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mimeCam

DevBrain

by mimeCam
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

DevBrain MCP Server

Chat with your favorite newsletters (coding, tech, founder).

Audit

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About

It is a newsletter-based MCP that searches for relevant code snippets, indie developer articles and blog posts so you don't have to hunt through generic web results again. Just ask LLM: "research on devbrain"

It's kind of like a web search, but specifically tuned for high-quality, developer-curated content. You can easily plug in your favorite newsletter to expand its knowledge base even further.

For example, when you are implementing feature "A", DevBrain can pull related articles that would serve as a solid reference and a foundation for your implementation.

Claude app

Goose app (tap on an image to open utube)

DevBrain returns articles as short description + URL, you can then:

  • instruct LLM agent like Claude or Goose to fetch full contents of the articles using provided URLs

  • instruct LLM to implement a feature based on all or selected articles

Try quickly with remote http-transport MCP

"devbrain": {
    "type": "http",
    "url": "https://devbrain.svenai.com/mcp"
},

Related MCP server: Smart Coding MCP

Local Installation (remote http mcp recommended)

Via uv or uvx. Install uv and uvx (if not installed):

curl -LsSf https://astral.sh/uv/install.sh | sh

Example command to run MCP server in stdio mode:

uvx --python ">=3.10" --from devbrain devbrain-stdio-server

Use in Claude Code (or other coding agents)

https://docs.anthropic.com/en/docs/claude-code/mcp#installing-mcp-servers You can either add MCP to cc manually or reference tthe same .json file that Claude app uses.

Use in Claude

To add devbrain to Claude's config, edit the file: ~/Library/Application Support/Claude/claude_desktop_config.json and insert devbrain to existing mcpServers block like so:

{
  "mcpServers": {
    "devbrain": {
      "command": "uvx",
      "args": [
        "--python", ">=3.10",
        "--force-reinstall",
        "--from",
        "devbrain",
        "devbrain-stdio-server"
      ]
    }
  }
}

Claude issues:

sudo mkdir -p /usr/local/bin
sudo ln -s ~/.local/bin/uvx /usr/local/bin/uvx
sudo ln -s ~/.local/bin/uv /usr/local/bin/uv

and restart Claude.

Integration for Cline and other AI agents

Command to start DevBrain MCP in stdio mode:

uvx --python ">=3.10" --force-reinstall --from devbrain devbrain-stdio-server

and add this command to a config file of the AI agent (Cline or other).

Note that DevBrain requires Python 3.10+ support. Most systems have it installed. However VS Code (that Cline depends on) is shipped with Python 3.9. Use correct version of Python when running DevBrain MCP. A corrected version to launch DevBrain MCP looks like this:

uvx --python ">=3.10" --force-reinstall --from devbrain devbrain-stdio-server

Docker integration

You can run this MCP as a Docker container in STDIO mode. First build an image with build.sh. Then add a config to Claude like so:

{
  "mcpServers": {
    "devbrain": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "svenai/mcp-devbrain-stdio:latest"
      ]
    }
  }
}

Test command to verify that docker container works correctly:

docker run -i --rm svenai/mcp-devbrain-stdio:latest

License

This project is released under the MIT License and is developed by mimeCam as an open-source initiative.

Available Tools

2 tools
read_full_articleC

Returns the full content of an article identified by its URL.

Args: url: The URL of the article to read.

Returns: str: The full content of the article or an error message.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns content or an error message, but does not cover critical aspects like rate limits, authentication needs, network behavior, or error handling specifics. This is a significant gap for a tool that likely involves external requests.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by structured sections for args and returns. It is efficient with minimal waste, though the 'Returns' section could be more concise by integrating with the main description, as the output schema exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (external URL fetching), lack of annotations, and an output schema that only specifies a string type, the description is moderately complete. It covers the basic operation but omits important contextual details like error conditions, performance, or dependencies, which are crucial for effective tool use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, but the description adds basic semantics by explaining that 'url' is 'The URL of the article to read.' This clarifies the parameter's purpose beyond the schema's type information. However, it does not provide details on URL format, validation, or examples, leaving room for improvement.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns the full content of an article identified by its URL.' It specifies the verb ('Returns') and resource ('full content of an article'), making it easy to understand. However, it does not explicitly differentiate from the sibling tool 'retrieve_knowledge', which could have similar functions, so it misses the top score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'retrieve_knowledge'. It lacks context on prerequisites, constraints, or scenarios where this tool is preferred, leaving the agent to infer usage based on the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

retrieve_knowledgeA

Queries DevBrain (aka developers brain` system) and returns relevant information.

Args: query: The question or ask to query for knowledge. tags: Optional comma-separated list of tags (keywords) to filter or ground the search. (e.g.: ios, ios,SwiftUI, react-native, web, web,react, fullstack,react-native,flutter). Do not provide more than 3 words.

Returns: str: Helpful knowledge and context information from DevBrain (articles include title, short description and a URL to the full article to read it later).

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
tagsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the system returns 'relevant information' and describes the output format, but doesn't address important behavioral aspects like rate limits, authentication requirements, error conditions, response time expectations, or whether queries are cached. The description adds some value about the return format but leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Args, Returns) and front-loads the core purpose. The parameter explanations are detailed but necessary given the schema coverage gap. Some redundancy exists in explaining DevBrain as both 'developer's brain system' and the source of articles, but overall the description earns its length with valuable information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (which covers return values), the description appropriately focuses on purpose and parameters rather than return format. It provides good parameter semantics despite 0% schema coverage. For a query tool with a sibling, it could benefit from more explicit differentiation and behavioral context, but it's reasonably complete for its complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed semantic information about both parameters. It explains 'query' as 'the question or ask to query for knowledge' and 'tags' as 'optional comma-separated list of tags to filter or ground the search', including specific examples and the constraint 'Do not provide more than 3 words'. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'queries DevBrain and returns relevant information', specifying both the action (queries) and resource (DevBrain system). It distinguishes from the sibling 'read_full_article' by focusing on querying rather than reading full articles. However, it doesn't fully explain what DevBrain is beyond 'developer's brain system', leaving some ambiguity about the knowledge domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through the mention of 'queries DevBrain' and the sibling tool name, suggesting this is for initial knowledge retrieval while 'read_full_article' is for deeper reading. However, it lacks explicit guidance on when to choose this tool versus alternatives, and doesn't mention prerequisites, constraints, or specific scenarios where this tool is most appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedread_full_article
    • First observedretrieve_knowledge
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TDQS

B3.2/5.0
Disambiguation5/5

The two tools have completely distinct purposes: one fetches full article content from a specific URL, while the other queries a knowledge base with a search query and optional tags. There is no functional overlap or ambiguity between these operations.

Naming Consistency5/5

Both tools follow a clear verb_noun pattern (read_full_article, retrieve_knowledge) with consistent snake_case formatting. The naming conventions are predictable and semantically appropriate for their functions.

Tool Count2/5

With only 2 tools, the server feels severely under-scoped for a system described as a 'developer's brain' knowledge base. A comprehensive knowledge system would typically need more operations like searching, filtering, listing, or managing knowledge entries beyond just retrieving content and querying.

Completeness2/5

The tool surface is incomplete for a knowledge base system. While it provides retrieval of specific articles and knowledge queries, it lacks essential operations like listing available articles, searching by metadata, updating knowledge, or managing the knowledge base structure. This creates significant gaps for agent workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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