Zoom MCP Server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. get_room_details targets a single room, get_zoom_rooms retrieves multiple rooms with optional filtering, get_zoom_sites lists locations, resolve_location is a debug tool for location resolution, and test_zoom_connection validates authentication. The descriptions explicitly differentiate use cases, preventing misselection.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case naming (e.g., get_room_details, get_zoom_rooms, get_zoom_sites, resolve_location, test_zoom_connection). The verbs are descriptive and aligned with their functions, making the set predictable and readable.
Tool Count5/5
With 5 tools, the server is well-scoped for managing Zoom rooms and locations. Each tool earns its place by covering essential operations: authentication testing, location resolution, site listing, room listing with filtering, and detailed room queries. This count is appropriate for the domain without being too thin or heavy.
Completeness4/5
The tool set covers core workflows for Zoom room management, including authentication, location hierarchy, and room queries. However, there are minor gaps such as the lack of update or delete operations for rooms (e.g., modifying room settings or removing rooms), which agents might need to work around. The surface is largely complete for monitoring and querying purposes.
Average 4.5/5 across 5 of 5 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

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 mentions the tool returns 'full room configuration, settings, and recent events', which gives some insight into output behavior. However, it lacks details on error handling, authentication requirements, rate limits, or whether the operation is idempotent—leaving gaps for a tool with no annotation coverage.
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 well-structured and front-loaded with the core purpose, followed by usage guidelines and examples. Every sentence adds value without redundancy, and the bullet-point-like examples enhance readability without unnecessary verbosity.
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 simple read-only tool with 1 parameter and no output schema, the description is largely complete—it covers purpose, usage, and output scope. However, without annotations or an output schema, it could benefit from more detail on error cases or response structure, slightly limiting completeness for an agent.
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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It clarifies that 'room_id' refers to 'a specific room ID' and implies it's required for fetching details, adding meaningful context beyond the bare schema. However, it doesn't specify the format or constraints of the ID (e.g., numeric vs. alphanumeric).
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 specific action ('Get detailed information') and resource ('about a specific Zoom room'), distinguishing it from sibling tools like get_zoom_rooms (which likely lists multiple rooms) and get_zoom_sites (which focuses on sites rather than individual rooms). The purpose is unambiguous and well-articulated.
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?
The description explicitly states when to use this tool ('when you have a specific room ID and need complete details about that single room') and provides concrete examples ('Tell me about room ABC123', 'What are the details of this specific room?'), making it clear this is for single-room queries rather than bulk operations or other contexts handled by siblings.
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 describes what the tool returns ('hierarchy and aliases') and its read-only nature is implied by 'Get', but it lacks details on potential limitations like pagination, rate limits, or error handling. The description adds some context but does not fully compensate for the absence of annotations.
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 well-structured and front-loaded, starting with the core purpose, followed by usage guidelines and examples. Each sentence adds value without redundancy, and the bullet-point style for examples enhances readability without wasting space.
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?
Given the tool's complexity (simple read operation with no parameters) and the absence of annotations and output schema, the description is largely complete. It explains what the tool does, when to use it, and provides examples, though it could benefit from more behavioral details like response format or constraints to fully compensate for the lack of structured data.
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 input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately does not discuss parameters, focusing instead on the tool's output and usage. This meets the baseline for tools with no parameters, as it avoids unnecessary details.
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 tool's purpose with specific verbs ('Get all Zoom sites/locations') and resources ('sites/locations'), and distinguishes it from siblings by specifying it shows 'hierarchy and aliases' rather than room details or location resolution. It explicitly answers questions like 'What locations do we have?' which reinforces its distinct role.
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?
The description provides explicit guidance on when to use this tool ('USE THIS to understand available locations before using location-specific queries') and includes alternative scenarios ('Perfect for: ...') that clarify its application. It differentiates from siblings by focusing on site overview rather than specific details or resolution tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 effectively describes key traits: it's a debug tool that simulates resolution without actual fetching, shows matching aliases and found locations, and indicates API call implications. However, it doesn't specify error handling, rate limits, or authentication needs, leaving some behavioral aspects uncovered.
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 well-structured and front-loaded, starting with the core purpose. Each sentence adds value: the first states the tool's function, the second explains its utility, and the third gives concrete use cases. There is no redundant or wasted text, making it highly efficient.
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?
Given the tool's moderate complexity (debugging/resolution without annotations or output schema), the description is mostly complete. It covers purpose, usage, and parameter intent effectively. However, it lacks details on return format (e.g., structure of resolved data) and error scenarios, which would enhance completeness for a debug 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 input schema has 0% description coverage, so the description must compensate. It adds meaningful context for the 'location_query' parameter through examples like 'DEN1' and 'Floor 1', clarifying it's a string for testing location matching. While it doesn't detail syntax constraints, it provides sufficient semantic understanding beyond the bare 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 clearly states the tool's purpose: 'Test how location queries get resolved without fetching rooms.' It specifies the verb ('Test') and resource ('location queries'), and distinguishes it from sibling tools by emphasizing it's for debugging/resolution rather than actual data fetching. The examples ('How would "DEN1" be resolved?') reinforce this specific scope.
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?
The description provides explicit guidance on when to use this tool: 'USE THIS to understand what locations will be searched before running expensive room queries.' It contrasts with siblings by positioning it as a preparatory/debugging step to avoid costly operations, and includes perfect-use-case examples that clarify its role versus tools like get_room_details or get_zoom_rooms.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 clearly explains what the tool returns ('authentication status, account info, and token cache status') and its purpose in troubleshooting. While it doesn't mention rate limits or error behaviors, it provides sufficient context for a diagnostic 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 efficiently structured with clear sections: purpose statement, usage instruction, return values, and use case examples. Every sentence adds value without redundancy, and the information is front-loaded with the most important guidance first.
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 diagnostic tool with no annotations and no output schema, the description provides comprehensive context about what the tool does, when to use it, and what information it returns. The only minor gap is the lack of explicit output format details, but the described return values give sufficient semantic understanding.
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 with 100% schema description coverage, so the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the tool's purpose and usage context.
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 tool's purpose with specific verbs ('test', 'validate') and resources ('Zoom API connection', 'authentication credentials'). It distinguishes itself from sibling tools by focusing on connection testing rather than data retrieval or resolution operations.
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?
The description provides explicit guidance on when to use this tool: 'USE THIS FIRST to verify your Zoom credentials are working before using other tools.' It also gives concrete examples of appropriate use cases: 'Perfect for: "Is my connection working?", "Test Zoom authentication", troubleshooting setup.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 effectively describes key behavioral traits: the tool's efficiency (single API call without location_query vs. multiple with it), smart location resolution, and performance implications. However, it lacks details on error handling, rate limits, or authentication needs, which are relevant for a tool with API calls.
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 appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first states the purpose, the next two provide important usage guidelines with efficiency tips, and the examples reinforce the guidance. There is no wasted text, and the structure is clear and logical.
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?
Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is largely complete. It covers purpose, usage, parameter semantics, and behavioral aspects like efficiency. However, it lacks details on output format (what data is returned) and potential errors, which would enhance completeness for an API-based tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must fully compensate. It adds significant meaning beyond the schema by explaining the semantics of location_query: when to use it (for specific location filtering), when to omit it (for company-wide queries), and examples (e.g., 'SF1', 'DEN1'). This clarifies the parameter's purpose and usage context effectively.
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 tool's purpose: 'Get Zoom rooms with optional location filtering.' It specifies the verb ('Get'), resource ('Zoom rooms'), and scope ('with optional location filtering'), distinguishing it from siblings like get_room_details (specific room details) and get_zoom_sites (sites rather than rooms).
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?
The description provides explicit guidance on when to use this tool versus alternatives, including detailed scenarios. It specifies when to omit location_query (for company-wide queries) and when to use it (for location-specific filtering), and mentions efficiency trade-offs (single vs. multiple API calls), which helps differentiate from siblings like resolve_location for location resolution.
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:

[![zoom-mcp MCP server](https://glama.ai/mcp/servers/chadkunsman/zoom-mcp/badges/card.svg)](https://glama.ai/mcp/servers/chadkunsman/zoom-mcp)

Score Badge

Copy to your README.md:

[![zoom-mcp MCP server](https://glama.ai/mcp/servers/chadkunsman/zoom-mcp/badges/score.svg)](https://glama.ai/mcp/servers/chadkunsman/zoom-mcp)

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/chadkunsman/zoom-mcp'

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