Skip to main content
Glama
patanjali-22

mcpRAG

by patanjali-22
OverviewSchemaRelated ServersScoreDiscussions
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves from a specific FAQ collection, the other performs general web searches. There is no functional overlap, and each tool's description directs usage to its intended context.

    Naming Consistency5/5

    Both tools use the same naming pattern: a descriptive source/domain prefix combined with the action and the suffix '_tool', all in snake_case. This is consistent and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a general RAG use case. The count is borderline—not excessive, but the minimal surface may leave agents wanting more specialized retrieval options.

    Completeness2/5

    The server only provides retrieval operations, with no tools for managing or updating the FAQ collection or ingesting new documents. This is a significant gap for a complete RAG workflow, potentially causing agent failures when asked to perform any write or update operation.

  • Average 4.2/5 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description is the sole source of behavioral disclosure. It mentions searching and returning results but does not disclose any potential side effects, rate limits, authentication needs, or failure modes. The description only covers input and output, not behavioral traits.

    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 concise and front-loaded: the first sentence states the purpose, the second gives usage guidance, and the input/output block is clearly structured. No unnecessary words.

    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?

    For a simple search tool with one parameter and no output schema, the description adequately covers purpose, usage, and input/output. However, it does not specify the number of results returned or behavior when no results are found, which would improve completeness.

    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?

    The schema has only one parameter, 'query', with no description. The description clarifies that the query is 'The user query to search for information,' which adds semantic meaning beyond the parameter name. It also describes the output format, aiding understanding.

    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 uses the specific verb 'Search' and identifies the resource ('information on a given topic using Bright Data'). It distinguishes from the sibling faq_retrieval_tool by stating it is for topics 'not related to general machine learning.'

    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 explicitly states when to use the tool: 'Use this tool when the user asks about a specific topic or question that is not related to general machine learning.' This provides a clear conditional and implies an alternative for ML-related queries.

    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 transparency burden. It implies a read-only retrieval via 'retrieve' and mentions vector DB, but does not disclose limitations, error behavior, or any side effects. This is adequate but not rich.

    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 compact, with a one-sentence purpose, a usage directive, and clearly labeled input/output sections. No redundant information, and the structure enhances readability.

    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?

    For a single-parameter tool with no output schema and no annotations, the description covers the essential purpose, usage, input, and output. It lacks details on edge cases like empty results, but overall it is sufficient for basic invocation.

    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?

    The schema only defines 'query' as a string, but the description explains it as 'The user query to retrieve the most relevant documents,' adding semantic meaning beyond the schema. While no format or constraints are given, it clarifies the parameter's role.

    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 retrieves relevant documents from an FAQ collection, with a specific resource and action. It also emphasizes the F1 Racing domain, distinguishing it from the sibling web 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 Guidelines4/5

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

    Explicitly states 'Use this tool when the user asks about F1 Racing,' providing clear usage context. However, it does not mention alternatives or when not to use, so it lacks full exclusions.

    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

RAG-App-MCP MCP server

Copy to your README.md:

Score Badge

RAG-App-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/patanjali-22/RAG-App-MCP'

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