Ignition Doc MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: two search tools target different documentation sets (user manual vs SDK guide), and get_page fetches content by URL. No overlap or ambiguity exists.
Naming Consistency5/5The search tools follow a consistent 'search_ignition_' prefix pattern, and get_page is a simple, clear verb_noun pair. All names are snake_case and readable.
Tool Count4/5With 3 tools, the set is minimal but well-scoped for a documentation-focused server. It covers the primary needs without unnecessary bloat, though it could benefit from one or two more tools.
Completeness4/5The tool surface covers core documentation workflows: searching two main documentation sources and fetching specific pages. Minor gaps like version selection or browsing are absent, but for typical usage it is sufficient.
Average 3.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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.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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It adds the useful behavioral detail that it always searches the latest version, but it does not mention any limitations, side effects, or safety profile (e.g., read-only nature). For a search tool, this is a moderate disclosure.
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 sentences with the key action and resource front-loaded. Every sentence contributes: the first states the core function, the second provides use-case guidance. No wasted 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?
The description is reasonably complete for a simple search tool: it covers purpose, topics, and version. However, it lacks parameter explanations and does not reference sibling tools for alternative use cases. The presence of an output schema mitigates the need for return value details, but the missing parameter semantics is a clear gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only implies that 'query' is a natural language question (via 'questions about...'), but 'n_results' is entirely unexplained. Neither parameter gets meaningful semantic elaboration.
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 action ('Search') and the resource ('the Ignition User Manual'), and specifies that it always uses the latest version. It also enumerates covered topics (configuration, tags, scripting, modules, gateways, designers), which distinguishes it from sibling tools like search_ignition_sdk.
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 description explicitly says 'Use this for questions about...' and lists relevant topics, providing clear usage context. However, it does not explicitly mention when not to use it or point to alternatives (e.g., search_ignition_sdk), so it falls short of a 5.
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 bears responsibility for disclosing behavior. The description indicates the search source (SDK Programmer's Guide) and the intended query context, but does not explicitly state that this is a read-only operation, what it returns, or any pagination/format details. Since 'search' implies a non-destructive operation, the risk is low, but the description lacks richer 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and every word adds value. It avoids redundancy and clearly communicates purpose and usage in minimal text.
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 provides sufficient context for a simple search tool: it names the search source and the type of queries. The presence of an output schema reduces the need to explain return values. However, the unclarified 'n_results' parameter leaves a small gap in completeness, preventing a perfect score.
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 has 0% description coverage; the schema only defines 'query' and 'n_results' with no per-parameter descriptions. The description partially compensates by suggesting 'query' should be about building Ignition modules, but it does not explain 'n_results' at all. Since the description adds some semantic value for 'query' but leaves 'n_results' undefined, a 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's purpose: 'Search the Ignition SDK Programmer's Guide.' It specifies the resource (SDK Programmer's Guide) and the intended use case (questions about building Ignition modules), listing concrete classes like GatewayModuleHook and DesignerModuleHook. This distinguishes it from sibling tools like search_ignition_docs, which likely covers broader documentation.
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 description explicitly says 'Use this for questions about building Ignition modules,' providing clear context for when to invoke this tool. While it does not name alternatives or exclusion criteria, the specific use case and the mention of the SDK guide imply a narrower scope than general docs search. This is clear context without explicit alternatives, matching a score of 4.
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 full burden. It discloses the constraint that URLs should come from search tools (a behavioral trait), but it does not mention potential side effects, authentication needs, or behavior around the max_chars parameter. It is safe to assume a fetch is read-only, but the description does not explicitly confirm it.
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 sentences long, with the core action upfront and the usage context in the second sentence. Every word earns its place, and it is highly efficient without any 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?
This is a simple fetch tool with an output schema, and the description gives enough context for the main use case (feed it a URL from search). The missing information about 'max_chars' and error behavior is a minor gap, but the tool's simplicity keeps the description largely adequate.
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 description must compensate. It adds meaning to the 'url' parameter by specifying it comes from search tools, but it completely omits any explanation of 'max_chars', leaving that parameter undocumented. The description only partially compensates for the low coverage.
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 action ('Fetch the text content') and the resource ('an Ignition documentation page'). It also distinguishes itself from sibling search tools by specifying that it retrieves content for a given URL, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs to use URLs returned by the sibling search tools, providing clear workflow guidance on when to use this tool relative to alternatives. This is a direct 'when to use' pointer that prevents misuse.
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/nurnaufal321/inductiveautomation-doc-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server