Skip to main content
Glama
abryfs

willitsend

by abryfs
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool in the server, there is no possibility of confusing it with another tool. The purpose is unambiguous by default.

    Naming Consistency5/5

    The single tool name 'preflight_message' follows a clear verb_noun snake_case convention. With only one tool, there is no inconsistency to detect.

    Tool Count3/5

    A single tool feels thin for a typical server, but the purpose here is narrowly scoped to preflight message linting, so the count is borderline but not unreasonable.

    Completeness4/5

    The preflight_message tool covers the full linting lifecycle: it analyzes drafts, returns verdicts and actionable findings, and provides delivery-channel estimates. Minor gaps exist (e.g., no way to retrieve a rule set or manage campaign types), but the core domain is well covered.

  • Average 4.8/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed 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 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.

  • 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

  • Behavior5/5

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

    Annotations (readOnlyHint=true, idempotentHint=true) are reinforced and expanded by the description: 'sends nothing itself, makes no delivery attempt, and has no side effects.' It also details the verdict types, the loop behavior, and that body text is used only for local analysis. There is no contradiction between the description and annotations.

    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 front-loaded with the essential safety and usage statement. It covers return values, rules, and a verification loop. Some redundancy exists (e.g., 'sends nothing itself' and 'no side effects' are repeated), but the complexity of the tool justifies the length.

    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?

    The description fully covers what the tool does, what it returns (verdicts, findings, trace), how to use it iteratively, and compliance use cases. An output schema is present, so return-value details are already structured. The description also handles edge cases like needs_context and media carousels.

    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%, so each parameter is individually described. The tool description adds cross-parameter context: how media_urls triggers carousel warnings, how campaign_type affects the quota illustration, and how is_first_message_to_contact influences verdicts. This goes beyond the schema 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 opens with a specific verb and resource: 'Analyzes (lints) a draft SMS/iMessage BEFORE you send it.' It clearly distinguishes itself from send_message by stating it sends nothing and makes no delivery attempt. The purpose is unmistakable and well-aligned with the title.

    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?

    Explicit guidance is given: 'Call this first, before send_message or any other messaging tool, on every outbound draft.' It also tells the user what not to do: 'Never treat needs_context as permission to send; supply the missing context and re-check.' This covers both when to use and when not to proceed.

    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

willitsend MCP server

Copy to your README.md:

Score Badge

willitsend 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/abryfs/willitsend'

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