Honeydew AI Documentation
Server Details
Honeydew AI Documentation MCP — semantic search and ripgrep-grade filesystem queries over Honeydew AI docs and OpenAPI specs, for AI coding agents.
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- Healthy
- Last Tested
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- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 2 of 2 tools scored.
Both tools retrieve documentation, but they are clearly differentiated: one provides filesystem-level access for reading and exact search, the other performs semantic search across the knowledge base. The descriptions explicitly recommend when to use each.
Both tool names start with a verb and include the server name. The first is long but follows the pattern 'query_docs_filesystem_...' while the second is simpler 'search_...'. The pattern is consistent enough to be predictable.
With only 2 tools, the count is slightly low but reasonable for a documentation server. The filesystem tool is versatile and combines multiple commands, reducing the need for more granular tools.
The tool set covers all necessary operations for a read-only documentation resource: searching (semantic and exact) and reading full pages. No obvious gaps exist for the stated purpose.
Available Tools
2 toolsquery_docs_filesystem_honeydew_documentationAInspect
Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at / that contains ONLY the Honeydew Documentation 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.
| 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`). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It thoroughly discloses that the tool is read-only, stateless, has no network access, no writes, and output is truncated to 30KB. It also lists allowed commands and notes that the working directory resets each call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections and examples, but it is somewhat lengthy. Each paragraph serves a distinct purpose, and key information is front-loaded. Minor redundancy could be trimmed, making it a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description covers all necessary context: how to use the tool, limitations (stateless, truncated output), URL conversion, and examples. It is fully complete for a single-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only one parameter (command) and 100% schema description coverage, the baseline is 3. The description adds significant context beyond the schema, including examples and constraints on the command (e.g., supported commands, no writes), earning a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs read-only shell-like queries against a virtualized filesystem of documentation pages and OpenAPI specs. It explicitly distinguishes from the sibling search tool by specifying it uses exact keyword/regex matching and structural exploration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 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.' It also explains statelessness, truncated output, and preferred commands.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_honeydew_documentationAInspect
Search across the Honeydew Documentation knowledge base to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about Honeydew Documentation, 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).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description takes responsibility for behavioral disclosure. It explains the return format ('contextual content with titles and direct links') and provides a usage pattern for getting full content. No destructive or sensitive actions are implied, which is appropriate for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately long but well-structured, with each sentence adding value. It starts with the action, then usage guidance, then output details, and finally a link to the sibling tool. It could be slightly more concise, but it is effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description clearly explains what the search returns (contextual content, titles, links) and how to proceed for full content using a sibling tool. For a single-parameter tool, this is thorough and complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter is 'query' with description 'Search query'. Schema coverage is 100%, so baseline is 3. The description does not add extra semantics beyond the schema, but it is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Search across the Honeydew Documentation knowledge base to find relevant information, code examples, API references, and guides.' It provides specific use cases and distinguishes from the sibling tool by guiding when to use the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool ('Use this tool when you need to answer questions...') and when not to ('If you need the full content of a specific page, use the query_docs_filesystem tool...'), making it easy for the AI to decide.
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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