Gong MCP Server
Gong MCP 服务器
一个模型上下文协议 (MCP) 服务器,提供对 Gong API 的访问,用于检索通话录音和文字记录。该服务器允许 Claude 通过标准化接口与 Gong 数据进行交互。
特征
列出 Gong 呼叫,并可选择日期范围过滤
检索特定通话的详细记录
使用 Gong 的 API 凭证进行安全身份验证
标准化 MCP 接口,可轻松与 Claude 集成
Related MCP server: GCP MCP
先决条件
Node.js 18 或更高版本
Docker(可选,用于容器化部署)
Gong API 凭证(访问密钥和秘密)
安装
本地开发
克隆存储库
安装依赖项:
npm install构建项目:
npm run build
Docker
构建容器:
docker build -t gong-mcp .配置 Claude
打开 Claude 桌面设置
导航至 MCP 服务器部分
添加具有以下配置的新服务器:
{
"command": "docker",
"args": [
"run",
"-it",
"--rm",
"gong-mcp"
],
"env": {
"GONG_ACCESS_KEY": "your_access_key_here",
"GONG_ACCESS_SECRET": "your_access_secret_here"
}
}将占位符凭据替换为
.env文件中的实际 Gong API 凭据
可用工具
列出通话
检索带有可选日期范围过滤的 Gong 呼叫列表。
{
name: "list_calls",
description: "List Gong calls with optional date range filtering. Returns call details including ID, title, start/end times, participants, and duration.",
inputSchema: {
type: "object",
properties: {
fromDateTime: {
type: "string",
description: "Start date/time in ISO format (e.g. 2024-03-01T00:00:00Z)"
},
toDateTime: {
type: "string",
description: "End date/time in ISO format (e.g. 2024-03-31T23:59:59Z)"
}
}
}
}检索成绩单
检索指定呼叫 ID 的详细记录。
{
name: "retrieve_transcripts",
description: "Retrieve transcripts for specified call IDs. Returns detailed transcripts including speaker IDs, topics, and timestamped sentences.",
inputSchema: {
type: "object",
properties: {
callIds: {
type: "array",
items: { type: "string" },
description: "Array of Gong call IDs to retrieve transcripts for"
}
},
required: ["callIds"]
}
}执照
MIT 许可证 - 详情请参阅许可证文件
贡献
分叉存储库
创建你的功能分支(
git checkout -b feature/amazing-feature)提交您的更改(
git commit -m 'Add some amazing feature')推送到分支(
git push origin feature/amazing-feature)打开拉取请求
Available Tools
2 toolslist_callsB
List Gong calls with optional date range filtering. Returns call details including ID, title, start/end times, participants, and duration.
| Name | Required | Description | Default |
|---|---|---|---|
| fromDateTime | No | Start date/time in ISO format (e.g. 2024-03-01T00:00:00Z) | |
| toDateTime | No | End date/time in ISO format (e.g. 2024-03-31T23:59:59Z) |
TDQS
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 states the tool returns call details, but lacks information on permissions, rate limits, pagination, or error handling. This is inadequate for a tool with potential complexity in data retrieval.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose, optional filtering, and return details. It is front-loaded and wastes no words, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description partially compensates by listing return fields, but it lacks details on behavioral aspects like pagination or error handling. For a list tool with two parameters, it is minimally adequate but leaves gaps in operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions optional date range filtering, which aligns with the two parameters in the schema. Since schema description coverage is 100%, the description adds minimal value beyond what the schema already documents, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 a specific verb ('List') and resource ('Gong calls'), and mentions optional date range filtering. It distinguishes from the sibling 'retrieve_transcripts' by focusing on call metadata rather than transcripts, though not explicitly contrasted.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving call details with date filtering, but provides no explicit guidance on when to use this tool versus alternatives like 'retrieve_transcripts' or other potential tools. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
retrieve_transcriptsB
Retrieve transcripts for specified call IDs. Returns detailed transcripts including speaker IDs, topics, and timestamped sentences.
| Name | Required | Description | Default |
|---|---|---|---|
| callIds | Yes | Array of Gong call IDs to retrieve transcripts for |
TDQS
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. It mentions what the tool returns ('detailed transcripts including speaker IDs, topics, and timestamped sentences'), which is helpful. However, it doesn't address critical behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what happens if call IDs are invalid. For a tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured. It consists of two sentences: the first states the purpose and required input, and the second describes the return value. Every word earns its place with no redundancy or fluff. It's appropriately front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (retrieving transcripts for multiple calls), lack of annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and return format but misses important contextual details like error handling, permissions, or limitations. The absence of an output schema means the description should ideally explain the return structure more thoroughly, which it only does at a high level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'callIds' clearly documented as 'Array of Gong call IDs to retrieve transcripts for.' The description doesn't add any meaningful semantic information beyond what the schema already provides. According to the rules, when schema_description_coverage is high (>80%), the baseline score is 3 even with no parameter info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieve transcripts for specified call IDs.' It specifies the verb ('retrieve'), resource ('transcripts'), and scope ('for specified call IDs'). However, it doesn't explicitly distinguish this from the sibling tool 'list_calls' (which likely lists calls rather than retrieving transcripts), so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. It doesn't mention the sibling tool 'list_calls' or any other context for selection. The only implied usage is that you need call IDs to retrieve transcripts, but this is basic parameter information rather than strategic guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
list_calls - First observed
retrieve_transcripts
TDQS
The two tools have clearly distinct purposes: list_calls retrieves metadata about calls, while retrieve_transcripts fetches detailed transcripts for specific calls. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool.
Both tools follow a consistent verb_noun pattern (list_calls and retrieve_transcripts), using snake_case and clear action verbs. The naming is predictable and readable across the set.
With only two tools, the server feels thin for a Gong MCP server, which typically handles call-related operations. This limited set may not cover essential workflows like creating, updating, or deleting calls, or accessing other call metadata beyond listing and transcripts.
Inferring the domain as call management, there are significant gaps: no tools for creating, updating, or deleting calls, and missing operations like searching calls, accessing call recordings, or managing participants. This incomplete surface will likely cause agent failures in broader workflows.
Maintenance
Resources
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