You said "just start," so I did. I assumed "multi-tenant" meant splitting the database by header — you meant routing by subdomain. Eight files later, you noticed I'd misread you.
That's the opening of a real entry. The whole book reads in that voice: not a third-person tutorial on how to use AI, but the AI telling you, in the first person, where you most often go wrong working with it.
📖 Fully bilingual. Every entry exists in both English and 中文 — Chinese is the writing source, English is a 1:1 mirror. Switch languages from the top-right of the site, or read the 中文 README.
Plenty of "best-practice lists" and "config dumps" for AI coding tools already exist. AiWiki fills a gap none of them do:
First-person AI narration — "here's what I, the model, see you doing," not a detached how-to.
Root cause from mechanism — every pitfall is traced back to how the model actually works, so the fix becomes something you can re-derive yourself.
Verifiable, not vibes — each entry tags its evidence (official docs / arXiv / CVE & security advisories) and is version-stamped; the case library cites real incidents, three of them with CVE numbers.
Cross-tool, honestly scoped — the same pitfall compared across five coding tools, with each tool's coverage depth labeled rather than padded out.
Related MCP server: doc-hub-mcp
76 pitfall entries across the 8 phases of the software lifecycle
5 real-world case studies (3 CVE-backed)
13 toolkit pieces — checklists, prompt templates, and ready-to-copy workflows
5 coding tools covered, indexed in one tool matrix
Phase | Directory |
Setup & collaboration |
|
Ideation & feasibility |
|
Requirements |
|
Architecture |
|
Detailed design |
|
Implementation |
|
Testing |
|
Acceptance & release |
|
Every entry follows one structure: what I see you doing → why it happens → consequences → best practice → example → version notes → sources.
New here? A few ways in:
🌐 Read the site (English) · 中文站点
🧭 Tool matrix — what differs between Claude Code, Cursor, Copilot, Codex, and Gemini
👤 Browse by role — PM / architect / engineer / QA / ops
A few representative pitfalls: Skipping plan mode and just letting me change things · Context-file overload · You ask me to "fix the test," I make it green instead of correct
Claude Code has the deepest coverage and is the book's default reference. Cursor, GitHub Copilot, Codex CLI, and Gemini CLI each get their own "tool differences" sections, plus a standalone entry wherever a pitfall comes from a mechanism unique to one tool. Coverage depth is labeled honestly — proprietary, fast-changing tools lean on security disclosures and official docs, and are thinner. See the tool matrix.
The whole encyclopedia is available as an MCP server — ask your AI assistant "search AiWiki for why long sessions get dumber" and it pulls the entry, mechanism, and sources:
claude mcp add aiwiki -- npx -y aiwiki-mcpWorks with any MCP client (Cursor, Windsurf, Claude Desktop…). Details in mcp/README.md.
Built on Docusaurus (native versioning, bilingual i18n, full-text search).
npm install
npm run start # Chinese site
npm run start -- --locale en # English site
npm run build # full build (both locales)An open, long-running project. New pitfall entries, corrections, and translation fixes are all welcome.
📖 CONTRIBUTING.md — entry template & submission flow
✍️ STYLE-GUIDE.md — how to write human, reliable, well-sourced prose
Content is shared under CC BY-SA 4.0; sources are cited at the foot of each entry. Diagrams are self-drawn (Mermaid) where possible; external material is used only when clearly licensed and attributed. Spot a problem? Open an issue.
Fetch the full markdown of one AiWiki entry by id (as returned by aiwiki_search). Entries include mechanism analysis, consequences, best practices, and verifiable sources.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Entry id, e.g. "context-rot" | |
| lang | No | Content language (default zh; falls back to zh if en missing) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the output format ('full markdown') and content structure ('mechanism analysis, consequences, best practices, verifiable sources'), but does not mention error handling or language fallback (although the fallback is in the schema). This adds some transparency but not exhaustive detail.
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 two sentences with no filler. The key action and resource are front-loaded, and the second sentence explains entry content, efficiently using the space.
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 tool's simplicity (id + optional lang, no output schema), the description is complete enough. It specifies what the entry contains and that the output is markdown. It does not discuss edge cases, but for a basic retrieval tool this is acceptable.
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% for both parameters, so the baseline is 3. The description adds semantic value by clarifying the id is 'as returned by aiwiki_search', linking it to the search tool's output. This enriches the parameter meaning beyond the schema description.
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 specifies the action ('Fetch the full markdown') and the resource ('one AiWiki entry by id'). It also differentiates from the sibling aiwiki_search by indicating the id comes from that search tool.
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 aiwiki_search' provides clear context that this tool is used after searching. It does not explicitly state when not to use it, but the context is sufficient for a simple fetch tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search AiWiki — an encyclopedia of AI-coding pitfalls and LLM privacy protection, written from the AI's first-person perspective. Use when the user hits a recurring AI-coding failure (context issues, hallucinated APIs, tests being gamed, destructive commands, prompt injection…) or an LLM privacy question (training data extraction, membership inference, RAG leakage…). Queries can be Chinese or English.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language of returned titles/summaries/urls (default zh) | |
| limit | No | Max results (default 5) | |
| query | Yes | Keywords or a short description of the problem (中文或英文) | |
| subject | No | Restrict to one subject; omit to search both |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the content perspective (first-person) and query language flexibility (Chinese/English), but it omits details such as how results are ordered, whether search requires exact matches, or what variations in output might occur. The description is adequate but not deeply transparent about search behavior beyond the basic action.
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 compact and front-loaded: it states the tool's purpose, domain, concrete use cases, and language flexibility in just two sentences. The parenthetical examples are efficient and informative without padding. Every element earns its place, making this an exemplary model of concise yet informative writing.
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 tool's moderate complexity (4 parameters, no annotations, no output schema), the description provides strong domain context, examples of query topics, and language support. The schema already hints at return structure via the 'returned titles/summaries/urls' phrase in the lang parameter. However, the description could have explicitly tied to the sibling 'aiwiki_get_entry' for next steps, which would have made it fully complete. It covers most contextual needs without being exhaustive.
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 provides 100% coverage for all four parameters (query, lang, limit, subject), so the baseline is 3. The description adds minimal parameter-specific meaning beyond the schema—it reiterates that queries can be Chinese or English, which duplicates the schema's query description. It does not introduce new parameter semantics or clarify parameter interactions, so the score stays at baseline.
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 verb and resource ('Search AiWiki') and immediately defines the resource as 'an encyclopedia of AI-coding pitfalls and LLM privacy protection'. This clearly distinguishes the search tool from its sibling 'aiwiki_get_entry' by scope (searching vs. retrieving) and content domain, making the tool's purpose unmistakable.
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 when the user hits a recurring AI-coding failure... or an LLM privacy question...', with concrete examples. It does not explicitly list when-not-to-use or name the alternative 'aiwiki_get_entry', but the usage context is rich enough that an agent can infer the appropriate scenario. This is a clear context, though not a full when/when-not set.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0aiwiki_get_entryaiwiki_searchThe two tools have completely distinct purposes: one searches for entries, the other fetches a specific entry by ID. There is no overlap or confusion between them.
Both tools share the 'aiwiki_' prefix, which is good, but the second word differs in style: 'search' is a verb, while 'get_entry' is verb_noun. This is a minor deviation from a fully consistent verb_noun pattern.
With only 2 tools, the server is at the low end of the scale. However, for a knowledge base encyclopedia, a search and a fetch tool can be a reasonable minimal set, though it feels slightly thin.
The server covers the core workflow of discovering and retrieving entries. A notable gap is the lack of a list/browse function to see all entries without a query, but search and retrieval are sufficient for basic use.
Read, search and edit Smartipedia, an open AI-native encyclopedia. No API key, no signup.
Search and read the aicoolies developer-tools knowledge graph.
Search and read public Wikivibe articles about AI coding, agents, MCP, GEO, bots and deployment.
Search indexed websites, read raw page markdown, and score AI visibility for any site.
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