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pree-dew

mcp-bookmark

by pree-dew
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Save and search are clearly distinct actions with no overlap. An agent can easily determine which tool to use based on whether it needs to add or find a bookmark.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (save_bookmark, search_bookmark), making the naming predictable and intuitive.

    Tool Count3/5

    With only two tools, the server feels thin for a bookmarking domain. While save and search are core operations, a typical bookmark manager would likely require more tools, making the count borderline.

    Completeness2/5

    The tool surface is significantly incomplete. It offers only create (save) and search, but lacks essential operations like list, delete, and update, which would cause agent failures when those actions are needed.

  • Average 2.8/5 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

  • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
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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 carries the full burden. It mentions 'vector store' which hints at semantic search, but it does not disclose whether this is a read-only operation, how results are ranked, any limitations, or what the return format looks like. This is minimal disclosure beyond the basic action.

    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 short sentence that is immediately understandable and front-loaded. Every word earns its place without unnecessary fluff. It is concise but not so minimal that it becomes meaningless.

    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's simplicity (1 parameter, no output schema, no annotations), the description is too sparse. It does not explain the expected query format, results behavior, or when to choose this over the sibling tool. The overall context is insufficient for an agent to use the tool confidently.

    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 description has 0% schema coverage and does not explain the 'query' parameter at all. While the parameter name 'query' is suggestive, the description does not clarify what kind of input is expected (e.g., natural language, exact text, keywords) or how it is processed. The description fails to compensate for the low schema coverage.

    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 action ('Search') and the resource ('bookmark'), and the addition of 'in the vector store' adds useful context. It distinguishes from the sibling 'save_bookmark' by implying a read/retrieval operation versus a write operation.

    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 explicit guidance is given on when to use this tool versus the sibling 'save_bookmark'. The description does not mention any prerequisites, scenarios, or exclusions. The usage is only implied by the tool's name and the contrast with 'save_bookmark'.

    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 are provided, so the description must disclose behavioral traits. It only says 'Save a bookmark' but doesn't mention persistence, overwriting behavior, idempotency, or any side effects. The 'vector store' hint is vague and lacks depth.

    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, efficient sentence with no redundant wording. It gets straight to the point, though it is extremely brief for a tool with three required parameters.

    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?

    The description is minimal and fails to provide context about return values, error cases, duplicate handling, or the relationship with search_bookmark. Given the lack of annotations and output schema, this is a significant gap.

    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?

    The input schema has three parameters (name, link, additional_detail) with no descriptions, and schema_description_coverage is 0%. The description doesn't explain the meaning, format, or purpose of any parameter, leaving the agent to guess.

    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 ('save'), the resource ('bookmark'), and the destination ('vector store'), distinguishing it from the sibling tool 'search_bookmark' which searches rather than saves.

    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 the sibling 'search_bookmark'. It doesn't mention any prerequisites, typical use cases, or situations where saving 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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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