Honeydew AI Documentation MCP — semantic search and ripgrep-grade filesystem queries over Honeydew AI docs and OpenAPI specs, for AI coding agents.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.5/5 across 3 of 3 tools scored.
The two main tools are clearly distinct: one performs filesystem-style queries on a virtual documentation tree, and the other is a semantic search over the knowledge base. The third tool, feedback submission, is entirely different in purpose. The only minor ambiguity is that the search tool and filesystem tool both retrieve documentation, but their descriptions clarify different use cases.
Tool names are descriptive but not uniformly patterned. 'query_docs_filesystem_honeydew_documentation' uses verb-object with a suffix, 'search_honeydew_documentation' uses verb-noun, and 'submit_feedback' is a simple verb-noun. The inconsistency lies in the inclusion of the server name in some but not all, and the length of the first name is cumbersome, but the verbs are clear.
Three tools is on the lean side but appropriate for a documentation assistant: one for structured file-system access, one for semantic search, and one for feedback. It avoids unnecessary redundancy and covers the core need. Slightly more could be added (e.g., a dedicated get_page tool), but the count is reasonable.
The tool set covers the essential workflows: searching for information, reading specific pages, and reporting issues. The filesystem tool can simulate file browsing and reading, which compensates for the lack of a dedicated 'get page' tool. A minor gap is the absence of a tool to list all pages or navigate with more advanced filters, but agents can work around this with tree/ls and rg.
query_docs_filesystem_honeydew_documentationARun 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 layout
rg -il "rate limit" / — find all files mentioning "rate limit"
rg -C 3 "apiKey" /api-reference/ — show matches with 3 lines of context around each hit
head -80 /quickstart.mdx — read the top 80 lines of a specific page
head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx — read multiple pages in one call
cat /api-reference/create-customer.mdx — read a full page when you need everything
cat /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`). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and no destruction, but the description goes further by explaining the sandbox nature, lack of network/process control, and statelessness. It clearly states this is not a real machine shell, fully aligning 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with paragraphs and bullet examples, but it is somewhat repetitive—several sentences restate the same sandbox and read-only guarantees. It could be tightened without losing key information.
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 the presence of sibling tools, the description fully explains when to use this one, covers output truncation and path-to-URL conversion, and provides enough detail for effective usage. No output schema exists, but the description addresses expected behavior sufficiently.
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 single parameter 'command' is fully described in the schema with examples, and the tool description adds comprehensive usage examples (e.g., 'rg -il', 'tree', 'head') that illustrate valid commands without ambiguity.
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 runs read-only shell-like queries against a virtualized documentation filesystem. It distinguishes itself from sibling tools by focusing on shell command execution for structural exploration and exact content reading, not semantic search.
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 says when to use this tool versus the search tool, providing a workflow: '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.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_honeydew_documentationSearch documentationASearch 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds no additional behavioral traits beyond confirming it returns search results. Since annotations already provide transparency, the bar is lower, and the description is consistent but adds no new context.
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 concise and well-structured with three sentences. It covers purpose, usage, and an alternative tool without unnecessary fluff. It is slightly longer than the minimal two sentences but remains efficient and clear.
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?
The description sufficiently covers the tool's purpose, usage scenarios, output (contextual content with titles and links), and points to a complementary tool for full content. Given no output schema, it adequately describes what the user can expect. The context is complete for a read-only search 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?
The only parameter 'query' has a schema description 'Search query' which is straightforward. The tool description does not elaborate further on the parameter, but schema coverage is 100%, so the baseline is acceptable. No additional meaning is provided beyond the schema.
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: searching across Honeydew Documentation to find relevant information, code examples, API references, and guides. It also specifies the output (contextual content with titles and links), 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.
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 ('when you need to answer questions about Honeydew Documentation, find specific documentation, understand how features work, or locate implementation details') and provides an alternative tool ('use the query_docs_filesystem tool') for full page content, giving clear guidance on usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_feedbackSubmit documentation feedbackAInspectReport 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.
| 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is a non-read-only, non-destructive action. The description adds useful scope by saying feedback goes to the docs team and is about documentation content itself. It does not mention response behavior or confirmation, but for a simple submit tool this is reasonably transparent.
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 only two sentences. The first sentence explains the purpose; the second provides when-to-use and when-not-to-use guidance. No redundant or misleading content is present.
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?
For a simple two-parameter feedback tool with complete schema coverage and no output schema, the description is sufficient. It explains the purpose, target audience, appropriate use cases, and exclusions, so the agent can confidently invoke the tool correctly.
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 input schema already provides full 100% coverage with meaningful descriptions for both `path` and `feedback`, including an example path. The description adds context about what kind of feedback is appropriate, but it does not need to add parameter-level details because the schema covers them.
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 opens with a specific action and object: 'Report a problem with this documentation site so the docs team can fix it.' It clearly distinguishes the tool from the sibling query/search tools by emphasizing this is for docs-content feedback, not searching or retrieving documentation.
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 lists trigger conditions: a page is 'incorrect, outdated, confusing, incomplete, or has a broken example.' It also states exclusions: 'not for product support requests or feedback about this tool or assistant,' which helps the agent decide 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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
