Skip to main content
Glama
OuchiniKaeru

MCP Data Analyzer

by OuchiniKaeru
OverviewSchemaRelated ServersScoreDiscussions
Python

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 completely distinct purposes: load_file is for loading data files into DataFrames, while run_script is for executing Python scripts for analytics tasks. There is no overlap in functionality or ambiguity about when to use each tool.

    Naming Consistency4/5

    Both tools use snake_case naming, which is consistent. However, load_file follows a verb_noun pattern while run_script uses verb_noun, but the noun 'script' is less specific than 'file', creating a minor deviation in clarity. Overall, the naming is mostly predictable and readable.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a 'Data Analyzer' purpose. Key operations like data transformation, filtering, aggregation, or visualization-specific tools are missing, making it inadequate for comprehensive data analysis workflows.

    Completeness2/5

    The tool set is significantly incomplete for data analysis. While loading and script execution are covered, there are major gaps: no tools for data cleaning, transformation, statistical analysis, or dedicated visualization. This will likely cause agent failures when trying to perform common analytics tasks beyond basic loading and scripting.

  • Average 3.7/5 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it executes Python scripts, outputs results via stdout, allows saving DataFrames in memory and creating charts, and prohibits overwriting original DataFrames. This covers core functionality, constraints, and output methods, though it lacks details on error handling, execution environment, or resource limits.

    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 (Purpose, Allowed Actions, Prohibited Actions) and uses bullet points for readability. It's appropriately sized with no redundant sentences, though the 'Purpose' section could be more front-loaded with critical details.

    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 complexity (executing arbitrary Python scripts), no annotations, no output schema, and low schema coverage, the description is moderately complete. It covers basic behavior and constraints but lacks details on return values, error cases, execution context, or integration with sibling tools, leaving gaps for an AI agent to infer.

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

    Parameters2/5

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

    The input schema has 0% description coverage, so the description must compensate. It mentions 'save_to_memory' in the allowed actions, providing some semantic context for that parameter, but doesn't explain the 'script' parameter beyond implying it's for Python code. This leaves key parameter details undocumented, failing to fully compensate for the schema gap.

    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 as 'Execute Python scripts for specific data analytics tasks,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'load_file' (which likely loads files rather than executing scripts), so it misses full sibling differentiation.

    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 provides implied usage through 'Allowed Actions' and 'Prohibited Actions' sections, which suggest when to use this tool (for data analytics tasks with specific allowed outputs) and some constraints (not overwriting DataFrames). However, it doesn't explicitly state when to use this vs. alternatives like 'load_file' or other potential tools, nor does it provide clear exclusions or prerequisites.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It explains default naming behavior for df_name and sheet selection for XLSX files, which are useful behavioral traits. However, it doesn't address important aspects like error handling, file size limits, memory implications, or what the output looks like (though there's no output schema).

    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?

    The description is efficiently structured with clear sections (Purpose, Usage Notes), uses bullet points for readability, and contains no redundant information. Every sentence adds value, and the information is appropriately front-loaded with the core purpose stated first.

    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?

    For a data loading tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description provides adequate basic information but lacks important context. It doesn't explain what a DataFrame is in this context, doesn't mention supported file encodings or formats beyond CSV/XLSX, and doesn't describe error conditions or the structure of returned data.

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

    Parameters4/5

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

    With 0% schema description coverage, the description must compensate. It provides meaningful context for all three parameters: file_path is implied as the data source, df_name gets default naming behavior explained, and sheet_name gets XLSX-specific guidance. The description adds substantial value beyond the bare schema, though it doesn't explain parameter formats or constraints.

    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 specific action ('Load a local CSV or XLSX file into a DataFrame'), identifies the resource (data files), and distinguishes this from its only sibling 'run_script' by focusing on data loading rather than script execution. The purpose is concrete and unambiguous.

    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 provides implied usage guidance through the 'Usage Notes' section, which explains default behaviors for optional parameters. However, it doesn't explicitly state when to use this tool versus alternatives (like 'run_script') or mention any prerequisites or exclusions. The guidance is helpful but incomplete.

    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-analyzer MCP server

Copy to your README.md:

Score Badge

mcp-data-analyzer 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/OuchiniKaeru/mcp-data-analyzer'

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