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sampathpulukurthi

mcp-agentic-rag

by sampathpulukurthi
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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation4/5

    The two query tools are distinguished by the fallback feature, and seed is clearly separate. Some confusion possible but descriptions help.

    Naming Consistency5/5

    All tools follow a consistent snake_case verb_noun pattern, making naming predictable and logical.

    Tool Count3/5

    With only 3 tools, the set feels minimal but is within acceptable bounds for a simple RAG server. However, missing management tools make it slightly thin.

    Completeness2/5

    The toolset lacks capabilities to update, delete, or list documents, which are essential for a complete RAG knowledge base management. This is a significant gap.

  • Average 2.7/5 across 3 of 3 tools scored. Lowest: 1.9/5.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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.

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    {
  "$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

  • Behavior1/5

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

    With no annotations, the description must disclose behavioral traits, but it only states 'Query the RAG knowledge base'. It does not mention that the operation is read-only, any side effects, authentication needs, or rate limits. The description adds no behavioral context beyond the name.

    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 concise (one sentence) but under-specified. While it is front-loaded, it fails to provide necessary information, making it a tautology of the tool name. Conciseness without substance is not beneficial.

    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?

    Given the tool has an output schema, return values are not needed, but the description lacks context on when to use this tool, parameter semantics, and how it differs from siblings. It is incomplete for an agent to correctly select and invoke it.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description does not explain any parameters. The 'query' and 'k' parameters are not described at all, leaving the agent to infer their meaning from names alone. The default for k (5) is in the schema but not clarified as the number of results.

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

    Purpose3/5

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

    The description 'Query the RAG knowledge base' provides a clear verb and resource, but it is generic and does not distinguish from the sibling 'query_rag_with_fallback'. It lacks specificity about the query type (e.g., semantic 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 given on when to use this tool versus alternatives like 'query_rag_with_fallback' or 'seed_faq'. There are no usage conditions, when-not, or contexts provided.

    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?

    Discloses web fallback behavior but lacks details on fallback conditions, latency, or side effects. No annotations provided.

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

    Conciseness3/5

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

    Very concise (one sentence), but at the expense of omitting necessary parameter details and usage context.

    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?

    Minimal description for a tool with fallback behavior and two undocumented parameters; output schema reduces need for return value explanation but parameter semantics missing.

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

    Parameters1/5

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

    Schema description coverage is 0%, and description provides no explanation for 'query' or 'k' parameters, leaving agent uninformed about their roles.

    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 queries the RAG knowledge base with web fallback, but does not explicitly distinguish from sibling 'query_rag' which likely lacks fallback.

    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 this tool over siblings 'query_rag' or 'seed_faq', and no mention of circumstances for web fallback triggering.

    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?

    No annotations provided. The description only states the action without disclosing behavioral traits like destructive potential (overwrites existing data?), idempotency, or required permissions. For a seed operation, such details are critical.

    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?

    Single sentence, no wasted words. Front-loaded with action and resource. Every word earns its place.

    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 complexity is low (no parameters, simple action) and output schema exists, the description is mostly adequate but lacks information on idempotency, error states, and whether seeding is cumulative or replaces. Agent may need to infer or test.

    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?

    No parameters in schema, so description adds no parameter info. Baseline for 0-parameter tools is 4, and the description sufficiently covers the tool's purpose. No additional parameter details needed.

    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 action ('Seed') and the resource ('knowledge base') with a specific dataset ('ML FAQ dataset'). It distinguishes from sibling query tools (query_rag, query_rag_with_fallback) by implying this is an initialization step.

    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 this tool vs alternatives. No mention of prerequisites, idempotency, or whether it should be run once or on updates. Sibling tools are query-focused but no explicit comparison.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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