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SpiritHerb

MCP Research Router

by SpiritHerb
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.14

  • Disambiguation5/5

    The two tools have completely distinct purposes: one lists/discover tools, the other executes them. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun snake_case pattern: execute_tools and get_tool_list.

    Tool Count3/5

    Only two tools, which is minimal but functional for a meta-server that primarily discovers and executes tools from other servers.

    Completeness5/5

    Covers the essential functionalities of a research router: tool discovery and execution. No obvious gaps.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • 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"
  ]
}

    Then . Browse examples.

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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

  • Behavior4/5

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

    No annotations exist, so description carries full burden. It discloses key behaviors: only for remote tools, batch is parallel with 3-5x speedup, sequential for dependent tools. Does not cover error handling or synchronization details, but sufficiently explains core traits.

    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 well-organized with headers, bullet points, and clear sections. It is thorough but each sentence contributes useful information. A slight reduction due to length, but efficient for the content.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite no output schema, the description provides complete context: purpose, constraints, usage methods, performance benefits, and scenario recommendations. It distinguishes from the sibling and covers critical usage aspects comprehensively.

    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% and descriptions are already good. The description adds value by explaining the naming format 'server_name-tool_name' and clarifying mutual exclusivity between 'tool_name' and 'tools' parameters, beyond what schema states.

    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 'Execute tools obtained from remote MCP servers' with a specific verb and resource. It distinguishes from sibling tool 'get_tool_list' by focusing on execution rather than listing.

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

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides explicit guidance on when to use this tool vs. direct invocation, based on the naming convention 'server_name-tool_name'. Also details usage modes for single, batch parallel, or sequential execution with clear applicability to dependency scenarios.

    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?

    No annotations are provided, so the description carries the full burden. It discloses the tool's behavior: it returns lists or recommendations, includes constraints on JSON format, exact tool names, and parameter matching. It also mentions performance benefits of batch execution. However, it does not explicitly state side effects (none expected for a read tool) or authentication needs, but that is acceptable for an informational tool.

    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 long but well-organized with headings, bullet points, and examples. Every section adds necessary information. It could be slightly more concise by merging some redundant explanations, but the structure aids readability and completeness. The front-loaded summary of modes is effective.

    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 complexity (two modes, four parameters, custom prompt modes) and lack of output schema, the description adequately covers return values for both modes, usage constraints, and integration with 'execute_tools'. It is thorough enough for an agent to use the tool correctly without additional context.

    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?

    Schema coverage is 100%, and the description adds substantial value beyond the schema. It explains optionality, default values, and the custom prompt mode feature (e.g., creating prompt files in 'prompts/' folder). It also provides concrete examples demonstrating different parameter combinations, which helps the agent understand usage patterns.

    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 dual purpose: listing all available tools or intelligently recommending tools based on a query. It distinguishes two modes and specifies the resource ('工具列表'). The verb 'get' and resource are explicit, and it differentiates from the sibling tool 'execute_tools' by stating that execution should be done via that tool.

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

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit guidance on when to use each mode: mode 1 for simply listing tools, mode 2 for research/query scenarios. It also instructs to use 'execute_tools' for execution, recommends batch mode for multiple tools, and warns against sequential execution without dependencies. This leaves no ambiguity about when to invoke this tool versus alternatives.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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