Skip to main content
Glama
vivekhaldar

Emacs MCP Server

by vivekhaldar
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one executes Emacs Lisp code, while the other generates it. There is no overlap or ambiguity between these functions, making it easy for an agent to select the appropriate tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'execute_emacs_lisp_code' and 'generate_emacs_lisp_code'. The naming is uniform, using snake_case and clear verbs that accurately describe each tool's action.

    Tool Count2/5

    With only two tools, the server feels thin for an Emacs integration, as it lacks operations like editing files, managing buffers, or handling common Emacs commands. This minimal set may limit agents in performing typical Emacs-related tasks.

    Completeness2/5

    The tool surface is severely incomplete for an Emacs server, covering only code execution and generation. It misses essential CRUD/lifecycle operations such as opening files, saving changes, or interacting with Emacs modes, which are core to the domain.

  • Average 2.8/5 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior1/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only states what the tool does without any details on behavioral traits such as whether it's a read-only or destructive operation, authentication needs, rate limits, or what the output looks like. This makes it inadequate for informing the agent about how the tool behaves beyond its basic function.

    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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it efficient and easy to parse. This is an example of optimal conciseness for a simple tool.

    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 tool's complexity (code generation), lack of annotations, no output schema, and low parameter documentation, the description is incomplete. It doesn't provide enough context for the agent to understand how to use the tool effectively, such as what the generated code looks like, any limitations, or how it differs from executing code. This gap makes it insufficient for a tool with these characteristics.

    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 1 parameter with 0% description coverage, meaning the schema provides no details about the 'purpose' parameter. The description adds minimal semantics by implying the parameter is for specifying the purpose of the code generation, but it doesn't explain what format or content is expected, examples, or constraints. This leaves the parameter poorly documented, failing to compensate for the low schema coverage.

    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 the tool's purpose: 'Generate Emacs Lisp code for the given purpose.' It specifies the verb ('Generate'), resource ('Emacs Lisp code'), and input ('given purpose'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'execute_emacs_lisp_code', which is why it doesn't score a 5.

    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?

    The description provides no guidance on when to use this tool versus its sibling 'execute_emacs_lisp_code'. It doesn't mention any prerequisites, alternatives, or exclusions, leaving the agent to infer usage based on the tool name alone. This lack of explicit guidance reduces its effectiveness in helping the agent select the correct tool.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions sending code to the Emacs process and returning results, but lacks details on permissions, side effects, error handling, or execution environment. This is a significant gap for a code execution tool.

    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, efficient sentence that front-loads the core functionality with zero waste. Every word earns its place, making it appropriately sized and well-structured.

    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 complexity of code execution, lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover return values, error cases, or execution nuances, leaving the agent with insufficient context for safe and effective use.

    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 description coverage is 0%, so the description must compensate. It implies the 'code' parameter is Emacs Lisp code to execute, adding basic meaning beyond the schema. However, it doesn't detail syntax, constraints, or examples, leaving gaps in parameter understanding.

    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 the action ('Execute Emacs Lisp code') and the resource ('Emacs process'), specifying that it sends code and returns results. However, it doesn't explicitly differentiate from its sibling tool 'generate_emacs_lisp_code', which likely generates code rather than executing it.

    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, such as its sibling 'generate_emacs_lisp_code'. The description implies usage for executing code but lacks explicit context, prerequisites, or exclusions.

    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

emacs-mcp-server MCP server

Copy to your README.md:

Score Badge

emacs-mcp-server 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/vivekhaldar/emacs-mcp-server'

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