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enached134-ctrl

Agentic RAG MCP

by enached134-ctrl
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct purpose: 'ask' provides synthesized answers, 'search' returns raw chunks, and 'ingest' adds content. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names are single-word verbs in lowercase, following a consistent and predictable pattern. No mixing of conventions.

    Tool Count5/5

    Three tools is ideal for this RAG server: one for ingestion, one for answering, and one for raw retrieval. The scope is well-defined without unnecessary tools.

    Completeness4/5

    The tool set covers the core RAG workflow (ingest, ask, search). A potential minor gap is the lack of a delete or update tool, but for the stated purpose it is highly complete.

  • Average 4.6/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 17 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

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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.

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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?

    No annotations are provided, so the description carries full burden. It discloses the main side effects: will scrape, chunk, embed, and store. However, it does not mention potential failure modes, authentication requirements, or whether the operation is idempotent.

    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?

    Two sentences, front-loaded with the core action, no unnecessary detail. Every word adds value.

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

    Completeness5/5

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

    Given the tool's simplicity (one parameter) and the presence of an output schema, the description fully covers the purpose, use case, and process. No gaps remain.

    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 a clear schema description for the sole parameter 'url'. The description adds context about the overall process but does not add parameter-specific semantics beyond the schema.

    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?

    Description clearly states the action ('Add a web page to the knowledge base') and the specific steps (scrape, chunk, embed, store). It distinguishes itself from siblings 'ask' and 'search' by mentioning that the added content enables future queries.

    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?

    Description explicitly says 'Use this to teach the system new source material before querying it,' providing clear when-to-use context. It does not explicitly mention when not to use or name alternatives, but the sibling tools are implied.

    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 behavior: it describes the multi-agent RAG pipeline (plan, retrieve, synthesize, self-critique) and notes source-citation. This goes beyond a simple claim of answering questions.

    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 three sentences, front-loaded with the core purpose, and contains no redundant or unnecessary information. Every sentence earns its place.

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

    Completeness5/5

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

    Given the one-parameter schema, output schema existence, and clear sibling differentiation, the description provides all necessary context. It covers behavior, usage constraints, and alternatives comprehensively.

    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% and already describes the parameter as 'A natural-language question to answer from the knowledge base.' The tool description adds minimal extra semantic meaning, so 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 tool answers a question with a written, source-cited answer, distinguishing it from the sibling tool 'search'. The specific verb 'answer' and resource 'question' are well defined.

    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?

    Explicit guidance is given: use when the user wants an answer, and for raw matching documents use 'search'. This provides clear context and an alternative, satisfying high standards.

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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses that output includes raw chunks and similarity scores, and that no synthesized answer is produced. It implies a read-only retrieval operation. Lacks explicit mention of authentication or side effects, but given nature of tool, this is sufficient.

    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?

    Two sentences, first sentence front-loads primary action and key characteristics, second provides usage guidance. Every sentence serves a purpose with no redundancy.

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

    Completeness5/5

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

    Tool has output schema, so return value details are covered. Description provides complete guidance for a simple retrieval tool: what it does, when to use, and how it differs from sibling. No missing information.

    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?

    Schema has 100% description coverage, baseline 3. Description adds meaning by calling results 'raw top-k source chunks' and mentioning 'similarity scores', which provides context beyond schema field descriptions.

    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?

    Description clearly states 'Retrieve the raw top-k source chunks matching a QUERY' with specific verb and resource. It distinguishes from sibling 'ask' by specifying that it returns raw chunks without synthesized answer, providing clear differentiation.

    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?

    Explicitly states when to use: 'Use this when you want the underlying documents themselves.' and provides alternative: 'To get a written, cited answer instead, use `ask`.' No ambiguity.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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