@palveron/mcp-server
Server Quality Checklist
Latest release: v1.0.4
- Disambiguation5/5
Each tool has a distinct purpose: palveron_check verifies a single action, palveron_status provides system diagnostics, and palveron_policy_list lists policies. There is no ambiguity or overlap between them.
Naming Consistency4/5All tools share the consistent palveron_ prefix and snake_case format, but the second word is a mix of verb (check) and nouns (status, policy_list). Minor deviation from a strict verb_noun pattern.
Tool Count5/5Three tools is well-scoped for a governance checker: one to perform checks, one for status, and one for policy listing. No tools feel redundant or missing.
Completeness5/5The domain is governance policy enforcement, and the set covers the core operations: enforcing a policy (check), understanding system state (status), and discovering applicable policies (list). No obvious gaps for the stated purpose.
Average 4/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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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 are present, so the description carries the burden of behavioral disclosure. It adds useful context about filtering to 'active' policies and scoping to the 'current project', and lists output fields. However, it does not mention behavior around the environment parameter, default settings, pagination, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with front-loaded action ('List...'), followed by output details. Every word adds value, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given 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 adequately covers the core purpose and output content. It could be enhanced by noting the environment filter's effect, but this is documented in the schema. Overall, it is sufficiently complete for an agent to select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'environment' has a schema description covering its default and purpose, achieving 100% schema description coverage. The tool description adds no additional parameter semantics, 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.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists active governance policies for the current project, with a specific verb ('List') and resource ('active governance policies'). It also enumerates the output details (policy names, enforcement actions, content type scopes), making its purpose distinct from sibling tools like palveron_check and palveron_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need to view governance policies, but provides no explicit guidance on when to use this tool versus siblings or when it should not be used. There are no exclusions or alternative tool references.
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 explicitly lists what the tool shows (connection state, circuit breaker status, configured policies, gateway health), giving a clear picture of its read-only, diagnostic nature. It doesn't describe potential side effects or prerequisites, but none are expected for a status tool, and the listed outputs provide solid transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct and well-structured. It opens with a clear, front-loaded purpose statement, followed by a concise list of diagnostic outputs. Every word adds value with no redundancy or filler, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete for a status tool with no parameters and no output schema. It enumerates the key areas of diagnostic information (connection, circuit breaker, policies, gateway health), giving the agent a clear understanding of what the tool returns. It doesn't include extensive detail like exact response format, but for a status/diagnostics tool, this is adequate and not a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters (0 params), so the baseline is 4. The description correctly adds no parameter details since none exist. The input schema is empty, and the description is consistent, not requiring any compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get PALVERON governance status and diagnostics.' It uses a specific verb ('Get') and resource ('PALVERON governance status'), and lists the specific diagnostic areas (connection state, circuit breaker, policies, gateway health). This distinguishes it from siblings like palveron_check and palveron_policy_list, which focus on checking or listing policies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus its siblings. It doesn't mention alternatives or exclusions. While the purpose is clear, it says nothing about scenarios where palveron_check or palveron_policy_list might be more appropriate, leaving the agent without explicit usage 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?
With no annotations provided, the description fully bears the transparency burden. It discloses the return categories (allowed, blocked, modified), PII redaction, and the creation of an immutable audit trace, giving the agent a clear picture of side effects. It does not mention rate limits or failure modes, but for a governance check this is reasonably complete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core action, and each sentence adds value: what it does, what it returns, and the audit side effect. No redundant or vague language is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is moderately simple (4 params, no output schema), and the description covers purpose, usage context, outcome types, and side effects. It lacks explicit examples or detailed return structure, but these are not essential given the schema and the clear description. The description is sufficiently complete for an agent to choose and invoke the tool appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with descriptions for all four parameters. The description itself does not add parameter-level detail beyond the schema, but it does frame the purpose of the tool/input parameters. This meets the baseline for full schema coverage, with no additional semantics provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a specific verb+resource combination: 'Verify a tool call against PALVERON governance policies before execution.' This clearly distinguishes the tool from siblings like palveron_status and palveron_policy_list, focusing on pre-execution checking rather than status or policy listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'before execution' clearly establishes when to use the tool (prior to running a tool call). It also describes the outcome (allowed/blocked/modified) which implies appropriate usage as a gate. However, it does not explicitly name alternatives or provide conditions for when not to use it, so it lacks explicit exclusions.
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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- Evaluate tool definition quality.
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