Agent Policy Gateway MCP Server

by AiAgentKarl

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct and non-overlapping purpose: policy enforcement, legal compliance, PII scanning, emergency stop, action logging, and audit retrieval. No two tools could be confused for the same operation.
Naming Consistency4/5
Most tools follow a verb_noun pattern (apply_guardrails, check_compliance, check_pii, get_audit_log, log_action), but 'emergency_stop' is a noun phrase without a leading verb, breaking the pattern slightly.
Tool Count5/5
Six tools is well-scoped for a policy gateway server, covering guardrails, compliance, PII, emergency stop, and audit without feeling too few or excessive.
Completeness4/5
Core use cases are covered: policy enforcement, legal checks, PII scanning, emergency stop, and audit logging. Minor gaps exist (e.g., no explicit tool for human approval or policy configuration), but agents can still function with these tools.
Average 4/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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}
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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?
Description mentions append-only nature and file location (~/.agent-audit-log/), but lacks details on authentication, rate limits, or what happens on duplicates. No annotations provided, so burden is on description.
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?
Description is concise at 5 lines of German plus Args/Returns section. Mixed language is acceptable but could be more consistent. Every part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 0% schema coverage, description covers purpose, parameters, and return fields. Missing error conditions and example usage.
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 0%; description adds example values for action and explains details as additional details. However, it doesn't specify format constraints or allowed values beyond the examples.
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?
Description uses clear verb 'loggt' and specific resource 'Agent-Aktion in ein Append-Only Audit-Log'. It distinguishes itself from sibling tools like get_audit_log (read) and check_compliance (different purpose).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives like check_compliance or emergency_stop. Usage is implied but not stated.
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 that the tool checks policies and returns a decision, but it does not explicitly state whether the tool is read-only or if it has side effects. The term 'prüft' suggests checking, but safety implications are not clarified.
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 sections for parameters and return values, and each sentence adds value. It is front-loaded with the main purpose. Minor redundancy in listing checks after the first sentence, but overall concise.
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 and no annotations, the description is fairly complete. It explains return values (allowed, decision, reason, policy_checked) and context fields. For a guardrail tool with 2 parameters, this is sufficient.
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?
With 0% schema coverage, the description compensates well by detailing the 'action' parameter with examples and explaining the 'context' object fields (amount_usd, domain, api_calls_this_minute, custom_policies). This adds significant meaning 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 the tool checks if an agent action is allowed according to configurable policies, listing specific checks (spend limits, allowed domains, blocked actions, actions needing human approval, API rate limits). This distinguishes it from siblings like check_compliance, check_pii, emergency_stop, etc.
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 for any action requiring policy verification but does not explicitly state when to use this tool versus alternatives or when not to use it. No contrast with sibling tools is provided.
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, description carries full burden. It discloses output fields, known risk categories, and that only EU jurisdiction is currently supported. No mention of side effects, but tool appears to be a read-only query.
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-structured with Args and Returns sections, front-loaded with purpose. A bit verbose with full risk category list, but each 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?
No output schema, so description compensates by listing all return fields. Parameter semantics are covered, and jurisdiction limitation is mentioned. Sufficient for a query tool.
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 0%, but description adds meaning: explains action_type as 'Art der Aktion' with examples, and notes jurisdiction defaults to 'EU' and is currently limited. Provides context beyond 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?
Description clearly states the tool checks if an action type has special requirements under EU AI Act or DSGVO, with specific verb 'prüft' and resource 'Aktionstyp'. It distinguishes from siblings like check_pii or apply_guardrails by focusing on compliance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like check_pii or apply_guardrails. Does not mention when not to use it or provide context for 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 provided, the description carries the full burden. It states the tool reads the JSONL file and returns the newest entries first, implying non-destructive behavior. However, it lacks details on permissions, error handling, or file size limits, so transparency is moderate.
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 structured with a header, behavior sentence, args, and returns. It is reasonably concise at 6 lines but includes redundant phrases like 'Liest die JSONL-Audit-Datei' which is implied by the purpose. Slightly more compact wording would improve this.
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, the description covers purpose, parameters, and return values (including structure). It adds use-case context and ordering. Missing details like pagination or error conditions are minor, making it largely complete for a read-only log retrieval tool.
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 description adds meaningful explanations for both parameters ('agent_id' as unique agent ID, 'limit' as max entries with default 50) beyond the schema's minimal info. Since schema coverage was 0%, the description compensates well, though it could also specify the data type more precisely.
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 audit log entries for a specific agent, reads the JSONL audit file, and returns the latest entries. It differentiates from sibling tools like 'log_action' or 'check_compliance' by focusing on reading historical logs for compliance and incident 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?
The description explicitly mentions use cases ('Compliance-Reviews und Incident-Analyse'), providing context for when to use the tool. It does not specify when not to use it or mention alternatives, but the purpose is sufficiently clear to guide selection.
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 must convey all behavioral traits. It explains that an audit log entry is created and a kill-switch signal is returned, and that the agent must stop immediately. This is transparent, though it could further clarify any irreversible consequences.
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 a short paragraph followed by Args and Returns sections. It is not overly verbose and presents information clearly. Minor improvements could tighten the language.
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 provides return fields with descriptions, which is helpful. It covers the main purpose and effect. However, it lacks prerequisites or permissions information, which would enhance completeness for a critical tool.
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 input schema has 0% description coverage, but the tool description includes an Args section that explains agent_id and reason. This adds substantial meaning beyond the schema, though it does not detail expected formats or constraints.
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 'Löst einen Notfall-Stopp für einen Agent aus' (triggers an emergency stop for an agent). It specifies the verb (trigger) and resource (agent), and the function is distinct from siblings like apply_guardrails or check_compliance.
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 instructs that the calling agent MUST stop all actions upon receiving the kill-switch signal. This provides clear usage context. However, it does not explicitly mention when not to use it or list alternatives, which would be beneficial.
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 carries the full burden. It discloses that the tool detects specific PII types and returns a redacted version, implying no side effects. However, it does not explicitly state that the tool is read-only or mention any authorization or rate limits.
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 concise with a clear structure: a brief explanation, a list of detected PII, and a structured Args/Returns section. It is front-loaded and every sentence adds value, though it could be slightly more streamlined.
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 single-parameter tool with no output schema or annotations, the description covers input purpose, output structure (found_pii, redacted_text, etc.), and usage context. It is complete enough for agent invocation.
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 only parameter 'text' is described as 'Der zu prüfende Text' in the description, providing meaning beyond the schema which only has a title and type. The description adds context that the text is to be scanned for PII, which aids correct usage.
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 scans text for PII, lists specific data types (emails, phone numbers, SSNs, etc.), and explains its output. It distinguishes itself from siblings like apply_guardrails by focusing on PII detection before sending to external services.
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 tells when to use the tool: 'Nutze dieses Tool BEVOR du Text an externe Services sendest.' It does not specify when not to use or mention alternatives, but the directive is clear and actionable.
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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