@striderlabs/mcp-discord

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
The tools are mostly distinct: get_servers, get_channels, get_messages, send_message, get_dms, and search_messages each target a different resource. There is minor overlap between get_messages and search_messages, and get_dms could be confused with channel message retrieval, but descriptions clarify the intended use.
Naming Consistency4/5
Most tools follow a consistent get_* pattern, with send_message and search_messages being logical action-based exceptions. The use of 'dms' instead of 'direct_messages' is a minor inconsistency but does not hurt readability.
Tool Count5/5
Six tools is a well-scoped set for a Discord MCP server covering server discovery, channel listing, message reading, searching, sending, and DM access. No tool feels redundant or unnecessary.
Completeness4/5
The core read/send workflows for Discord are covered: list servers, list channels, fetch messages, search messages, send messages, and read DMs. Minor gaps exist such as no direct message sending or message editing/deletion, but the main use cases are supported.
Average 3.4/5 across 6 of 6 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
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.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it only says 'get recent messages.' It does not disclose ordering, pagination behavior, channel access requirements, rate limits, or what the response contains.
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 one short, front-loaded sentence with no filler or redundancy. It efficiently states the core purpose without repeating the tool name verbatim.
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?
Without annotations or an output schema, the description leaves out important context about return values, message ordering, pagination implications, and how this tool relates to search_messages or get_dms. An agent has enough to attempt the call but not enough to use it confidently in varied scenarios.
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 covers all three parameters with descriptions and defaults, so the schema carries the parameter-semantics load. The description adds no additional meaning beyond what the schema already provides, which matches the baseline for high 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 names a specific resource (Discord channel) and action (get recent messages), which clearly distinguishes it from send_message and get_servers. It does not explicitly contrast with search_messages, but 'recent messages' gives a usable scope.
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 about when to use get_messages versus search_messages or get_dms. The word 'recent' implies a usage context, but there are no exclusions, alternatives, or conditions such as pagination scenarios.
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 only says 'recent', which hints at ordering/limiting but does not clarify return format, pagination, whether conversations are sorted, whether message content is included, or side effects (e.g., marking as read). An agent cannot anticipate the behavior beyond a basic read operation.
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 efficient sentence that front-loads the core purpose. It contains no fluff or repetition. It could arguably include more context, but as far as conciseness and structure, it earns its place.
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?
The tool has no output schema and minimal annotations, so the description should compensate by explaining what a returned conversation looks like or any relevant edge cases. It does not. An agent knows it gets recent DM conversations and can pass an optional limit, but has no idea of the response shape, ordering, or whether it includes metadata or content. This is a meaningful gap for a simple 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 input schema already fully documents the only parameter (limit) with type and default value, so schema coverage is 100%. The description does not mention or explain any parameters, but the baseline of 3 applies because the schema does the heavy lifting. No additional semantic meaning is added.
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 ('Get'), a resource ('direct message conversations'), and a scope ('recent', 'bot has had'). The term 'conversations' distinguishes it from sibling get_messages (which likely returns individual messages) and other sibling tools like get_servers/get_channels. Purpose is unambiguous.
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 alternatives such as search_messages or get_messages. There is no mention of which use cases this serves or when a sibling would be more appropriate. Usage context must be inferred entirely from the tool name and description.
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 are provided, so the description must carry the full behavioral burden. It only states that a message is sent and does not disclose side effects, permission requirements, rate limits, delivery guarantees, or return behavior, which are important for a mutation 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 concise sentence with no filler. It is front-loaded with the action and resource, making it easy to scan and understand.
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 no annotations, no output schema, and a write operation, the description is thin. It tells the agent what the tool does but not when to prefer it, what to expect after invocation, or what constraints apply, so context is incomplete.
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?
Schema description coverage is 100%, so the parameters are already well documented. The description adds no extra detail about parameter semantics, but also does not need to compensate much 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 uses a specific verb ('Send') and resource ('a message to a Discord channel'), making the tool's purpose immediately clear. It also distinguishes this from the sibling read/search tools such as get_messages, get_dms, and search_messages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied by the word 'send' rather than explicitly contrasted with the get/search siblings. There is no stated when-to-use or when-not-to-use guidance, but the description makes it understandable that this is the tool for posting a message.
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, the description carries the full burden for behavioral disclosure. It does not mention the channel_type default of 'text', which conflicts with the claim of listing 'all channels'. This is a meaningful gap because a caller relying on the description may expect voice/category channels by default.
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 immediately conveys the tool's purpose. There is no wasted wording, and the main action and target resource are front-loaded.
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 tool is simple, but the description omits the fact that channel_type defaults to 'text', making 'all channels' potentially misleading. Without an output schema, a bit more detail about the default filtering behavior would make the description fully self-sufficient.
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?
Schema description coverage is 100%, so the schema already documents both parameters and the enum values. The description adds no additional semantic detail beyond what the schema provides, so the 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 names a specific verb ('List'), resource ('all channels'), and scope ('in a Discord server'). It clearly distinguishes get_channels from sibling tools like get_messages, send_message, get_dms, and get_servers, which target different resources.
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 intended use case is clear: retrieve channels for a server. It does not explicitly say when not to use this tool, but the resource-based separation from sibling tools makes the appropriate context unambiguous.
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?
With no annotations, the description carries the behavioral burden. 'Search' implies a read-only operation, but it does not explicitly confirm that, nor does it mention access restrictions, match behavior, or what happens when no matches are found.
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, front-loaded sentence with no filler. Every word contributes to the core purpose, making it easy for an agent to parse quickly.
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 core purpose and required parameters are clear enough to invoke the tool, but with no annotations and no output schema, the return value and behavioral details are left implicit. The optional channel_id filtering is only discoverable in the schema.
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?
Since the input schema has 100% coverage for its four parameters, the baseline is 3. The description adds no meaningful parameter detail beyond evoking server_id and query with 'server's channels' and 'specific text.'
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 uses a specific verb ('Search'), identifies the resource ('messages'), and clearly scopes the action to 'a server's channels' and 'specific text.' This distinguishes it from siblings like get_messages and send_message.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage context is implied: use when you need messages containing specific text in a server. However, there is no explicit guidance on when not to use it or which sibling tool to prefer in other situations.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does communicate scope by limiting results to servers the bot is a member of, and 'List' implies a read-only operation, but it does not address return format, pagination, rate limits, or potential side effects. This is adequate but minimal.
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, tight sentence with no filler. The core action and scope are front-loaded, and every word contributes to understanding the tool's purpose.
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 zero-parameter, no-output-schema tool, the description is nearly complete: it names the resource, the scope, and the operation. It could mention what the returned server objects contain, but that omission does not block correct invocation for a simple list-all tool.
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 tool has zero parameters and the schema documents 100% coverage by declaring an empty properties object. The baseline for zero-parameter tools is 4 because there is no parameter-level meaning for the description to add.
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 is specific and unambiguous: it names the exact action ('List all') and the exact resource ('Discord servers (guilds) the bot is a member of'). It is clearly distinct from siblings like get_messages, get_channels, and get_dms, which operate on different Discord entities.
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 states what the tool does but gives no explicit guidance about when to choose it over alternatives, nor any conditions for use. Sibling tools are not mentioned, and there are no exclusions or contextual hints beyond the resource itself.
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

Copy to your README.md:

[![mcp-discord MCP server](https://glama.ai/mcp/servers/markswendsen-code/mcp-discord/badges/card.svg)](https://glama.ai/mcp/servers/markswendsen-code/mcp-discord)

Score Badge

Copy to your README.md:

[![mcp-discord MCP server](https://glama.ai/mcp/servers/markswendsen-code/mcp-discord/badges/score.svg)](https://glama.ai/mcp/servers/markswendsen-code/mcp-discord)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/markswendsen-code/mcp-discord'

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