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jiridudekusy

signal-cli-mcp

by jiridudekusy
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: sending to an individual, sending to a group, and creating a group. The recipient type and action are explicit in the names, leaving no ambiguity.

    Naming Consistency5/5

    All tools follow a consistent 'propose[Action]' pattern with clear noun phrases (SignalMessage, SignalGroupMessage, SignalGroupCreation). The naming is uniform and predictable.

    Tool Count3/5

    At 3 tools, the count is at the low end of the typical range. While each tool earns its place, the server feels minimal and could benefit from a few more operations to feel well-rounded.

    Completeness2/5

    The server covers sending messages and creating groups but lacks any receive, list, or management capabilities. For a Signal client, this is a significant gap that would prevent agents from performing common workflows.

  • 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
    • 5 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 passing

  • 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 provided, the description carries the full burden of behavioral disclosure. It reveals the key non-obvious traits: the call blocks for human approval in a local GUI, may take several minutes, and nothing is sent without approval. This is valuable transparency beyond what the schema provides.

    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 three sentences, front-loaded with the core purpose, and the rest of the sentences provide essential behavioral warnings. No fluff or repetition.

    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?

    For a tool with no output schema and moderate complexity, the description adequately covers the unique context: the approval workflow, the potential long wait, and the safety guarantee. It explains what the agent should expect when invoking the tool.

    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 schema already covers all parameters with descriptions (name, reason, members including format expectations). The description adds no additional parameter-level detail, so the baseline score of 3 is appropriate given the high schema coverage.

    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 states a specific verb and resource: 'Proposes creating a new Signal group.' This clearly distinguishes it from sibling tools like proposeSignalMessage and proposeSignalGroupMessage, which focus on messages rather than group creation.

    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 clarifies the context of use by noting that human approval is awaited and that the call may take minutes. It does not explicitly mention when not to use it or name alternatives, but the context is clear enough for the agent to decide when this tool is appropriate.

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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden. It explicitly discloses the approval requirement, the potential multi-minute wait, that the wait is not an error, and that nothing is sent without approval. This is genuinely helpful and goes beyond what structured data could convey.

    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 two sentences, both essential and front-loaded with the most important warnings (approval, wait time, not an error). There is no redundant or filler content.

    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?

    The description effectively covers the main contextual challenges for this tool: it is a proposal requiring human approval, may take minutes, and should not be mistaken for an error. It does not explain what happens after approval or the response format, but with simple parameters and no output schema, the key risks are addressed.

    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 already provides descriptions for all 3 parameters (100% coverage), so the tool description does not need to repeat them. It adds no extra parameter-specific context beyond what the schema already contains, warranting the baseline score.

    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 states a specific verb 'Proposes sending' with a clear resource 'Signal message to a group'. This distinguishes it from sibling tools proposeSignalMessage (likely for direct messages) and proposeSignalGroupCreation (creating a group).

    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 clearly communicates that the call awaits human approval and may take several minutes, setting proper expectations. It does not explicitly mention alternatives or when not to use the tool, but the scope is clear from the name and description.

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

  • Behavior5/5

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

    With no annotations, the description carries full responsibility for behavioral disclosure. It explicitly warns that the call awaits human approval in a local GUI, may take several minutes, is not an error, and that nothing is sent without approval. This fully covers the tool's non-obvious behavior.

    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 two concise sentences that front-load the key purpose and the critical behavioral warning. Every sentence adds necessary information, and there is no wasted text.

    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 having no output schema or annotations, the description covers the essential context: purpose, recipient scope, approval workflow, duration warning, and the guarantee that nothing is sent without approval. Parameter details are fully handled by the schema, so no significant gaps remain.

    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 provides 100% coverage with meaningful descriptions for all three parameters, including recipient resolution and failure conditions. The description itself adds no parameter-level details, but since the schema is thorough, a baseline of 3 is appropriate.

    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 a specific action ('Proposes sending a Signal message to an individual') and differentiates it from sibling tools that target groups or group creation. The verb 'propose' and resource 'individual message' are unambiguous.

    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 gives clear context by specifying 'to an individual,' which implies this tool is for person-to-person messages rather than group messages. It does not explicitly name alternatives or when-not-to-use, but the sibling tool names and the individual/group contrast provide enough guidance.

    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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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.

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