Skip to main content
Glama
yksanjo

fusion-mcp

by yksanjo
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of overlapping or confusing tools. The single tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    With a single tool, the naming is trivially consistent. The verb_noun pattern (fusion_ask) is clear and well-formed.

    Tool Count2/5

    A single tool is too few for a typical MCP server, making the tool surface feel sparse. Even if the tool is powerful, the server lacks the breadth expected of a useful toolkit.

    Completeness4/5

    The server fully covers its stated purpose of asking a fusion panel, but there are minor gaps such as no configuration options or alternative request modes. The core functionality is complete, but some auxiliary features are missing.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

  • Behavior4/5

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

    With no annotations, the description carries the full burden and does well by disclosing the parallel panel behavior, web search, judge comparison, and single synthesized answer. It also surfaces the cost implications. It does not cover edge cases like failure modes or latency, but for the described functionality it is adequately transparent.

    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 three sentences, front-loaded with the core purpose, and every sentence contributes (purpose, use cases, cost trade-off). There is no redundancy or filler, making it highly efficient.

    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 the absence of an output schema and annotations, the description covers the essential context: it explains what the tool does, when to use it, and its cost. It mentions the return value ('one synthesized answer is returned'). It lacks details on performance or error handling, but these are secondary for a tool of this complexity.

    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?

    Schema coverage is 100%, setting a baseline of 3. The description adds value by explaining the high-level process (panel of models, judge comparison), which clarifies the role of parameters like judge_model and analysis_models beyond their raw schema definitions. This contextual enrichment justifies a small uplift over the baseline.

    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: 'Ask OpenRouter Fusion: a panel of models answers your prompt in parallel...' and explains the process (panel, judge, synthesized answer). It distinguishes itself from a single model call by emphasizing the multi-model synthesis, leaving no ambiguity about what the tool does.

    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 use cases: 'Use for research, expert critique, or any question where being wrong is costly.' It also implies a trade-off with cost: 'Costs more than a single call (panel + judge completions),' which serves as a when-not-to-use hint. However, it does not explicitly name alternative tools or provide a formal when-not-to-use list, so it falls just short of a 5.

    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

fusion-mcp MCP server

Copy to your README.md:

Score Badge

fusion-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/yksanjo/fusion-mcp'

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