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Kolsetu-Opensource

Elba MCP Server

by Kolsetu-Opensource
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct resource: list_agents for agents, list_voices for voices, upsert_agent for creating/updating agents. No functional overlap.

    Naming Consistency5/5

    All tools use a consistent verb_noun pattern (list_agents, list_voices, upsert_agent) with clear, predictable naming.

    Tool Count5/5

    Three tools is well-scoped for a voice AI agent management server, covering listing agents and voices, and creating/updating agents without unnecessary clutter.

    Completeness4/5

    The set covers the main lifecycle operations (list, create/update) but lacks a delete tool, which may be needed for full management. However, upsert covers creation and updates adequately.

  • Average 4.4/5 across 3 of 3 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It correctly implies a read-only operation and specifies what fields are returned. It does not mention destructive behavior, which is appropriate for a list operation.

    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?

    Two efficient sentences with no wasted words. The key information about purpose, return data, and usage guidance is front-loaded.

    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 no parameters and no output schema, the description adequately explains the tool's output and connection to upsert_agent. It does not mention pagination or edge cases, but for a simple list tool, this is sufficient.

    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?

    There are zero parameters, and schema description coverage is 100%. The description adds meaning beyond the schema by detailing what is returned, meeting the baseline for a no-parameter tool.

    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 'List available voices for Elba voice AI agents' with a specific verb and resource. It distinguishes from siblings list_agents and upsert_agent by focusing on voices.

    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 explicitly advises to use the returned voice ID as the 'voice' parameter in upsert_agent, providing clear context for when to use this tool. It does not state when not to use, but the sibling alternatives imply usage boundaries.

    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 provided, so description carries full burden. It discloses the upsert logic, name-based matching, and default voice behavior. It does not mention auth or error states, but for a simple upsert, this is adequate.

    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?

    Two succinct sentences that convey all essential information without unnecessary words. Perfectly front-loaded.

    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 no output schema and no annotations, the description covers the upsert logic and parameter behavior sufficiently. It lacks mention of return value or error conditions, but these are minor gaps 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%, baseline 3. The description adds valuable context: name is for upsert matching, voice defaults differ on create vs update, and agent_id skips name matching. This goes beyond the schema.

    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 'Create or update a voice AI agent', specifying the verb (create/update) and resource (agent). It distinguishes from siblings list_agents and list_voices, which are listing tools.

    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 explains the upsert behavior: 'If an agent with the given name exists, it will be updated. Otherwise, a new agent is created with sane defaults.' This provides clear context for when to use, but lacks explicit when-not-to-use or alternative recommendations.

    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 burden. It discloses that the tool lists agents and returns specific fields (ID, name, status). It does not mention any destructive behavior or side effects, which is acceptable for a read-only list operation.

    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 sentence with two concise clauses. Every word adds value. It is front-loaded with the action and resource, and efficiently communicates the return information.

    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?

    For a tool with zero parameters and no output schema, the description is complete. It fully explains the tool's purpose and output, leaving no gaps for an AI agent to misunderstand.

    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 input schema has zero parameters, and schema description coverage is 100%. Per guidelines, with 0 parameters the baseline is 4. The description adds no parameter information, which is appropriate since there are none.

    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 verb 'List', the resource 'voice AI agents', the scope 'in your Elba organization', and the returned fields (ID, name, status). This distinguishes it from sibling tools like 'list_voices' which would list voices, and 'upsert_agent' which is for create/update.

    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 implicitly indicates when to use this tool (when you need a list of all agents). While it does not explicitly state when not to use it or name alternatives, the sibling tool names provide differentiation. For such a simple tool, the usage context is clear.

    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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