Skip to main content
Glama
nanameru

Next.js Docs MCP

by nanameru
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'get_all_docs' has a single, clear purpose: to provide all Next.js documentation URLs for AI agents. This eliminates any ambiguity in tool selection.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'get_all_docs' follows a clear verb_noun pattern (get + all_docs), which is straightforward and descriptive, leaving no room for inconsistency.

    Tool Count2/5

    A single tool for a documentation server is too few for the apparent scope, as it only provides URLs without direct operations like searching, filtering, or fetching specific content. This forces agents to handle all complexity externally, making the tool surface thin and limiting functionality.

    Completeness2/5

    The tool surface is severely incomplete for a documentation server. It lacks essential operations such as searching documentation, fetching content directly, or accessing specific sections. While it provides URLs, agents must implement additional steps to retrieve and process content, creating gaps that could lead to inefficiencies or failures.

  • Average 4.4/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as returning a list of URLs, requiring agents to fetch content post-call with specific constraints (10s timeout, concurrency limit of 3, follow redirects), and handling failures by trying next URLs. However, it doesn't mention rate limits, authentication needs, or potential errors from the tool itself, leaving some 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 for auto-trigger guidance, post-call instructions, and tool purpose. It's appropriately sized for its complexity, but some sentences could be more concise (e.g., the post-call instructions are detailed but slightly verbose). Overall, it's front-loaded with purpose and efficient in conveying necessary information.

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

    Completeness5/5

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

    Given the tool's complexity (no annotations, no output schema, but rich behavioral guidance), the description is complete. It covers purpose, usage guidelines, behavioral traits, and post-processing steps thoroughly. The absence of an output schema is compensated by explaining the return type (list of URLs) and how to handle it, making it sufficient for an AI agent to use effectively.

    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?

    Schema description coverage is 100%, so the schema already documents all three parameters. The description adds context by explaining how 'context' and 'filePath' are used by clients for invocation decisions, but it doesn't provide additional syntax or format details beyond what the schema states. With high schema coverage, a baseline score of 3 is appropriate as the description adds minimal extra value.

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

    Purpose5/5

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

    The description clearly states the tool's purpose: 'Provide all Next.js documentation URLs for AI agents.' It specifies the exact resource (Next.js documentation URLs) and the action (provide all of them). The description distinguishes this tool by emphasizing it returns a 'comprehensive list' for agents to fetch from, which is specific and actionable.

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

    Usage Guidelines5/5

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

    The description provides explicit guidance on when to use this tool, including auto-trigger conditions based on user queries, error keywords, and file paths. It also includes post-call instructions for how agents should handle the output, specifying actions like fetching top URLs and fallback strategies. This covers both invocation timing and post-processing steps comprehensively.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Next.js-Docs-MCP MCP server

Copy to your README.md:

Score Badge

Next.js-Docs-MCP MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nanameru/Next.js-Docs-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server