Skip to main content
Glama
kirill-markin

Weaviate MCP Server

by kirill-markin
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
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: mcp_fetch is for fetching website content, while mood is a whimsical interaction unrelated to data operations. There is no overlap or ambiguity between them.

    Naming Consistency2/5

    The naming is inconsistent: mcp_fetch follows a verb_noun pattern, but mood is a noun with no action verb. This mixing of conventions reduces predictability and clarity in the tool set.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a Weaviate MCP Server, which typically involves vector database operations like querying, indexing, or managing data. The tools provided do not align with the expected domain.

    Completeness1/5

    The tool surface is extremely incomplete for a Weaviate server. There are no tools for core vector database functions such as searching, adding data, or managing schemas, leaving significant gaps that would cause agent failures in this domain.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • No issues in the last 6 months
    • No commit activity data available
    • Last stable release on
    • 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

  • Behavior2/5

    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 mentions fetching and returning content, but doesn't cover important aspects like error handling, rate limits, authentication needs, timeouts, or what happens with invalid URLs. This leaves significant gaps for a tool that interacts with external resources.

    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 extremely concise - a single sentence that directly states the tool's function. Every word earns its place, with no unnecessary elaboration or repetition. It's front-loaded with the core functionality.

    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?

    For a tool that fetches external websites with no annotations and no output schema, the description is insufficient. It doesn't explain what format the content is returned in (HTML, text, etc.), potential limitations, error conditions, or security considerations. The lack of output schema means the description should compensate by explaining return values, which it doesn't do.

    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%, with the single parameter 'url' clearly documented in the schema. The description doesn't add any meaningful parameter information beyond what's already in the schema, so it meets the baseline for high schema coverage but doesn't provide additional value.

    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 with a specific verb ('fetches') and resource ('website'), and specifies the outcome ('returns its content'). However, it doesn't differentiate from the sibling tool 'mood', which appears unrelated but could have overlapping functionality in some contexts.

    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 alternatives or in what context it should be applied. The description only states what it does, not when it's appropriate or when other tools might be better suited.

    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 reveals the server will 'always respond with a cheerful message and a heart ❤️' which indicates predictable, positive output. However, it doesn't disclose other behavioral traits like response format details, potential errors, or interaction patterns beyond the basic promise.

    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 extremely concise at just one sentence, with zero wasted words. It's front-loaded with the core purpose and includes the behavioral promise efficiently. Every part of the single sentence earns its place by conveying essential information.

    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 tool's low complexity (single parameter, no output schema, no annotations), the description is minimally complete. It states what the tool does and the expected response behavior. However, it lacks context about why this tool exists alongside 'mcp_fetch' or what use cases it serves, leaving gaps in overall understanding.

    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 fully documents the single 'question' parameter with examples. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

    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: 'Ask the server about its mood' which is a specific verb+resource combination. It distinguishes from the sibling tool 'mcp_fetch' by focusing on mood inquiry rather than data fetching. However, it doesn't fully specify what 'mood' means in this context beyond 'always happy'.

    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?

    The description provides no guidance on when to use this tool versus alternatives. While it mentions the server's mood is 'always happy,' it doesn't explain when this inquiry is appropriate or what scenarios warrant using this tool over 'mcp_fetch' or other potential tools. No explicit when/when-not instructions are provided.

    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

example-mcp-server MCP server

Copy to your README.md:

Score Badge

example-mcp-server 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/kirill-markin/example-mcp-server'

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