developer-toolkit-mcp
The documentation, as a tool your agent can call: 950+ AI-dev guides. Search + fetch tools.
Should I use this
Quality & Safety
Findings (1)
- LOWTool 'fetch' suggests web access but openWorldHint=falsein fetch
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"developer-toolkit-mcp": {
"url": "https://developertoolkit.ai/mcp"
}
}
}Remote endpoints
https://developertoolkit.ai/mcpstreamable-httpWhat it can do
Tool inventory
Tools (2)
🟢search(query, llm_model)
Search the AI Developer Toolkit documentation: 950+ guides on Cursor, Claude Code and OpenAI Codex, covering setup, agent workflows, hooks, MCP, testing, CI and deployment, in English and Polish. Returns at most 10 ranked results, each with a short snippet rather than the article text; an empty list means the corpus has nothing on the topic. Pass a result id to `fetch` for the full text.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"minLength": 1,
"maxLength": 1024,
"description": "Natural-language query or keywords. Polish queries return Polish articles."
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
}
},
"required": [
"query",
"llm_model"
],
"$schema": "https://json-schema.org/draft/2020-12/schema"
}Output Schema
{
"type": "object",
"properties": {
"results": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"title": {
"type": "string"
},
"url": {
"type": "string"
},
"snippet": {
"type": "string"
},
"lang": {
"type": "string"
}
},
"required": [
"id",
"title",
"url",
"snippet",
"lang"
],
"additionalProperties": false
}
}
},
"required": [
"results"
],
"$schema": "https://json-schema.org/draft/2020-12/schema",
"additionalProperties": false
}🟢fetch(id, llm_model)
Retrieve the complete markdown of one documentation article by the id returned from `search` (for example `en/claude-code/advanced-techniques/hooks-automation`). The text is returned in full; `metadata.gated` only reports whether the article sits behind the paywall on the web. An unknown id is an error — call `search` first.
Input Schema
{
"type": "object",
"properties": {
"id": {
"type": "string",
"minLength": 1,
"description": "Article id from a `search` result."
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
}
},
"required": [
"id",
"llm_model"
],
"$schema": "https://json-schema.org/draft/2020-12/schema"
}Output Schema
{
"type": "object",
"properties": {
"id": {
"type": "string"
},
"title": {
"type": "string"
},
"text": {
"type": "string"
},
"url": {
"type": "string"
},
"metadata": {
"type": "object",
"properties": {
"lang": {
"type": "string"
},
"section": {
"type": "string"
},
"lastUpdated": {
"type": "string"
},
"gated": {
"type": "boolean"
}
},
"required": [
"lang",
"section",
"lastUpdated",
"gated"
],
"additionalProperties": false
}
},
"required": [
"id",
"title",
"text",
"url",
"metadata"
],
"$schema": "https://json-schema.org/draft/2020-12/schema",
"additionalProperties": false
}Community
Evidence