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DeepSpringAI

parquet mcp server

by DeepSpringAI
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have clearly distinct purposes: one for performing web searches and scraping results, the other for extracting information from those prior searches. While they are related, there is no functional overlap.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern. 'search-web' and 'extract-info-from-search' both clearly indicate action and target.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a web search and extraction domain. However, the tools cover a basic two-step workflow, making the count borderline acceptable.

    Completeness3/5

    The server provides a basic search and extraction cycle but lacks obvious features like managing search history, caching, or supporting different output formats. Minor gaps exist.

  • Average 2.2/5 across 2 of 2 tools scored. Lowest: 1.5/5.

    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

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  • This repository includes a README.md file.

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

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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

  • Behavior1/5

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

    With no annotations, the description fails to disclose any behavioral traits. It does not indicate whether the tool is read-only, mutates state, requires authentication, or what side effects occur. The phrase 'extract' is not clarified.

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

    Conciseness2/5

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

    The description is extremely brief (4 words), but it sacrifices clarity for brevity. It is not front-loaded with key information and omits essential details, making it under-specified rather than appropriately concise.

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

    Completeness1/5

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

    Given the lack of annotations, output schema, and the tool's potential relation to 'search-web', the description is inadequate. It does not explain what the tool returns, how it uses the queries, or how it differs from a regular search. The tool appears to be for refining or summarizing previous searches, but this is not conveyed.

    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 provides a clear description for the only parameter: 'List of search queries to merge'. The tool description adds no value and even introduces confusion by using 'extract' instead of 'merge'. Schema coverage is 100%, so the description does not enhance parameter understanding.

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

    Purpose2/5

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

    The description says 'Extract relative information from previous searches', which is vague. The term 'relative' is ambiguous (likely meant 'relevant'), and 'previous searches' is unclear—does it mean past user searches or the provided queries? The schema indicates the tool merges queries, but the description doesn't align, causing confusion.

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

    Usage Guidelines1/5

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

    No guidance on when to use this tool versus alternatives like 'search-web'. There is no mention of prerequisites, context, or any criteria for appropriate usage.

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

  • Behavior2/5

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

    With no annotations, the description must disclose behavioral traits. It states that results are scraped but provides no details on output format, pagination behavior, rate limits, or error handling.

    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 a single, concise sentence with no extraneous information, making it efficiently front-loaded.

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

    Completeness2/5

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

    Without an output schema or annotations, the description is insufficient for a web search tool. It does not explain return values, result structure, or how scraping integrates with search, leaving significant gaps.

    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 both parameters. The description adds no additional meaning beyond the parameter descriptions, meeting the baseline of 3.

    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 action: perform a web search and scrape results. It uses a specific verb-resource pair, but does not explicitly differentiate from the sibling tool 'extract-info-from-search'.

    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 is provided on when to use this tool versus the sibling tool 'extract-info-from-search' or in what contexts it is appropriate.

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

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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.

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