rancher-mcp-server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource or action: listing clusters, projects, workloads, pods, nodes; retrieving events, logs; diagnosing a workload; redeploying or scaling. No overlaps.
Naming Consistency5/5
All tools use an imperative verb followed by a noun (e.g., list_clusters, get_pod_logs, diagnose_workload). Names are uniformly snake_case and follow a predictable pattern.
Tool Count5/5
10 tools is right in the ideal range (3-15). The set covers listing, retrieval, diagnosis, and two write operations without being bloated or too sparse.
Completeness5/5
For a diagnostics and light remediation server, the tool surface is complete: full read coverage of relevant Rancher resources, log/event access, workload diagnosis, and write operations for redeploy and scaling. No obvious gaps.
Average 3.7/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
Without annotations, the description carries full burden. It discloses the default warning filter but does not specify pagination, ordering, or what 'recent' means. Some transparency is provided, but significant gaps remain.
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?
Single sentence, front-loaded with the core action, no unnecessary words or redundancy.
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 presence of an output schema and 4 parameters, the description is insufficient. It lacks details on what 'recent' means, how to use namespace or limit, and does not mention pagination. The agent needs more context to use the tool correctly.
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 must compensate. It only implicitly hints at the only_warnings parameter via 'Warning events by default' and does not explain limit, namespace, or cluster_id. This adds marginal value over 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 it lists recent Kubernetes events of a cluster, with a default for warning events. It is a specific verb+resource and distinguishes from sibling tools like get_pod_logs or diagnose_workload.
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 guidance on when to use this tool versus alternatives. It does not mention prerequisites or exclusions, leaving the agent to infer usage context from the name alone.
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?
With no annotations, the description must disclose behavioral traits. It only mentions the ability to fetch logs from a previous container, but does not state whether the operation is read-only, requires specific permissions, or has rate limits. These gaps leave the agent with insufficient behavioral context.
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 short sentences with no redundant words. It efficiently conveys the core function and a key parameter detail, demonstrating excellent conciseness.
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 has 6 parameters (3 required) and no parameter explanations, the description is too brief. It does not explain how to obtain or specify the required parameters (e.g., cluster_id, namespace, pod_name), nor does it mention the existence of an output schema. The minimal context leaves significant gaps for proper usage.
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 schema description coverage is 0%, so the description must compensate. It only hints at 'tail_lines' (via 'last N') and 'previous', but provides no explanation for the other four parameters (pod_name, container, namespace, cluster_id), leaving their semantics unclear beyond the schema names.
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 ('Fetch') and the resource ('last N log lines of a pod'), making the tool's purpose immediately evident. It also naturally distinguishes from sibling tools like get_events or diagnose_workload, which deal with other Kubernetes data.
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 provides a specific usage hint for the 'previous' parameter, but lacks guidance on when to choose this tool over alternatives (e.g., get_events for events or diagnose_workload for performance issues). There is no mention of prerequisites 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?
No annotations are provided, so the description carries full behavioral disclosure. It correctly indicates a write operation via the requirment, but does not detail effects like whether the redeploy is disruptive, asynchronous, or what happens to existing pods.
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 sentences, front-loaded with the core action, no redundant words. Every sentence serves a purpose: defining the action and stating a prerequisite.
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 the existence of an output schema, the description's minimalism is partially forgivable. However, it lacks context on the redeploy process (e.g., synchronous/asynchronous, what the return indicates), which would be helpful for a mutation tool.
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?
With 0% schema description coverage, the description must compensate but provides no information about the two required parameters (project_id, workload_id), leaving their semantics entirely to the parameter names.
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 verb 'Trigger a rolling redeploy' and the resource 'workload', and it distinguishes from sibling tools like scale_workload or list_workloads by specifying the action is equivalent to the Rancher UI 'Redeploy' button.
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 includes a precondition 'Requires RANCHER_MCP_ALLOW_WRITE=true', but provides no guidance on when to use this tool versus alternatives like scale_workload or diagnose_workload.
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 of behavioral disclosure. It describes the output fields but does not mention any side effects, error conditions, pagination, or the implicit read-only nature of the operation, leaving gaps in the agent's understanding.
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 clear sentence of 14 words, directly conveying the essential information without extraneous content. It is optimally structured for quick parsing.
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?
The description covers the basic purpose and return fields, and an output schema exists. However, it omits context such as that it lists all nodes in the specified cluster, and lacks any error or edge case handling cues, making it adequate but not thorough.
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 only parameter, cluster_id, is described in the schema only with a title 'Cluster Id'. The description reiterates 'a cluster' but adds no concrete meaning about format, valid values, or examples, failing to compensate for the low schema coverage.
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 (list), resource (nodes of a cluster), and the details returned (roles, state, requested resources). It distinguishes from sibling tools like list_clusters and list_pods by focusing on nodes within a specific cluster.
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 provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or context such as necessary permissions or cluster availability. This leaves the agent uncertain about appropriate invocation scenarios.
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, so the description carries the full burden. It does not disclose behavioral traits like idempotency, permissions, rate limits, or side effects. Only states the default value of only_unhealthy.
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 sentences that are front-loaded with the primary purpose. No redundant information; every sentence 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 the tool has 3 parameters (1 required) and an output schema, the description briefly covers one filter parameter but omits namespace and project_id. Output schema exists, so return values need not be described in detail, but the absence of namespace guidance is a gap.
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%. The description only explains the only_unhealthy parameter, leaving project_id and namespace undocumented. This is insufficient for a 3-parameter tool.
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 'List pods of a project', which is a specific verb+resource combination. It distinguishes from sibling tools like list_nodes or list_clusters by focusing on pods.
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?
Provides explicit guidance on when to use the only_unhealthy parameter ('to see only pods in CrashLoopBackOff/Error/Pending states or with more than 3 restarts'). Does not address when to use list_pods vs other list tools 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. While 'List' suggests read-only, the description does not explicitly state safety, permissions, or side effects. It adds only the workload inspection context but lacks behavioral depth.
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?
A single sentence of 19 words efficiently communicates the purpose and context. No unnecessary words; front-loaded with action.
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?
The description explains why the tool is needed (project IDs for workloads), which adds valuable context. However, it does not mention prerequisites, authorization, or output structure (despite output schema existing). Adequate for a simple list tool.
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%, so the description should compensate. It mentions 'cluster' but does not explain the format, source, or constraints of the cluster_id parameter. Minimal addition over 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 lists Rancher projects for a given cluster, which distinguishes it from siblings like list_clusters or list_workloads. It also explains the value (project IDs needed to inspect workloads).
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 implies usage context: you need project IDs before inspecting workloads. It provides clear context but does not explicitly exclude other uses or name alternative 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?
No annotations are provided, so the description carries full burden. It implies a read-only diagnostic action but does not explicitly state that it does not modify resources or mention any limitations (e.g., rate limits, result size). Partial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, both front-loaded with the core purpose and usage guidance. Every word serves a purpose with no redundancy.
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 presence of an output schema, the description appropriately summarizes key return elements (state, unhealthy pods, restart counts, warning events). It lacks mention of edge cases like missing workload, but overall sufficiently complete for a diagnostic tool.
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%, so the description must compensate. It does not describe the parameters (project_id, workload_id) beyond what the schema shows, providing no additional meaning or usage hints for those fields.
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 provides a one-shot diagnosis including state, unhealthy pods, restart counts, and warning events. It distinguishes itself as an 'ideal first call' for failures, implying a specific role among sibling tools.
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 says 'Ideal first call when something is failing,' providing clear usage context. However, it does not mention when to avoid this tool or specify alternatives among siblings, though the context signals list them.
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 does not disclose any behavioral traits such as pagination, rate limits, authentication requirements, or side effects. It only states the basic function, leaving the agent with limited insight into how the tool behaves.
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 that contains all essential information without any wasted words. It is concise and to the point.
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 that there are no parameters and an output schema exists to describe return values, the description provides sufficient context. It covers the action and the nature of the response.
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?
There are zero parameters, so the description adds no additional semantic value. The schema coverage is 100% by default, and a baseline of 4 is appropriate for this case.
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 verb 'List' and the resource 'all Rancher-managed clusters' with specific output details (state and capacity summary). This distinguishes it from sibling tools that list other resources like nodes, pods, or workloads.
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 the tool is for retrieving a list of clusters, but it does not explicitly state when to use it versus alternatives, nor does it provide any conditions or exclusions.
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 resource scope and optional filter but lacks details on pagination, rate limits, or permission requirements. It does not contradict any annotations.
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 with no wasted words. It efficiently communicates the tool's purpose and optional filter.
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 an output schema exists (details not shown), the description need not explain return values. It clarifies the scope (project) and workload types with examples. Minor gap: does not specify if other workload types like statefulsets are included.
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 description coverage is 0%, so the description compensates by explaining that project_id identifies the project and namespace is an optional filter. This adds meaningful context beyond the schema's type-only 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 lists workloads (deployments, daemonsets) of a project, with an optional namespace filter. It distinguishes itself from sibling tools like list_pods or diagnose_workload by specifying the resource type.
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 (list workloads of a project) but does not provide explicit guidance on when to use this tool vs alternatives like list_pods 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that scaling is a write operation (via the require flag) and is subject to a cap. This provides essential behavioral context for safe agent use.
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 sentences, each delivering distinct value: core action first, constraints second. No redundant or vague language.
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 description covers the main operation and constraints but omits guidance on how to obtain project_id and workload_id, and does not mention the output shape. The presence of an output schema reduces the burden somewhat, but for a 3-parameter tool with 0% schema coverage, more detail is expected.
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%, so the description must compensate. Only the 'replicas' parameter is vaguely described ('to N replicas'), while 'project_id' and 'workload_id' receive no explanation. The agent must infer their purpose from the tool name.
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 ('Scale'), the resource ('a workload'), and the quantity ('to N replicas'). This distinguishes it well from sibling tools like list_workloads and redeploy_workload.
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 requires RANCHER_MCP_ALLOW_WRITE=true, indicating when this tool is usable. The cap by RANCHER_MCP_MAX_REPLICAS provides constraints. However, it does not contrast with alternatives like redeploy_workload or advise when not to use.
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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