netlab-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: topology generation, known-good retrieval, host check, example listing, compatibility query, config rendering, failure reporting, and lab validation. No overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., generate_topology, validate_in_lab, report_failure). Naming is predictable and readable.
Tool Count5/5
With 8 tools, the set is well-scoped for a netlab MCP server. Each tool is justified and covers a specific step in the workflow without being excessive or insufficient.
Completeness5/5
The tool surface covers the full lifecycle: topology generation, config rendering, validation, host readiness, compatibility queries, failure reporting, and example browsing. No obvious missing operations for the intended domain.
Average 3.7/5 across 8 of 8 tools scored. Lowest: 2.7/5.
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
- Last stable release on June 9, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
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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?
With no annotations, the description carries the burden. It discloses that the tool is read-only (querying) and overlays two data sources, flagging conflicts. However, it doesn't discuss permissions, rate limits, or the exact conflict detection logic.
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 (3 short sentences) and front-loaded with the core purpose. The use of backticks and asterisks adds clarity for 'declared', 'observed', and 'conflicts'. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (combining two data sources with optional filtering), the description omits crucial details: how to filter by module/platform, output structure, conflict criteria, and relationship to sibling tools. An output schema exists but is not described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the description does not explain the purpose or expected values of the 'module' and 'platforms' parameters. A user cannot infer how to use these parameters from the description alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: querying compatibility data by overlaying declared (netlab) and observed (lab) supports. It distinguishes itself from sibling tools like validate_in_lab by focusing on data comparison and conflict flagging.
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?
The description explains the data sources (declared from netlab, observed from prior runs) but provides no guidance on when to use this tool versus alternatives like get_known_good or validate_in_lab. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It only states it records a result, but does not explain side effects (e.g., overwriting, idempotency, validation) or requirements (e.g., authentication). This is insufficient for a write operation.
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 a single concise sentence. However, it sacrifices essential information (parameter explanations, behavioral notes). While efficient, it is too brief to be fully helpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters (4 required) with no schema coverage. Despite having an output schema, the lack of parameter descriptions and behavioral transparency means the agent cannot reliably invoke this tool. The description is incomplete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description does not explain any of the 5 parameters (module, platforms, topology_yaml, error, stage). An agent cannot know format or constraints, making correct invocation unlikely.
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 action 'Record a negative result' and specifies the target 'compatibility matrix'. It distinguishes this tool from siblings like query_compatibility or validate_in_lab by focusing on reporting failures.
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 when a combo fails but does not explicitly state when to use this tool vs alternatives, nor does it mention when not to use it. No guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states it returns data 'if any'. It does not disclose whether the tool is read-only, what happens on missing data, or any side effects, leaving behavioral assumptions ambiguous.
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?
The description is a single front-loaded sentence of 13 words, efficiently conveying the core purpose without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, 0% coverage, no annotations) and the presence of an output schema, the description still lacks context on what defines a 'known good' entry, how the lookup works, and what the rendered config includes, making it insufficient for a first-time user.
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?
With 0% schema description coverage, the description should clarify parameter meanings. It mentions 'module+platform' but does not explain the parameters, particularly netlab_version which is optional, nor does it provide examples 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 that the tool returns a previously lab-passed topology and rendered config for a given module and platform. It effectively distinguishes this from sibling tools like generate_topology (which creates new) and render_config (which may not be limited to known-good).
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 retrieving known-good results, but does not explicitly state when to use this tool versus alternatives, nor does it provide conditions 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key side effects (lab teardown, persistent matrix row) and return components. However, it contains an apparent contradiction: it states 'The lab is always torn down afterward' but the schema includes a 'keep_lab' parameter (default false) that suggests the lab may be kept. This undermines transparency slightly. No annotations were provided, so the description carries full burden.
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 brief (3 sentences) and front-loads the primary action. The contradiction slightly harms clarity, but overall it is efficient and avoids unnecessary verbosity.
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?
The tool has an output schema (not shown) which reduces the need to detail return values. The description covers key outcomes and persistence but omits parameter details and has a contradiction. Given the parameter count (6), more completeness would be beneficial.
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 coverage is 0%, meaning the description must explain parameter meanings. It only implicitly covers 'topology_yaml' (the topology) and possibly 'platforms'. It fails to explain 'module', 'scenario', 'keep_lab', and 'timeout_s', leaving the agent to guess their roles in the validation process.
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 specifies the action ('validate'), the resource ('topology in containerlab'), and the process (deploy, run netlab validate, record verdict). It distinguishes this tool from siblings like 'generate_topology' and 'host_check' by focusing on validation and persistence.
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 prerequisites (Docker, containerlab, Linux host) and gracefully degrades to 'unavailable' without them. It implies usage for validation scenarios but does not mention alternatives or when not to use, though the context is clear enough for an agent.
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?
Given no annotations, the description discloses the offline nature, the data-model transform + render process, and the return structure. Lacks fuller detail on side effects or permissions, but provides adequate behavioral context for an apparent read-only compute.
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 concise sentences: first states purpose with key qualifiers, second details return structure and process. No fluff; 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?
Despite lacking schema descriptions, the description covers core functionality, offline mode, transformation process, and return structure. With an output schema, return details are covered elsewhere. Minor gaps in parameter explanation prevent a 5.
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%, yet the description does not explain what topology_yaml expects (e.g., YAML string content) or how nodes filters devices. Baseline with no schema descriptions would be 1, but the description gives minimal hint that input is a netlab topology, raising it to 2.
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?
Clearly states verb 'Render' and resource 'real per-device config from a netlab topology'. The offline, no-container detail distinguishes it from sibling tools that likely involve live environments, such as validate_in_lab.
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?
Implies usage for offline config generation but no explicit when-to-use or when-not-to-use guidance. Sibling tools like host_check and validate_in_lab suggest different contexts, but the description does not directly contrast them.
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 burden. It discloses the two operational modes but does not mention side effects, authentication needs, or error handling. A 3 is appropriate as it is minimally transparent.
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?
The description is two sentences with no wasted words. It front-loads the overall purpose and then details the parameter behavior. Highly concise and well-structured.
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 simplicity (1 optional parameter, output schema present), the description covers the two modes completely. The output schema handles return details, so no further explanation is needed.
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%, requiring the description to compensate. It explains the 'module' parameter behavior (null vs string), adding meaningful context that the schema lacks. This justifies a score above baseline.
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 indexes netlab's integration test topologies and explains the two distinct behaviors with and without the module parameter. It differentiates from sibling tools like generate_topology or validate_in_lab by focusing on listing/indexing.
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 guides usage with and without the module parameter. It does not provide exclusion criteria or mention alternatives, but the behavior is well-defined for a listing tool.
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?
The description discloses key behaviors: the output is parse-validated YAML, the module is inferred, and platform list order matters (DUT first). With no annotations provided, the description carries the full burden and covers the main behavioral aspects, though it doesn't mention any potential side effects or permissions required.
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?
The description is concise (two sentences plus brief elaboration) and front-loaded with the main purpose. Every sentence adds value: purpose, parameter guidance, and downstream usage. No unnecessary 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 has 2 parameters, an output schema, and no nested objects, the description covers all necessary aspects: what the tool does, what inputs mean, and how to use the output. It is fully complete for an agent to select and invoke the tool correctly.
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?
The description adds significant meaning beyond the input schema: it explains that 'intent' is free text with examples, and 'platforms' is a NOS list with DUT-first ordering and an MVP set. Schema coverage is 0% but the description fully compensates, providing clear semantics for both parameters.
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: 'Turn an intent + target platforms into a netlab topology YAML (parse-validated).' It uses a specific verb ('Turn') and resource ('netlab topology YAML'), and distinguishes itself from siblings by mentioning that the output should be fed to 'render_config' or 'validate_in_lab'.
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 context by explaining that the output is intended for downstream tools ('render_config' or 'validate_in_lab'), but it does not explicitly state when to use this tool versus alternatives like 'list_examples' or 'query_compatibility'. It gives examples of intent but no exclusions.
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 discloses what the tool reports: Docker/containerlab availability, versions, allowed platforms, locally loaded images, and validate anchor devices. It does not mention side effects, but as a diagnostic tool, side effects are unlikely.
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?
The description is extremely concise: two sentences with no wasted words. The first sentence is a clear call-to-action, and the second enumerates the diagnostic outputs.
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 no-parameter diagnostic tool, the description covers its purpose, usage timing, and output details. An output schema exists, so the description need not detail return values, making it complete.
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 tool has zero parameters, so the input schema is fully covered. The description adds value by explaining what information the tool gathers and reports, going beyond the trivial 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 diagnoses host lab readiness and should be run first when failures occur. It distinguishes itself from sibling tools like generate_topology and validate_in_lab by positioning itself as a first diagnostic step.
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 advises running this tool 'first when anything fails,' providing clear when-to-use guidance. It lacks explicit when-not-to-use instructions, but the context and sibling list imply its role as a preliminary check.
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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