Skip to main content
Glama
zssty2010

Agnes MCP Server

by zssty2010
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    Each tool targets a distinct operation: image generation, video generation submission, and video status polling/download. No overlapping functionality, making selection unambiguous.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (generate_image, generate_video, get_video), with no deviations or mixed conventions.

    Tool Count5/5

    Three tools cover the essential workflows for image and video generation without unnecessary bloat, fitting well within the ideal 3-15 range.

    Completeness4/5

    Core generation and status retrieval are covered. Missing delete or list operations for generated content, but these are minor gaps given the server's likely purpose.

  • Average 3.6/5 across 3 of 3 tools scored. Lowest: 3/5.

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

    • No community issues in the last 6 months
    • 9 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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, the description must disclose behavioral traits. It mentions two modes but does not explain prerequisites (e.g., requiring images parameter for editing), return format, or other side effects. This is insufficient for a tool with 5 parameters.

    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 concise with two sentences and no redundancy. However, it sacrifices necessary detail in favor of brevity. It is well-structured but lacks depth.

    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 5 parameters, no output schema, and no annotations, the description is incomplete. It does not address return values, parameter roles, or typical use cases, making it inadequate for complex interactions.

    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 0% description coverage, and the description adds almost no meaning beyond stating modes. Parameters like size, format, images, and outputDir are not explained, leaving the agent with no guidance on how to use them.

    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: generate or edit an image using a specific API. It mentions two modes (text-to-image and image-to-image), which distinguishes it from sibling tools that handle video.

    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 implies usage for image generation/editing but does not explicitly guide when to use this tool versus siblings like generate_video or get_video. No exclusions or alternatives are mentioned.

    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, the description carries full burden. It discloses the default async behavior (returns videoId), the blocking option, and auto-download. However, it omits potential side effects, authentication, rate limits, or failure handling.

    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?

    Three concise sentences, each adding value. Front-loaded with the primary action, then options. No redundant information.

    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 11 parameters, no output schema, and no annotations, the description is incomplete. It covers key workflow but leaves many parameters unexplained, forcing the agent to guess or rely on external knowledge.

    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?

    Schema coverage is 0%, so description must compensate. It only explains wait and outputDir, leaving 9 parameters (prompt, mode, seed, image, width, height, frame_rate, num_frames, negative_prompt) undocumented. This is insufficient for an agent to use the tool effectively.

    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 identifies the tool's purpose: submitting a video generation task. It distinguishes from siblings by mentioning videoId polling (related to get_video) and implies difference from generate_image.

    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 explicit guidance on using wait=false (default) for polling vs wait=true for blocking, and mentions outputDir for auto-download. It does not explicitly exclude scenarios but offers clear context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    With no annotations, the description fully covers behavior: it's a read operation returning status and download URL, with optional auto-download via outputDir. It does not mention side effects, but 'Get' implies non-destructive. The description adds context beyond minimal.

    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?

    Three concise sentences: first frames purpose, second lists parameters and returns, third notes optional auto-download. No redundant information; every sentence earns its place.

    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?

    Given no output schema and no annotations, the description covers inputs, outputs (progress, status, download URL), and optional behavior. It lacks mention of error cases, prerequisites (e.g., needing to have called generate_video first), or authentication needs, but is fairly complete for a simple status-checking tool.

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

    Parameters4/5

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

    The schema has 0% coverage (no parameter descriptions). The description explains videoId (required), taskId (optional for identifying task), and outputDir (for auto-download). This adds meaning beyond the raw schema, though formats and constraints are not detailed.

    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 retrieves the status of a video generation task and optionally downloads it. The verb 'Get' and resource 'video generation task' are specific. Siblings 'generate_image' and 'generate_video' are distinct, so no confusion.

    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 implies use after initiating a generation task but does not explicitly state when to use this tool versus alternatives. No mention of prerequisites or when not to use it. The context of siblings suggests usage, but explicit guidance is missing.

    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

agnes-mcp-server MCP server

Copy to your README.md:

Score Badge

agnes-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/zssty2010/agnes-mcp-server'

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