Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-wiki-server search the docs for how to set up authentication"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A minimal, agent-agnostic MCP server that exposes search over local markdown documentation. Any MCP-compatible client (Claude Code, Claude Desktop, or a custom agent built on any model) can connect to it — the server has no knowledge of which agent or LLM is calling it.
search_docs(query, limit?) — keyword search across docs/*.md, returns matching files with a snippet.
read_doc(path) — read the full contents of a doc by its relative path.
Related MCP server: mdbook-mcp-server
npm install
npm run buildnpm run inspectThis launches the MCP Inspector, a web UI for calling your tools directly without needing a full LLM client.
claude mcp add wiki-search -- node /absolute/path/to/mcp-wiki-server/dist/index.jsAdd to claude_desktop_config.json:
{
"mcpServers": {
"wiki-search": {
"command": "node",
"args": ["/absolute/path/to/mcp-wiki-server/dist/index.js"]
}
}
}Any other MCP client follows the same pattern: point it at node dist/index.js as a subprocess command.
Drop .md files into docs/ (subfolders supported). No re-deploy needed — search_docs reads from disk on every call.
Swap StdioServerTransport for the Streamable HTTP transport in src/index.ts.
Point docs/ at a real source (Confluence/Notion/Drive) instead of local files.
Containerize and deploy to Cloud Run.
Add auth (OAuth2/OIDC or Cloud Run IAM) in front of the HTTP endpoint.
Read the full contents of a specific markdown doc by its relative path (as returned by search_docs).
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Relative path to the doc, e.g. 'guides/onboarding.md' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Read the full contents' communicates that this is a read-only retrieval operation and that the entire document is returned, but it does not disclose behavior for invalid paths, permissions, or potential truncation. It adds some specificity needed beyond the name but leaves behavioral edge cases unaddressed.
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?
A single sentence that is front-loaded with the key verb and resource, then scopes the input. No redundant words, no restating the tool name. Every element earns its place.
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 single- parameter tool with no output schema, the description sufficiently conveys what the agent gets back ('full contents') and indicates the input format comes from search_docs. It does not cover error cases or whether the doc must exist, but those are mitigagated by the implied search-first flow and the tool's simplicity.
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 schema already fully describes the 'path' parameter (relative path, example), and the description reiterates it. Schema description coverage is 100%, so no additional param insight is required; the description adds marginal contextual value by tying the path to search_docs results.
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 uses a specific verb ('Read') and resource ('specific markdown doc') and specifies the input ('relative path'). It clearly distinguishes itself from the sibling 'search_docs' by stating the path comes from that tool, so the agent knows this is the retrieval step, not the search step.
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 phrase 'as returned by search_docs' implies the workflow: search first, then read by path. This gives context for when to use this tool, though it does not explicitly state 'use this instead of search_docs when you already have a path' or provide exclusion conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search internal markdown documentation for a keyword or phrase. Returns matching files with a short snippet of surrounding context.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default 5) | |
| query | Yes | Keyword or phrase to search for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the disclosure burden. It reasonably communicates that this is a read-only search returning snippets, but it does not mention search matching behavior such as case sensitivity, relevance ordering, or what happens when no matches are found.
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?
Two compact sentences with no filler. The primary action and return format are front-loaded, and every clause adds useful 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?
For a simple search tool with two parameters and full schema documentation, the description is nearly sufficient. It explains the purpose and result format, and the sibling read_doc provides enough context to orient the agent. Minor gaps like result ordering and edge-case behavior are not critical for correct invocation.
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?
Schema coverage is 100%: both query and limit are described in the schema. The description adds little beyond that, though 'keyword or phrase' reinforces the query semantics. This matches the baseline of 3.
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?
Description states a specific verb ('Search'), a clear resource ('internal markdown documentation'), and the output shape ('matching files with a short snippet'). The action of searching is clearly distinguished from the sibling tool read_doc, so an agent can tell them apart.
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 implies when to use the tool: when you need to locate documents by keyword, not when you already know the document and want to read it. However, it never explicitly names the sibling alternative or states when not to use this tool, leaving the decision partly to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools serve clearly distinct purposes: search_docs finds documents by keyword, while read_doc retrieves the full content of a known document by path. There is no overlap or ambiguity between them.
Both tools use a verb_noun pattern (search_docs, read_doc), making the pattern predictable. There is a minor inconsistency in pluralization—search_docs is plural while read_doc is singular—but it does not cause confusion.
With only two tools, the server feels minimally scoped. This is appropriate for a simple read-only wiki, but it is on the thin side and offers no additional utility beyond search and read.
For a read-only documentation server, the search-and-read lifecycle is complete. The only notable gap is the lack of a way to list or browse all documents without a search query, which could be a minor workaround in some cases.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Read-only MCP server for the OrchestKit docs: full-text search + Markdown fetch. No auth.
Political Comms documentation MCP server: search docs, query the docs filesystem. No auth.
Team docs served to AI agents over MCP - search, Markdown reads, version pinning, read audit.
Agent-native MCP server over the public saagarpatel.dev corpus. Read-only, stateless.
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