Skip to main content
Glama
sammcj

mcp-data-extractor

by sammcj
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: extract_data focuses on extracting i18n translations and similar data from source code into JSON files, while extract_svg specifically handles SVG components from React/TypeScript/JavaScript files into individual SVG files. There is no overlap in functionality, and an agent can easily distinguish between them based on their descriptions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'extract_' as the prefix, followed by the target resource (data or svg). This naming convention is predictable and makes it easy to understand the tool's purpose at a glance.

    Tool Count2/5

    With only 2 tools, the server feels thin for a data extraction domain that could include operations like validate, transform, or merge extracted data. While the tools are well-defined, the limited count suggests incomplete coverage of potential extraction workflows, making it harder for agents to handle complex tasks.

    Completeness2/5

    The server covers extraction for specific file types (i18n data and SVGs) but lacks tools for other common extraction scenarios (e.g., images, CSS, or general text). There are no tools for validating, updating, or managing extracted data, creating significant gaps that could lead to agent failures in broader data extraction tasks.

  • Average 4.1/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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?

    Discloses key behavior: source file replacement by default and ability to disable/customize via environment variables. With no annotations, the description carries full burden; it could be more comprehensive (e.g., what happens if target directory doesn't exist, handling of multiple SVGs).

    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?

    Five sentences are well-structured, front-loaded with purpose, then preservation, then default behavior and customization options. No redundant information.

    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 no output schema, no annotations, and 2 params, the description is fairly complete but lacks mention of return values or failure modes. Could also note if it handles one or multiple SVGs per file.

    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?

    Input schema has 100% coverage with clear param descriptions. The description adds minimal extra meaning beyond what schema already provides (e.g., implying sourcePath is a code file). Baseline of 3 is appropriate.

    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 verb 'Extract' and the resource 'SVG components from React/TypeScript/JavaScript files' with output to 'individual .svg files'. It distinguishes from the sibling tool 'extract_data' by specifying 'SVG components'.

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

    Usage Guidelines4/5

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

    Provides clear context for when to use (extract SVGs from code files) and details about default source file replacement and environment variables to control behavior. However, it doesn't explicitly mention when not to use or compare with alternatives.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavioral traits: it states the source file will be replaced with a 'MIGRATED TO ...' message and that this can be disabled via environment variable. It also mentions customizing the warning message. This gives the agent clear expectations of side effects.

    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 slightly longer but every sentence is purposeful: it explains the core action, usage directive, benefits, side effects, and configuration. It is front-loaded and well-structured, though could be slightly trimmed without losing value.

    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 no output schema and a tool with side effects, the description covers usage, behavior, and configuration. It explains what happens after extraction (file replacement) and how to control it. It could explicitly mention the return value (the JSON file written) but it is implied.

    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 coverage is 100% with clear descriptions. The description adds minimal semantic value beyond the schema; it repeats the purpose but does not provide new details about parameter formats or constraints. Baseline 3 is appropriate.

    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 it extracts data content (e.g., i18n translations) from source code to a JSON file. The verb 'extract' and resource 'data content from source code to JSON file' are specific, and it distinguishes from sibling 'extract_svg' by focusing on code data rather than SVG.

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

    Usage Guidelines4/5

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

    Provides explicit usage guidance: 'When encountering files with data such as i18n content embedded in code, use this tool directly instead of reading the file content first.' It explains why (prevents filling context window) but does not explicitly mention when not to use 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

mcp-data-extractor MCP server

Copy to your README.md:

Score Badge

mcp-data-extractor 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/sammcj/mcp-data-extractor'

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