Skip to main content
Glama
BobGod

WeChat Publisher MCP

by BobGod

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 clearly distinct purposes: one publishes articles and the other queries status/statistics. There is no overlap in functionality, making it impossible to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent 'wechat_verb_noun' pattern with snake_case. The naming is predictable and aligned with the server's domain.

    Tool Count2/5

    With only two tools, the server feels thin for a 'publisher' domain. It lacks basic operations like drafting, editing, deleting articles, or managing media, which are typical in content publishing workflows.

    Completeness2/5

    The toolset is severely incomplete for publishing. It covers publishing and status querying but misses essential CRUD operations (e.g., create drafts, update articles, delete posts) and other common features like media uploads or audience analytics.

  • Average 2.9/5 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.

  • 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. While '发布' (publish) implies a write/mutation operation, the description doesn't disclose important behavioral aspects: whether this is a live publish or draft creation, what authentication requirements exist beyond the appId/appSecret parameters, whether there are rate limits, what happens on success/failure, or whether the operation is reversible. For a mutation tool with 8 parameters and no annotation coverage, this represents significant gaps.

    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 Chinese sentence that communicates the core functionality efficiently. It's front-loaded with the main purpose and includes the key feature (Markdown support) without unnecessary elaboration. Every word earns its place, making this an excellent example of conciseness.

    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 with 8 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what happens after publishing (success response, error conditions), doesn't mention the sibling tool relationship, and provides minimal behavioral context. While the schema documents parameters well, the description fails to compensate for the lack of annotations and output schema, leaving significant gaps in understanding how this tool behaves in practice.

    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?

    The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds only one piece of parameter-related information: that content supports Markdown format (implied in the schema's 'Markdown格式的文章内容' but reinforced in the description). This provides minimal additional value beyond what the schema already offers, meeting the baseline expectation when schema coverage is high.

    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 ('发布' - publish) and target resource ('文章到微信公众号' - article to WeChat Official Account), making the purpose immediately understandable. It also specifies support for Markdown format, which adds useful detail. However, it doesn't explicitly differentiate from the sibling tool 'wechat_query_status' (which presumably queries status rather than publishes content).

    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 Markdown support, it doesn't indicate when to use this tool over other publishing methods or when the sibling tool 'wechat_query_status' would be more appropriate. There are no prerequisites, exclusions, or contextual recommendations provided.

    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 carries the full burden of behavioral disclosure. It states the tool queries status and statistics, implying a read-only operation, but doesn't clarify aspects like authentication needs (though parameters suggest it), rate limits, error handling, or what specific data is returned. This leaves gaps in understanding the tool's behavior.

    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, concise sentence in Chinese that directly states the tool's purpose. It's front-loaded with no unnecessary words, making it efficient. However, it could be slightly improved by adding brief context or usage hints without losing conciseness.

    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 moderate complexity (3 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage guidelines, and output expectations. With no annotations or output schema, more context would be helpful for an AI agent to use it effectively.

    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?

    The input schema has 100% description coverage, with clear parameter descriptions in Chinese (e.g., '消息ID' for msgId). The description doesn't add any additional meaning beyond the schema, such as explaining parameter relationships or usage examples. Since schema coverage is high, the baseline score of 3 is appropriate.

    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: '查询文章发布状态和统计数据' translates to 'query article publishing status and statistical data.' This specifies the verb (query) and resource (article publishing status/statistics). However, it doesn't explicitly differentiate from the sibling tool 'wechat_publish_article,' which appears to be for publishing rather than querying.

    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. It doesn't mention the sibling tool 'wechat_publish_article' or any other tools, nor does it specify prerequisites, contexts, or exclusions for usage.

    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

wechat-publisher-mcp MCP server

Copy to your README.md:

Score Badge

wechat-publisher-mcp 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/BobGod/wechat-publisher-mcp'

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