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FallseF

Fusion MCP Server

by FallseF

Fusion MCP Server

A Model Context Protocol (MCP) server that provides enhanced AI interactions through fusion algorithms and data transformation capabilities.

Features

  • Data Analysis: Analyze data using statistical, machine learning, or hybrid methods

  • Data Transformation: Transform data between different formats using fusion techniques

  • MCP Compatible: Fully compatible with Model Context Protocol specification

Related MCP server: Statsource MCP Server

Installation

npm install

Usage

Start the server:

npm start

For development with auto-reload:

npm run dev

Available Tools

fusion_analyze

Analyze data using various fusion algorithms.

Parameters:

  • data (required): Data to analyze

  • method (optional): Analysis method - "statistical", "ml", or "hybrid"

fusion_transform

Transform data using fusion techniques.

Parameters:

  • input (required): Input data to transform

  • target_format (required): Target format for transformation

Development

Run linting:

npm run lint

Format code:

npm run format

Run tests:

npm test

License

MIT

Available Tools

2 tools
fusion_analyzeC

Analyze data using fusion algorithms

ParametersJSON Schema
NameRequiredDescriptionDefault
dataYesData to analyze
methodNoAnalysis method to use

TDQS

C2.6/5.0
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. It mentions 'analyze' but doesn't specify whether this is a read-only operation, if it modifies data, requires authentication, has rate limits, or what the output looks like. The description is too vague to understand the tool's behavior beyond the basic action.

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, straightforward sentence with no wasted words. It's appropriately concise for a simple tool, though it could be more informative. The structure is clear but lacks depth that might be needed for better understanding.

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 the complexity implied by 'fusion algorithms' and the lack of annotations and output schema, the description is insufficient. It doesn't explain what analysis results to expect, how 'fusion' differs from other methods, or any behavioral traits. For a tool with two parameters and no structured output information, more context is needed to be 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, with clear documentation for both parameters ('data' and 'method' with enum values). The description doesn't add any meaningful information beyond what's in the schema—it doesn't explain what 'fusion algorithms' entail or how they relate to the parameters. 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.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Analyze data using fusion algorithms', which provides a basic purpose (analyzing data) and mentions the technique (fusion algorithms). However, it lacks specificity about what kind of analysis is performed or what 'fusion' means in this context. It doesn't clearly distinguish from the sibling tool 'fusion_transform', which might also process data.

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 'fusion_transform' or suggest scenarios where analysis is preferred over transformation. There's no information about prerequisites, input data formats, or expected outcomes that would help an agent decide when to invoke it.

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

fusion_transformC

Transform data using fusion techniques

ParametersJSON Schema
NameRequiredDescriptionDefault
inputYesInput data to transform
target_formatYesTarget format for transformation

TDQS

C2/5.0
Behavior1/5

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. However, it only states the action ('transform data') without any details on traits like side effects, permissions needed, rate limits, or what the transformation entails (e.g., destructive changes, format conversions). This leaves critical behavioral aspects unspecified for a tool that likely performs mutations.

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, efficient sentence with no wasted words, making it appropriately concise. However, it lacks front-loading of critical details (e.g., purpose differentiation), which slightly reduces its effectiveness despite the brevity.

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 the complexity implied by 'fusion techniques' and the lack of annotations and output schema, the description is incomplete. It fails to explain what the tool does beyond a vague action, what 'fusion' means, or what the transformation output looks like, leaving significant gaps for the agent to understand and use the tool 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 documentation for both parameters ('input' and 'target_format'). The description adds no additional meaning beyond the schema, such as examples of valid inputs or formats, but since the schema is comprehensive, the baseline score of 3 is appropriate as it doesn't detract from the existing information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Transform data using fusion techniques' is vague and tautological—it essentially restates the tool name 'fusion_transform' without specifying what 'fusion techniques' entail or what kind of data transformation occurs. It does not distinguish this tool from its sibling 'fusion_analyze', leaving the agent unclear about the specific action (transform vs. analyze).

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

Usage Guidelines1/5

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, such as its sibling 'fusion_analyze'. There is no mention of context, prerequisites, or exclusions, leaving the agent with no information to decide between tools or understand appropriate use cases.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updates
    • First observedfusion_analyze
    • First observedfusion_transform

TDQS

C2.6/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: 'fusion_analyze' is for analysis, while 'fusion_transform' is for transformation. There is no overlap or ambiguity between these operations, making it easy for an agent to select the correct tool based on the task.

Naming Consistency5/5

Both tools follow a consistent 'fusion_' prefix with a verb_noun pattern ('analyze' and 'transform'), using snake_case throughout. This predictable naming scheme enhances readability and reduces confusion for agents.

Tool Count2/5

With only 2 tools, the server feels thin and under-scoped for a domain like 'fusion' which typically involves complex data operations. A more complete surface would likely include additional tools for tasks like data ingestion, validation, or visualization, making this count borderline inadequate.

Completeness2/5

The tool set is severely incomplete for a data fusion domain. While analysis and transformation are core functions, there are obvious gaps such as tools for data loading, cleaning, merging, or output generation. This limited coverage may lead to agent failures when handling end-to-end workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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