Skip to main content
Glama
dataeyesai

dataeyes-mcp-server

Official
by dataeyesai
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.1.0

  • Disambiguation5/5

    The two tools have completely different purposes: one extracts content from a given URL, the other searches for URLs. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools use single-word imperative verbs ('read' and 'search') in lowercase, following a consistent and predictable pattern.

    Tool Count3/5

    With only two tools, the server is minimal but covers the basic web interaction workflow (search then read). It is on the lower end of the typical range but not inappropriate for a focused purpose.

    Completeness4/5

    The server provides a complete workflow for finding and reading web content. A minor gap is the lack of tools for batch processing or advanced content extraction, but core needs are met.

  • Average 3.8/5 across 2 of 2 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

  • Behavior3/5

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

    Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true. Description adds engine name and result structure but omits details on pagination, rate limits, or authentication. Adequate given annotations.

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

    Conciseness5/5

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

    Three sentences, front-loaded with action, no extraneous details. Efficient and well-structured.

    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?

    Describes output fields but does not summarize input parameters or filtering capabilities. Given the complex nested schema and multiple optional filters, the description could be more complete.

    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?

    Description does not mention any parameters or filters. Schema property descriptions cover parameter semantics adequately, so no additional context is provided. Baseline score applies.

    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?

    Clearly states the tool searches the web using DataEyes search engine and returns a list of results with specific fields (title, URL, snippet, relevance). Distinguishes from sibling 'read' which likely handles local content.

    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?

    Provides a general use case ('useful for finding information, research, discovering web pages') but lacks explicit when-to-use or when-not-to-use comparisons with alternatives. Usage is implied rather than directive.

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

  • Behavior4/5

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

    Annotations already mark it as read-only and idempotent. The description adds that it extracts main content and returns metadata, enhancing transparency without contradiction.

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

    Conciseness5/5

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

    Description is concise (4 lines), front-loaded with the main action, and free of fluff. Every sentence adds value.

    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 rich input schema and output schema (present), the description fully covers the tool's purpose and behavior. No missing elements for agent understanding.

    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 input schema already provides detailed descriptions for each parameter (url, timeout, format, etc.). The description adds only the default format, providing minimal extra semantic 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 it reads and extracts content from a web page URL, specifies default format (markdown) and metadata returned, distinguishing it from the sibling 'search' tool.

    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?

    No guidance on when to use vs the sibling 'search' tool or any prerequisites/limitations. The description does not indicate appropriate contexts or alternatives.

    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

dataeyes-mcp-server MCP server

Copy to your README.md:

Score Badge

dataeyes-mcp-server 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/dataeyesai/dataeyes-mcp-server'

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