Skip to main content
Glama
EtienneLescot

cc-delegate

by EtienneLescot
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: run_dev_task initiates a task, get_task_status checks progress, and fetch_task_result retrieves the final output. No overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (run_dev_task, get_task_status, fetch_task_result), making them predictable.

    Tool Count5/5

    Three tools is exactly right for a focused task life cycle: start, poll, and fetch result. Not too few or too many.

    Completeness5/5

    The tool set covers the full life cycle of an autonomous coding task: creation, status polling, and result retrieval. No obvious gaps for the stated purpose.

  • Average 3.2/5 across 3 of 3 tools scored. Lowest: 2.6/5.

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

    • No community issues in the last 6 months
    • 35 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.

  • This repository includes a glama.json configuration file.

  • 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

  • Behavior2/5

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

    With no annotations, the description carries the full burden of transparency. It fails to disclose whether the operation is read-only, idempotent, or has side effects. No mention of authorization requirements or rate limits, which is critical for a polling tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    The description is a single sentence, which is concise, but may be too brief for adequate understanding. It lacks structure such as context, examples, or edge cases, though it does front-load the key outputs.

    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 lack of output schema and annotations, the description should provide more context about the response format, error handling, or typical usage patterns. It is incomplete for an AI agent to reliably invoke this tool.

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

    Parameters2/5

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

    The input schema has 0% description coverage, and the description adds no meaning to the task_id parameter beyond its name. The agent is left to guess where to obtain the task_id or its expected format.

    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 it returns status, progress, cost, and turns for a delegated task, indicating a polling function. However, it does not differentiate from sibling tools like fetch_task_result or run_dev_task, which may have overlapping purposes.

    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?

    No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, typical use cases, or when not to use it, leaving the agent without decision-making context.

    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?

    No annotations provided, so description carries full burden. It lists what is returned on success but does not disclose behavior for non-succeeded tasks or invalid task_ids. No mention of error handling, authentication requirements, or side effects.

    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 short sentence with no wasted words. However, it could be more structured by front-loading the primary action and then listing return fields.

    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?

    The description lists the return fields (summary, patch path, etc.) which is helpful. But it is incomplete: no mention of error conditions, and no output schema exists. For a simple tool with one parameter, it is minimally adequate but could be improved.

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

    Parameters1/5

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

    Schema coverage is 0% and description does not mention the task_id parameter. There is no explanation of what task_id is, how to obtain it, or its required format. The description adds no value beyond the schema for this parameter.

    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 'fetch' and resource 'task result', and specifies the condition 'when status is succeeded'. It distinguishes from siblings like get_task_status (which just gets status) and run_dev_task (which runs a task).

    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 implies usage when a task has succeeded, but does not explicitly exclude use when task is pending, failed, or when to use alternatives. Provides good context but lacks explicit when-not-to-use guidance.

    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?

    Mentions autonomous worker and isolated git worktree, indicating it is a heavy operation. However, with no annotations, details about side effects, permissions, or errors are missing. Adequate but not comprehensive.

    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?

    Single sentence that efficiently conveys core action, asynchronous nature, and follow-up workflow. No waste.

    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?

    With 8 parameters, low schema coverage, no output schema, and no annotations, the description lacks sufficient detail on parameter behavior, return value, and error handling. Provides pattern but not completeness.

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

    Parameters2/5

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

    Schema coverage is low (25%). Description does not explain parameters beyond those already in schema (spec, repo_path). Other params like max_turns, timeout_ms are undocumented, failing to compensate for the coverage gap.

    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?

    Description clearly states it starts an autonomous coding worker, returns a task_id, and directs to poll with sibling tools. Specific verb and resource, differentiates from siblings by mentioning polling workflow.

    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?

    Describes the asynchronous usage pattern (poll with get_task_status, then fetch_task_result). Provides clear context for when to use, but does not explicitly state when-not or alternatives beyond siblings.

    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

cc-delegate MCP server

Copy to your README.md:

Score Badge

cc-delegate 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/EtienneLescot/cc-delegate'

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