Skip to main content
Glama
rcdelacruz

Nexus MCP Server

by rcdelacruz
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

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: 'nexus_read' fetches and parses content from a specific URL, while 'nexus_search' performs web searches based on a query. There is no overlap in functionality, and an agent can easily differentiate between them based on whether it needs to retrieve known content or discover new information.

    Naming Consistency5/5

    Both tools follow a consistent 'nexus_verb' naming pattern, using snake_case throughout. The verbs 'read' and 'search' are distinct and accurately describe their respective actions, creating a predictable and readable naming convention across the tool set.

    Tool Count2/5

    With only two tools, the server feels under-scoped for its apparent purpose of intelligent web content retrieval and search. This minimal set may limit agents' ability to perform more complex workflows, such as saving, organizing, or analyzing retrieved content, making it borderline too thin for practical use.

    Completeness3/5

    The tools cover basic retrieval (read) and discovery (search) functions, but there are notable gaps. For example, there is no tool for managing or storing fetched content, no way to interact with search results beyond viewing them, and no update or deletion capabilities, which could hinder agents in more advanced tasks.

  • Average 4.1/5 across 2 of 2 tools scored.

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

  • 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

  • Behavior3/5

    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 describes the tool's parsing logic and return behavior ('Parsed and cleaned content from the URL'), which adds value beyond the input schema. However, it does not cover important aspects like error handling, rate limits, authentication needs, or performance characteristics, resulting in moderate transparency.

    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 appropriately sized and well-structured, with a brief purpose statement followed by 'Args:' and 'Returns:' sections. Each sentence adds value without redundancy, making it easy to scan and understand. The formatting enhances readability without unnecessary verbosity.

    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 tool's moderate complexity, no annotations, and an output schema present, the description is largely complete. It covers the purpose, parameters, and return value adequately. However, it could improve by addressing behavioral aspects like error cases or limitations, slightly reducing completeness for a tool with parsing logic.

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

    Parameters5/5

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

    The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains the 'url' parameter as 'The URL to visit' and details the 'focus' parameter with three modes ('general', 'code', 'auto'), including their effects. This fully compensates for the schema's lack of descriptions, providing clear semantics for both parameters.

    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: 'Reads a URL with intelligent parsing logic.' It specifies the verb ('Reads') and resource ('URL'), and distinguishes it from the sibling tool 'nexus_search' by focusing on parsing content from a given URL rather than searching. However, it doesn't explicitly differentiate from 'nexus_search' beyond the name, which slightly limits clarity.

    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 through the 'focus' parameter options ('general', 'code', 'auto'), suggesting when to use different modes based on content type. However, it lacks explicit guidance on when to use this tool versus 'nexus_search' or other alternatives, and does not mention any prerequisites or exclusions, leaving usage context somewhat vague.

    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 provided, the description carries the full burden of behavioral disclosure. It explains the hybrid nature of the search and the two modes, but doesn't mention rate limits, authentication requirements, or what happens with invalid inputs. The description adds some context but lacks comprehensive behavioral details.

    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 perfectly structured and front-loaded: it starts with the core purpose, then clearly lists arguments with explanations, and ends with return information. Every sentence earns its place with no wasted words, 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 tool has an output schema (which handles return value documentation) and the description provides good parameter semantics, it's mostly complete. However, for a search tool with no annotations, it could benefit from mentioning rate limits or authentication requirements to achieve full completeness.

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

    Parameters5/5

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

    The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains what each parameter does: 'query' as the search term, 'mode' with specific explanations for 'general' and 'docs' options, and 'max_results' with its range constraint (1-20). This fully compensates for the schema's lack of descriptions.

    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: 'A hybrid search tool combining Exa's breadth and Ref's specificity.' It specifies the verb (search) and resource (web/technical documentation), and distinguishes it from its sibling 'nexus_read' by focusing on search rather than reading operations.

    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 context for when to use different modes: 'general' for broad web search and 'docs' to prioritize technical documentation. However, it doesn't explicitly state when NOT to use this tool or mention alternatives beyond the mode selection, which prevents a perfect score.

    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

nexus-mcp MCP server

Copy to your README.md:

Score Badge

nexus-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/rcdelacruz/nexus-mcp'

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