corporate-mcp-system-inspector
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a clearly separate subsystem: identity, resources, processes, and network. There is no functional overlap or ambiguity between the tool names and their stated purposes.
Naming Consistency4/5Tool names follow a readable noun-based pattern, with system_ prefix used for two tools and _summary suffix used for the other two. While not perfectly uniform, the naming is predictable and easy to reason about.
Tool Count5/5Four tools is a well-scoped count for a system inspection server. Each tool covers a distinct high-level concern without unnecessary fragmentation or redundancy.
Completeness4/5The surface covers the core system inspection areas: identity, resources, processes, and network. Minor gaps such as detailed disk I/O or user/session information exist, but they are not essential for basic system inspection.
Average 3.8/5 across 4 of 4 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
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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 provided, so the description carries some burden. It discloses the tool is a read-only introspection operation by saying 'Return', which is helpful. However, it doesn't disclose what specific fields are included (OS name, version, architecture, runtime version, etc.) or whether any side effects may occur—though for an identity tool, side effects are unlikely and the lack of parameters supports a read-only assumption.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that is front-loaded with the verb and resource. It is appropriately concise for a zero-parameter introspection tool. It could perhaps enumerate the identity details it returns, but it does not waste words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only identity tool, the description is mostly complete. However, there is no output schema, so the agent does not know what specific identity fields will be returned (e.g., OS type, kernel version, runtime name, runtime version). This is a modest gap given the tool's simplicity.
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?
There are zero parameters, so the schema is trivially complete. The description adds no parameter details because none exist. This is a no-argument tool, so parameter semantics are inherently clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns operating-system and runtime identity. It uses a specific verb (Return) and a distinct resource. However, it doesn't explicitly distinguish itself from siblings like process_summary or network_summary, though the 'system_identity' name makes the distinction reasonably clear.
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: call this when you need OS and runtime identity. It provides no explicit guidance on when not to use it or alternatives like system_resources, process_summary, or network_summary. The implied context is adequate but not 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 carries the behavioral disclosure burden. 'Return ... observations' implies a read-only, non-mutating operation and 'local' scopes its impact. It does not explain what 'bounded' means or whether any permissions or side effects exist, but for a zero-parameter summary tool this is minimally adequate.
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 a single sentence with no filler, front-loading the verb and object. Every word contributes, even if 'bounded' could have been defined more precisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description gives the broad domain of the returned data but, without an output schema, it leaves an agent to infer the exact fields and the meaning of 'bounded'. For a simple no-parameter tool this is adequate, but it would be stronger with a note about the output shape or the bound's significance.
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 and the schema has 100% coverage of that fact, so the description does not need to document parameter semantics. The baseline of 4 applies because there is nothing for the description to add.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Return' and identifies the resource as local interface and address observations, which separates it from sibling tools like process_summary and system_resources. The word 'bounded' is somewhat ambiguous, but the network focus is still clear.
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?
Usage context is implied by the network resource and the summary-like name, so an agent can infer this is for local network interface/address information. However, there is no explicit guidance about when not to use it or which sibling tool might be a better alternative for system-level or process-level summaries.
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 the behavioral disclosure burden. It does disclose that output is aggregated and that command lines are excluded, but it does not explicitly state read-only behavior, return fields, or privilege requirements. Minimal but not misleading.
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?
A single front-loaded sentence with no filler. Every word adds meaning: 'Return' gives the action, 'aggregate process information' gives the scope, and 'without command lines' gives a key exclusion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a no-argument summary tool, but because there is no output schema, the exact return shape is ambiguous ('aggregate process information' is broad). It tells enough to invoke the tool but not enough to fully anticipate what fields or structure the response will contain.
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, so there is no parameter documentation needed. The schema coverage is effectively 100% for an empty parameter set, so the description adds all necessary semantic context.
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 uses a specific verb ('Return') and resource ('aggregate process information'), and adds a meaningful qualifier ('without command lines'). It clearly maps to the process domain and is easy to distinguish from siblings about identity, resources, and network.
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?
No explicit timing, conditions, or alternative tools are stated. The domain ('process information') implies when to use it, and 'without command lines' implies a when-not, but there is no direct comparison to sibling 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?
With no annotations, the description carries the full burden, but 'Return' strongly implies a read-only observation operation with no side effects. It does not disclose units, sampling behavior, potential permissions, or whether the data is real-time or cached, but for a simple zero-parameter read tool this is mostly adequate.
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 a single efficient sentence that front-loads the action and resource list. Every word earns its place, with no repetition of the tool name or schema contents.
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 zero-parameter read-only tool, the description offers enough to select and invoke it correctly. It identifies the exact resources observed, though it does not describe the shape, units, or format of the returned observations since no output schema is provided.
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, so per the baseline rule there is nothing for the description to explain. The empty schema already fully documents the parameter surface, and the description correctly avoids inventing parameter details.
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 names a specific verb ('Return') and a specific resource set (CPU, memory, local disk), making the tool's scope clear. This also distinguishes it from sibling tools like network_summary and process_summary, which cover different resource categories.
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 use for retrieving system resource observations, and the sibling names suggest alternatives for process, network, and identity data. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it.
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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