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awkoy

notion-mcp-server

by awkoy

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v2.12.0

  • Disambiguation5/5

    The two tools have distinct, complementary roles: notion_describe provides schema guidance for complex payloads, while notion_execute performs the actual operations. No overlap in purpose.

    Naming Consistency5/5

    Both tools follow a clear 'notion_' prefix with a verb (describe/execute), maintaining a consistent naming pattern.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a comprehensive Notion API wrapper. However, the design centralizes operations into notion_execute, which could be suitable if the goal is a thin execution layer.

    Completeness2/5

    The tool surface lacks explicit representation of common Notion operations (e.g., query database, create page, list blocks). All operations are hidden behind the generic notion_execute tool, making it hard for agents to discover capabilities without external documentation.

  • Average 4.8/5 across 2 of 2 tools scored.

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

    • 5 of 5 community issues answered or closed in the last 6 months
    • 76 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.

  • This repository includes a glama.json configuration file.

  • 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

  • Behavior5/5

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

    Annotations already indicate readOnlyHint=true; description adds that it returns schema and example without contradicting. No additional behavioral traits needed.

    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, no wasted words, information front-loaded. Every sentence 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?

    For a simple tool with one parameter and no output schema, the description fully explains what it returns and when to use it. Complete and appropriately sized.

    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% for the single parameter, so baseline is 3. Description adds no additional meaning beyond the schema, which is sufficient.

    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 explicitly states it returns 'JSON Schema and a working example for one operation', which clearly defines the action and resource. It also distinguishes from the sibling tool notion_execute.

    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?

    Provides clear guidance: use BEFORE notion_execute for non-trivial payloads, otherwise just call notion_execute. Includes explicit alternatives and when-not-to-use.

    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?

    Discloses default slimmed responses, optional verbose mode, batch features (atomic, idempotency_key, concurrency), and error response behavior. Annotations indicate destructiveHint=true and openWorldHint=true, and the description adds valuable operational context without contradiction.

    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 well-structured with bullet points and front-loaded purpose. While every sentence adds value, it could be slightly more concise, but it remains efficient for the complexity.

    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?

    For a complex, open-world tool with no output schema, the description thoroughly covers usage patterns (single/batch), response behavior (verbose, slimmed), error handling, and links to companion tool notion_describe. No gaps remain.

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

    Parameters5/5

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

    Schema coverage is 100% but the description enriches both parameters: 'operation' links to notion_describe and lists common ops; 'payload' explains single vs batch structure and optional flags (verbose, atomic, etc.), adding substantial meaning beyond 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?

    The description clearly states 'Execute a Notion operation by name' and provides concrete examples of single and batch calls. It distinguishes itself from the sibling notion_describe tool, making the purpose unambiguous.

    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 advises when to use notion_describe ('for complex shapes') and when to use this tool directly. Also explains how to handle malformed payloads with automatic schema feedback, guiding effective retries.

    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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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.

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