A
licenseNot graded
qualityA
maintenanceProvides AI coding agents with accurate knowledge of the Siemens NXOpen .NET API by performing hybrid retrieval over local documentation, eliminating hallucinated API calls.
3
MIT
docs
by com.ntop
Server Details
Search and query nTop's knowledge base and engineering guides from AI applications.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolsquery_docs_filesystem_n_topARead-onlyIdempotentInspect
Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at / that contains ONLY the nTop documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks.
This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its .mdx path (e.g. /quickstart.mdx, /api-reference/create-customer.mdx) to head or cat. To search the docs with exact keyword or regex matches, use rg. To understand the docs structure, use tree or ls.
Workflow: Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path.
Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run --help on any command for usage.
Each call is STATELESS: the working directory always resets to / and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with && or pass absolute paths (e.g., cd /api-reference && ls or ls /api-reference). Do NOT assume that cd in one call affects the next call.
Examples:
tree / -L 2— see the top-level directory layoutrg -il "rate limit" /— find all files mentioning "rate limit"rg -C 3 "apiKey" /api-reference/— show matches with 3 lines of context around each hithead -80 /quickstart.mdx— read the top 80 lines of a specific pagehead -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx— read multiple pages in one callcat /api-reference/create-customer.mdx— read a full page when you need everythingcat /openapi/spec.json | jq '.paths | keys'— list OpenAPI endpoints
Output is truncated to 30KB per call. Prefer targeted rg -C or head -N over broad cat on large files. To read only the relevant sections of a large file, use rg -C 3 "pattern" /path/file.mdx. Batch multiple file reads into a single head or cat call whenever possible.
When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the .mdx extension. For example, /quickstart.mdx becomes /quickstart and /api-reference/overview.mdx becomes /api-reference/overview.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| command | Yes | A shell command to run against the virtualized documentation filesystem (e.g., `rg -il "keyword" /`, `tree / -L 2`, `head -80 /path/file.mdx`). |
TDQS
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses statelessness, working directory reset, output truncation to 30KB, no writes/network/process control, and the sandbox nature of the filesystem. It also explains path-to-URL conversion. No contradiction with 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 long but well-structured with clear sections: sandbox warning, workflow, supported commands, statelessness, examples, output limits, and URL conversion. Each section adds operational value, and the most critical caveat (not a real shell) is front-loaded.
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 one-parameter shell-query tool with no output schema, the description covers command semantics, usage patterns, limitations, output handling, and page reference conventions. It is sufficiently complete for an agent to invoke the tool correctly without additional context.
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 schema only defines `command` as a string, but the description enriches it with supported commands, chaining via `&&`, absolute paths, multiple file reads, and concrete examples. This goes far beyond the schema's minimal parameter description.
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 opens with a specific verb and resource: 'Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/`.' It clearly states this is the way to read documentation pages and distinguishes itself from the sibling search tool by emphasizing exact keyword/regex matching, structural exploration, and full-page reads.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit workflow guidance: 'Start with the search tool for broad or conceptual queries... Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path.' This names the alternative and defines when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_n_topSearch documentationARead-onlyIdempotentInspect
Search across the nTop knowledge base to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about nTop, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages. If you need the full content of a specific page, use the query_docs_filesystem tool to head or cat the page path (append .mdx to the path returned from search — e.g. head -200 /api-reference/create-customer.mdx).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
TDQS
A4.7/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint false, so safety is clear. The description adds meaningful behavioral context: it explains the search returns 'contextual content with titles and direct links' and explains how to retrieve full pages via append `.mdx`. No contradictions.
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 three well-organized sentences: purpose, when to use, and alternative for deeper access. Each sentence earns its place and includes a concrete example, with no fluff or repetition.
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 a single parameter, no output schema, and rich annotations, the description is complete enough. It explains what the tool returns (titles and direct links), when to use it, and how to access full content through a sibling tool. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the only parameter `query` already described as 'Search query'. The description does not add additional parameter semantics beyond implying the query is used to search the knowledge base, so baseline 3 is appropriate.
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 a specific action: 'Search across the nTop knowledge base' to find relevant information, code examples, API references, and guides. It distinguishes itself from sibling tools like query_docs_filesystem_n_top by explicitly pointing to that tool for full page content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit when-to-use guidance: 'Use this tool when you need to answer questions about nTop, find specific documentation, understand how features work, or locate implementation details.' It also gives an alternative for full content: 'use the query_docs_filesystem tool to head or cat the page path'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_feedbackSubmit documentation feedbackAInspect
Report a problem with this documentation site so the docs team can fix it. Use when a documentation page is incorrect, outdated, confusing, incomplete, or has a broken example. This is for feedback about the documentation content itself — not for product support requests or feedback about this tool or assistant.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | The documentation page path the feedback is about (e.g., the page you were reading, such as `/quickstart`). | |
| feedback | Yes | A clear description of the documentation issue or suggestion — what is incorrect, outdated, missing, or confusing. |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose non-read-only, open-world behavior. The description aligns and adds context that feedback is sent to the 'docs team' and is scoped to documentation content. It could go deeper on what happens after submission (e.g., tracking, response), but the basic behavioral expectation is well-defined.
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, no redundant phrases, and each clause contributes. It front-loads the action along with the purpose and then adds necessary exclusions. This is an well-structured description that wastes no tokens.
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 two-parameter feedback tool with no output schema, the description tells the agent when to use it, what it is not for, and what kind of feedback is relevant. It is a self-contained definition that gives sufficient context to invoke the tool correctly by making the boundaries crystal clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of the parameters and already explain 'path' and 'feedback' with examples and examples. The description adds no additional parameter context beyond the schema. The baseline of 3 is appropriate because the schema carries the full burden.
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 gives a specific verb ('Report a problem') and identifies the exact resource ('documentation site') with its intended scope. It clearly enumerates the types of issues accepted (incorrect, outdated, confusing, incomplete, broken example) and differentiates itself from sibling search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
This scores 5 because it explicitly states when to use it ('Use when a documentation page is...') and when not to use it ('not for product support requests or feedback about this tool or assistant'). This avoids the common confusion between the document-search siblings and this feedback tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- Changed
query_docs_filesystem_n_top1 field changed- removed
Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"
- Changed
search_n_top1 field changed- removed
Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"
- Changed
submit_feedback1 field changed- removed
Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"
3 tool updates
- First observed
query_docs_filesystem_n_top - First observed
search_n_top - First observed
submit_feedback
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TDQS
A4.4/5.0
Disambiguation5/5
Each tool serves a clearly distinct purpose: query_docs_filesystem_n_top is for exact filesystem-style searching and reading specific pages, search_n_top is for semantic/knowledge-base search across all docs, and submit_feedback is for reporting doc issues. There is no overlap or ambiguity between them.
Naming Consistency2/5
Two tools share the 'n_top' suffix but the prefixes are inconsistent ('query_docs_filesystem' vs 'search'), and the third tool 'submit_feedback' breaks the pattern entirely. The naming feels ad-hoc rather than following a predictable convention.
Tool Count4/5
With only 3 tools, the surface is minimal but each tool is substantive: the filesystem tool covers reading, listing, and exact searching; the semantic search covers broad discovery; and feedback closes the loop. It's on the lower end but still a reasonable scope for a documentation server.
Completeness5/5
The tools cover all core doc access needs: semantic search, exact filesystem queries for reading and exploring pages, and a feedback mechanism. There are no obvious gaps for a read-only documentation service.