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automanus-io

AutoManus MCP Server

Official
by automanus-io
OverviewSchemaRelated ServersScoreDiscussions(1)
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.2.6

  • Disambiguation5/5

    Each tool targets a distinct action: adding knowledge to an agent, creating an agent, and generating a QR code. No overlap in purpose.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (add_knowledge, create_sales_agent, generate_qr_code), making the API predictable.

    Tool Count3/5

    Three tools is a small set, but it may be appropriate for a focused initial release. However, the server's purpose likely requires more tools for full agent management.

    Completeness2/5

    Missing essential CRUD operations: no update or delete for agents, no list or remove for knowledge items, and no management features for deployed agents or QR codes.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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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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 full burden for behavioral transparency. The description only states the basic action without disclosing important behavioral traits such as whether adding knowledge is append-only, if duplicates are handled, if there are limits on number of items or content length, or any authentication requirements. This leaves significant gaps for the agent to infer behavior.

    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 concise with just two sentences. The first sentence states the main action and object, and the second sentence provides examples of use. Every sentence adds value without redundancy, making it easy for an agent to quickly grasp the tool's function.

    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 (5 parameters, 3 required, no output schema), the description covers the basic use case but lacks completeness. It does not explain the effect of adding knowledge (e.g., whether it overwrites or appends), the maximum length of content, or any constraints. More context would be needed for a fully self-contained definition.

    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 baseline is 3. The description adds some context by mentioning examples of knowledge content but does not provide additional semantics beyond what the input schema already specifies for each parameter. It adds marginal value.

    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's purpose: 'Add a knowledge base item to an existing AI sales agent.' It lists specific types of information (product info, FAQs, policies), making the action concrete. The tool is well-distinguished from siblings (create_sales_agent and generate_qr_code), which have entirely different functions.

    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?

    The description provides clear usage guidance: 'Use this to provide product info, FAQs, policies, or other information the agent should know.' It directly tells the agent when to invoke the tool. However, it does not explicitly mention when not to use it or suggest alternatives among siblings, which would slightly improve the score.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool researches the website automatically and deploys to WhatsApp and Webchat, which are key behaviors. However, it does not mention side effects (e.g., overwriting existing agents), authentication requirements, rate limits, or error conditions, leaving 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 two sentences, front-loaded with the main purpose and a practical usage hint. Every word contributes meaning with no redundancy or filler.

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

    Completeness4/5

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

    With no output schema, the description should cover return values. It states deployment targets but does not explain what the user receives (e.g., agent ID, claim link), though the schema's email description partially covers that. Overall, it is sufficient for basic use but could be more complete.

    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, so the baseline is 3. The description adds the note 'Ask the user for their email if not provided,' which reinforces the email parameter but does not provide significant new semantics beyond what the schema already describes.

    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 creates an AI sales agent, with specific actions: 'Researches the website automatically and deploys to WhatsApp and Webchat.' This differentiates it from siblings add_knowledge and generate_qr_code, which have distinct purposes.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implicitly indicates usage when creating a sales agent, and adds guidance to 'Ask the user for their email if not provided.' However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

    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?

    No annotations are provided, so the description must disclose behavioral traits. It states the tool returns a URL to the image, implying no side effects, but does not explicitly clarify that it is read-only or stateless, nor mention rate limits or authorization.

    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, front-loaded with purpose and return type.

    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 no output schema, the description should clarify parameter relationships (e.g., url vs phone+message). It does not, and size parameter is omitted. Fairly complete for a simple tool but with gaps.

    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 descriptions for each parameter. The description adds context about URL vs WhatsApp usage but does not significantly augment the schema's meaning. Baseline score 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 it generates a QR code for a URL or WhatsApp link and returns a URL to the image. This distinguishes it from siblings like add_knowledge and create_sales_agent, which are unrelated.

    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?

    The description implies its use for QR code generation, but provides no explicit guidance on when to use or not use it, nor alternatives. However, siblings are unrelated, so minimal guidance is acceptable.

    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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