Rust Docs | AI Builder Hub Review & Alternatives | Flaex AI

VERIFIED

Rust Docs

Safety: 75/100

0.0(0)

This MCP server provides up-to-date Rust crate documentation to LLMs, improving code generation accuracy by querying current API details via semantic search and summarization.

MCP ServersDevelopmentData & DatabasesSearch & WebAI & LLMUtilitiesSocial MediaCloud & InfrastructureFreemium

Boost this tool

Subscribe to listing upgrades or segmented pushes.

Overview

This MCP server provides up-to-date Rust crate documentation to LLMs, improving code generation accuracy by querying current API details via semantic search and summarization.

This server is relatively safe for read-only documentation access. Risks are primarily associated with the OpenAI API key management and potential supply chain vulnerabilities during crate download. Ensure the OpenAI API key is securely stored and consider using a trusted registry to mitigate supply chain risks.

Performance depends on the size of the crate documentation and the speed of the OpenAI API. Caching significantly improves subsequent startup times. Embedding generation can be time-consuming for large crates.

The primary cost is associated with OpenAI API usage for embedding generation and summarization. Caching minimizes repeated costs. Monitor OpenAI API usage to avoid unexpected charges.

HybridRust252 starsMITReleased 5/8/2025CI/CD

Connections & Capabilities

Connects To

GitHubStripe

Capabilities

Read

Client Compatibility

Claude Desktop

full

Cursor

partial

Windsurf

untested

VS Code (Copilot)

partial

Quickstart

Claude Desktop Config

{
  "mcpServers": {
    "rust-docs-reqwest": {
      "command": "/path/to/your/rustdocs_mcp_server",
      "args": [
        "reqwest@0.12"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE"
      },
      "disabled": false,
      "alwaysAllow": []
    },
    "rust-docs-async-stripe": {
      "command": "rustdocs_mcp_server",
      "args": [
        "async-stripe@0.40",
        "-F",
        " runtime-tokio-hyper-rustls"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Environment Variables

OPENAI_API_KEY

Tools (1)

safe

query_rust_docs

Queries the documentation for a specified Rust crate and returns a summarized answer based on semantic search.

Read-only access to documentation; no destructive operations.

Security

Auth Method

Environment Variable

Data Locality

hybrid

Known Risks

!Exposure of OpenAI API key if not properly secured.
!Potential for outdated or incorrect documentation if the crate is rapidly evolving.
!Reliance on OpenAI API introduces a single point of failure and potential cost increases.
!Vulnerability to malicious code in downloaded crate dependencies during documentation generation.

Safety Score

moderate risk

Positive

+Read-only access to crate documentation.
+Sandboxed execution via `cargo doc`.
+No direct filesystem access beyond caching.
+Limited network access only for crate download and OpenAI API.

Risks

-Requires an OpenAI API key, potentially exposing it if misconfigured.
-Relies on external services (OpenAI) for embeddings and summarization.
-Vulnerable to supply chain attacks if `cargo doc` downloads malicious crate dependencies.
-Potential for information leakage if OpenAI logs sensitive data from documentation queries.

Use Cases

Code Generation

-Generating code snippets using up-to-date crate documentation.
-Providing context-aware suggestions for Rust crate usage.
-Automating the creation of examples based on API documentation.

Documentation Assistance

-Answering specific questions about a Rust crate's API.
-Summarizing complex documentation sections for quick understanding.
-Finding relevant documentation based on semantic search.

Learning and Education

-Exploring the functionality of a new Rust crate.
-Understanding the usage of specific functions or methods.
-Comparing different approaches to solving a problem using a crate.

Who Is This For?

Best For

AI-powered coding assistants.
Developers working with rapidly evolving Rust crates.
Teams seeking to improve code generation accuracy.
Educational platforms teaching Rust programming.

Not Ideal For

Environments with limited or no internet access.
Applications requiring real-time documentation updates.
Use cases that demand extremely low latency responses.

Autonomy & Execution

Default ModeRead-only

Sandboxed

The server operates in a read-only mode, accessing documentation and providing summarized answers. No write or execute permissions are granted.

StatelessYes

Retry LogicNot documented

ConcurrencySingle-threaded

Production Tip

Pre-generate documentation and embeddings for commonly used crates to minimize latency during runtime.

FAQ

How do I specify a specific version of a crate?

Use the Cargo package ID specification, e.g., `crate_name@version_req` (e.g., `serde@^1.0`).

How do I enable features for a crate?

Use the `-F` or `--features` flag followed by a comma-separated list of features (e.g., `-F feature1,feature2`).

Where are the documentation and embeddings cached?

In the XDG data directory, typically under `~/.local/share/rustdocs-mcp-server/<crate_name>/<sanitized_version_req>/<features_hash>/`.

What OpenAI model is used for embeddings?

The server uses OpenAI's `text-embedding-3-small` model.

What OpenAI model is used for summarization?

The server uses OpenAI's `gpt-4o-mini-2024-07-18` model.

How do I configure this server with Roo Code?

Add a new entry in Roo Code's `mcp_settings.json` file, specifying the command, arguments (crate name and features), and OpenAI API key.

What happens if the cache is corrupted?

The server will automatically regenerate the documentation and embeddings.

Key Features

Development Data & Databases Search & Web AI & LLM Utilities Social Media Cloud & Infrastructure

MCP Server Integration - Seamlessly connects documentation retrieval to any MCP-compliant AI assistant or IDE.

Real-time Crate Documentation Fetching - Retrieves the most current API details directly from source to ensure accuracy.

Semantic Search Capability - Allows the LLM to perform intelligent lookups rather than relying on exact keyword matches.

Documentation Summarization - Condenses complex crate details into concise context for improved LLM token efficiency.

Automated Context Injection - Automatically feeds relevant documentation into the AI's prompt context during code generation tasks.

Error Reduction Engine - Minimizes compilation errors caused by outdated AI-generated syntax.

Use Cases & Problems Solved

Use Cases

•Use when building complex Rust applications that rely on rapidly evolving third-party crates to ensure your LLM assistant uses the latest API signatures.
•Perfect for debugging unfamiliar Rust libraries by allowing your AI agent to query real-time documentation instead of relying on stale training data.
•Ideal if you need to generate idiomatic Rust code for niche libraries where documentation is sparse or frequently updated.
•Great for integrating into local IDE environments to provide your LLM with context-aware, repository-specific documentation for Rust crates.
•Use when performing refactoring tasks that involve migrating code to newer crate versions to ensure all function calls and type definitions remain compatible.
•Perfect for developers who want to reduce hallucinated function arguments by providing the AI with semantic search results from current crate documentation.

Problems Solved

✓Eliminates the issue of AI assistants suggesting deprecated or non-existent Rust crate functions due to outdated training data.
✓Reduces the time spent manually cross-referencing crate documentation when prompting AI for code generation.
✓Removes ambiguity in code suggestions by providing the LLM with precise, up-to-date API context via semantic search.
✓Solves the 'stale context' problem where LLMs guess at crate usage patterns based on obsolete library versions.

Who It's For

Rust backend developersSystems programmers building complex software stacksAI engineers developing custom MCP-enabled coding agentsOpen source contributors maintaining Rust librariesTechnical leads overseeing AI-assisted development workflows

Metrics

Discovered2/14/2026

Reviews0

Saved By0 users

Rust Docs

Overview

Connections & Capabilities

Connects To

Capabilities

Client Compatibility

Quickstart

Claude Desktop Config

Environment Variables

Tools (1)

Security

Safety Score

Positive

Risks

Use Cases

Code Generation

Documentation Assistance

Learning and Education

Who Is This For?

Best For

Not Ideal For

Autonomy & Execution

FAQ

How do I specify a specific version of a crate?

How do I enable features for a crate?

Where are the documentation and embeddings cached?

What OpenAI model is used for embeddings?

What OpenAI model is used for summarization?

How do I configure this server with Roo Code?

What happens if the cache is corrupted?

Key Features

Use Cases & Problems Solved

Use Cases

Problems Solved

Who It's For

Metrics

Related Tools