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orliesaurus

PulseMCP Server

by orliesaurus
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

67%
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: list_integrations targets integrations, while list_servers targets MCP servers. There is no overlap in functionality or ambiguity between them, making it easy for an agent to select the correct tool based on the desired resource.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'list_' as the prefix, using snake_case throughout. This predictability enhances readability and reduces cognitive load for agents when scanning the tool set.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a PulseMCP Server, which might imply broader functionality. While the tools are clear, the count is too low to cover a comprehensive domain, potentially limiting agent capabilities without additional context.

    Completeness2/5

    The tool set is severely incomplete for a server likely intended to manage integrations and servers. There are no CRUD operations (e.g., create, update, delete) or detailed actions (e.g., get specific integration/server), leaving significant gaps that could cause agent failures in handling lifecycle tasks.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • 0 of 3 community issues answered or closed 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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 provided, the description carries the full burden of behavioral disclosure. While 'List' implies a read operation, it doesn't specify whether this requires authentication, what format the results come in, whether there are rate limits, or how pagination works beyond the offset parameter. The description adds minimal behavioral context 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely concise - a single sentence that communicates the core functionality without any wasted words. It's front-loaded with the main purpose and efficiently mentions the filtering capability.

    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?

    For a list tool with 4 parameters and no output schema, the description is insufficient. It doesn't explain what information is returned about servers, how results are structured, or provide any context about what 'MCP servers' are in this system. With no annotations and no output schema, more descriptive context would be helpful.

    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 all parameters are documented in the schema itself. The description adds no specific parameter semantics beyond mentioning 'optional filtering' which is already implied by the schema. This meets the baseline for high schema coverage where the description doesn't need to compensate.

    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 action ('List') and resource ('MCP servers'), making the tool's purpose immediately understandable. However, it doesn't differentiate from the sibling 'list_integrations' tool, which would require mentioning what distinguishes listing servers from listing integrations.

    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 mentions 'optional filtering' which implies some usage context, but provides no explicit guidance on when to use this tool versus alternatives like 'list_integrations' or how to decide between filtering methods. No when-not-to-use scenarios or prerequisites are mentioned.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. 'List all available integrations' implies a read operation but doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what format the output takes. For a tool with zero annotation coverage, this is insufficient behavioral context.

    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 a single, efficient sentence that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple list operation and front-loads the essential information without unnecessary elaboration.

    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?

    For a zero-parameter list tool with no output schema, the description provides the minimum viable information about what the tool does. However, it lacks important context about authentication requirements, rate limits, pagination behavior, and output format that would help an agent use it correctly. The absence of annotations increases the need for descriptive completeness.

    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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description doesn't need to add parameter information, and the baseline for this scenario is 4. The description appropriately focuses on the tool's purpose rather than redundant parameter details.

    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 verb ('List') and resource ('all available integrations'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'list_servers' - both are list operations but for different resources, so the distinction is implicit rather than explicit.

    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. There's no mention of prerequisites, timing considerations, or comparison to the sibling 'list_servers' tool. The agent must infer usage context from the tool name alone.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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