-2.0
`architecture`	No	Transformer
`parameter_count`	No	unknown
`training_datasets`	No
`format`	No	cyclonedx
`api_key`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.4/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully covers behavior: read-only, deterministic, no side effects, authentication, rate limits, error handling, idempotency, and data privacy.

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?

Well-organized with clear sections and front-loaded purpose, though the 'Args' block is somewhat redundant given schema titles.

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 complexity (9 params, no schema descriptions) and presence of output schema, the description covers usage, behavior, and output format adequately, but parameter semantics remain weak.

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

Parameters2/5

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

Schema description coverage is 0%, but the 'Args' section only repeats parameter names with generic 'to analyze or process' phrasing, adding minimal meaning. Some descriptions may be misleading (e.g., api_key).

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 generates an AI-BOM in CycloneDX ML-BOM or SPDX 3.0 format for compliance, distinguishing it from siblings like audit_ai_bom_completeness.

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?

Explicit 'When to use' and 'When NOT to use' sections provide clear guidance, including exclusion for real-time decisions without human review.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

map_to_regulationA

Map an AI-BOM against a specific regulatory framework's technical documentation requirements. Supported: eu_ai_act, nist_ai_rmf, us_eo_14028, iso_42001.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

Args: ai_bom_json (str): The ai bom json to analyze or process. regulation (str): The regulation to analyze or process. api_key (str): The api key to analyze or process.

ParametersJSON Schema

Name	Required	Default
`ai_bom_json`	Yes
`regulation`	No	eu_ai_act
`api_key`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully details behavior: read-only, stateless, idempotent, safe to repeat, rate limits (10/day free), authentication needs, error handling (structured errors), and data privacy (no storage/logging). This far exceeds typical transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with headings but is verbose, especially the behavioral transparency section which repeats information from the earlier behavior list. While comprehensive, it could be more concise.

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 presence of an output schema, the description covers all necessary aspects: purpose, parameters, behavior, rate limits, error handling, idempotency, and data privacy. Nothing critical is missing for an AI agent to use this tool correctly.

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?

Despite 0% schema description coverage, the description explains each parameter in an 'Args' section: ai_bom_json, regulation (with default eu_ai_act), and api_key. While explanations are somewhat generic, they add meaningful context beyond the schema's bare definitions.

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: mapping an AI-BOM against a specific regulatory framework's technical documentation requirements. It lists supported frameworks (eu_ai_act, nist_ai_rmf, us_eo_14028, iso_42001) and is distinct from siblings like audit_ai_bom_completeness or generate_ai_bom.

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 includes explicit 'When to use' and 'When NOT to use' sections, guiding the AI agent on appropriate contexts. It advises against real-time production use without human review, which sets clear expectations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

required_fieldsA

List the 10 required AI-BOM field categories and their fields.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

Args: api_key (str): The api key to analyze or process.

ParametersJSON Schema

Name	Required	Description	Default
`api_key`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.3/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully discloses behavioral traits: read-only/stateless, rate limits (free 10/day, Pro unlimited), no authentication required, idempotency, error handling returning structured errors, and data privacy (no storage/transmission). This is exceptionally thorough.

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 with clear sections (behavior, when to use, args, behavioral transparency). However, there is redundancy: the 'Behavior' section repeats the same read-only/stateless/rate limit info later in 'Behavioral Transparency.' Still, it remains organized and mostly front-loaded with the purpose.

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 listing tool, the description covers all necessary context: purpose, behavior, rate limits, authentication, error handling, idempotency, and data privacy. An output schema exists (not shown), but the description provides enough cues for safe invocation without needing it.

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

Parameters2/5

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

The input schema has 0% description coverage (no parameter descriptions), so the description must compensate. It only states 'api_key (str): The api key to analyze or process,' which adds minimal meaning—it does not clarify its optional role given 'no authentication required for basic usage.' The compensation is inadequate.

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 pair ('List the 10 required AI-BOM field categories and their fields'), clearly stating what the tool does. This distinct purpose differentiates it from siblings like 'audit_ai_bom_completeness' and 'generate_ai_bom' without needing explicit comparison.

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 dedicated 'When to use' and 'When NOT to use' sections, providing clear context for appropriate usage. It advises against real-time production use without human review, but does not explicitly name alternative sibling tools, so it lacks direct differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: auditing an existing BOM, generating a new BOM, mapping to regulations, and listing required fields. There is no ambiguity in their roles.

Naming Consistency3/5

Three tools follow a verb_noun pattern (audit_ai_bom_completeness, generate_ai_bom, map_to_regulation) but 'required_fields' is a noun phrase and not imperative, breaking the pattern. Also, 'map_to_regulation' includes a preposition while others do not.

Tool Count5/5

4 tools is well-scoped for an AI BOM server, covering generation, auditing, regulatory mapping, and field reference. Not overloaded or insufficient.

Completeness4/5

Covers the core lifecycle: generate, audit, and map to regulations. A minor gap is the lack of a tool for updating or converting between BOM formats, but the format parameter in generate_ai_bom partially addresses this.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

GitHub Repository

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