Foreman MCP Server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation3/5
The tools have some overlap in purpose, particularly around documentation fetching. 'fetch_foreman_dsl_docs' and 'Get Foreman DSL Documentation' both retrieve DSL documentation, though one fetches from source and the other from cache. 'call_foreman_api' is distinct as an action tool, while 'get_foreman_api_resource_docs' focuses on API resource documentation. The descriptions help clarify the differences, but the two DSL documentation tools could cause confusion.
Naming Consistency2/5The naming is inconsistent with mixed conventions. 'call_foreman_api', 'fetch_foreman_dsl_docs', and 'get_foreman_api_resource_docs' follow a snake_case pattern with verb prefixes, but 'Get Foreman DSL Documentation' uses a different style with spaces and title case. This deviation breaks the pattern and reduces predictability, though the core naming is still readable.
Tool Count4/5With 4 tools, the count is reasonable for a server focused on Foreman API interactions and documentation. It covers core operations like calling the API and fetching documentation, though it might feel slightly thin if more advanced actions are needed. The scope is well-defined, and each tool has a clear role, making the count appropriate for the apparent purpose.
Completeness3/5The tool set covers basic API calls and documentation retrieval, but there are notable gaps. It lacks operations for managing Foreman resources (e.g., create, update, delete) and does not provide full CRUD coverage. Agents can work around this by using 'call_foreman_api' for various actions, but the surface is incomplete for comprehensive Foreman management, focusing more on documentation and generic API access.
Average 2.7/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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 GPL 3.0.
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.
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.jsonto 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
- 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 of behavioral disclosure. It mentions a prerequisite ('Needs Foreman API resource to be available') but doesn't describe what the tool does beyond 'call an action', such as whether it performs read/write operations, authentication needs, rate limits, or error handling. This leaves significant gaps in understanding its behavior.
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 concise with two short sentences, front-loaded with the main purpose. There's no wasted text, but it could benefit from more detail given the complexity implied by the parameters and lack of annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain what the tool returns, how to structure parameters, or provide enough context for safe and effective use. The prerequisite hint is insufficient for a tool that likely performs API operations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter details. The description doesn't add any meaning to the parameters (resource, action, params) beyond what the schema titles imply. It doesn't explain what 'resource' or 'action' refer to, or what 'params' should contain, failing to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Call an action on a Foreman API resource', which provides a clear verb ('Call') and resource ('Foreman API resource'), but it's vague about what 'call an action' specifically entails. It doesn't distinguish from sibling tools like 'fetch_foreman_dsl_docs' or 'get_foreman_api_resource_docs', which appear to be documentation-related, but the distinction isn't explicitly stated.
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 includes 'Needs Foreman API resource to be available', which implies a prerequisite but doesn't provide explicit guidance on when to use this tool versus alternatives. There's no mention of when-not-to-use or how it differs from sibling tools, leaving usage context unclear.
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 of behavioral disclosure. It only states the action ('fetches') without detailing aspects like authentication needs, rate limits, error handling, or what the fetched documentation includes (e.g., format, scope). This is inadequate for a tool with no annotation coverage.
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 with no wasted words. It's front-loaded and appropriately sized for the tool's apparent simplicity, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It lacks details on behavior, parameter usage, and output, which are essential for an agent to use the tool effectively in context with sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 required parameter ('section') with 0% description coverage, meaning the schema provides no details about this parameter. The description adds no information about what 'section' means, valid values, or how it affects the fetch operation, 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.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('fetches') and resource ('DSL documentation from Foreman'), which clarifies the basic purpose. However, it's vague about what 'DSL documentation' entails and doesn't distinguish this tool from sibling tools like 'get_foreman_api_resource_docs' or 'Get Foreman DSL Documentation', which appear to serve similar documentation-fetching purposes.
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 alternatives. With sibling tools like 'call_foreman_api', 'get_foreman_api_resource_docs', and 'Get Foreman DSL Documentation', there's no indication of context, prerequisites, or distinctions, leaving the agent without usage direction.
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 full burden for behavioral disclosure. It states the tool 'fetches' documentation, implying a read-only operation, but doesn't specify whether it requires authentication, has rate limits, returns structured data, or handles errors. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple single-parameter fetch), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't address behavioral aspects like authentication needs, return format, or error handling, which are critical for an agent to use it correctly without structured data to rely on.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the parameter 'resource' is undocumented in the schema. The description adds minimal semantics by indicating it's for 'given Foreman API resource', but doesn't explain what constitutes a valid resource (e.g., format, examples, or constraints), failing to compensate for the low coverage.
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 action ('fetches') and target ('documentation for given Foreman API resource'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'fetch_foreman_dsl_docs' or 'Get Foreman DSL Documentation', which appear to serve similar documentation-fetching purposes but for different resource types.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention sibling tools like 'call_foreman_api' (which likely performs API calls rather than fetching docs) or clarify distinctions between API resource docs and DSL docs, leaving the agent without context for tool selection.
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 of behavioral disclosure. It mentions 'Reads from cache,' which hints at performance or data source behavior, but doesn't cover critical aspects like whether this is a read-only operation, potential errors, rate limits, or authentication needs. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 appropriately sized and front-loaded, with two sentences that efficiently convey the core functionality and a reference for further details. Every sentence adds value without redundancy, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a documentation retrieval tool with one parameter), no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on return values, error handling, and behavioral traits, which are essential for effective tool invocation. The reference to external sections helps but doesn't suffice for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does 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 for the undocumented parameter 'section.' It adds some meaning by linking to 'foreman://documentation/dsl/sections for available sections,' which provides context for valid values. However, this is minimal and doesn't fully explain the parameter's purpose or usage, leaving it inadequately documented.
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's purpose: 'Reads from cache and returns the documentation of available macros for template writing in Markdown format based on provided section.' It specifies the verb ('reads', 'returns'), resource ('documentation of available macros'), and format ('Markdown format'). However, it doesn't explicitly differentiate from sibling tools like 'fetch_foreman_dsl_docs' or 'get_foreman_api_resource_docs', which appear related to documentation retrieval.
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 provides some usage context by mentioning 'Refer to foreman://documentation/dsl/sections for available sections,' which implies where to find valid inputs. However, it doesn't explicitly state when to use this tool versus alternatives like 'fetch_foreman_dsl_docs' or provide exclusions. The guidance is implied but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ofedoren/foreman-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server