gong_get_transcriptRetrieve the full conversation transcript for a call with speaker names, timestamps, and dialogue. Use after gong_get_call to analyze specific conversations.
| Name | Required | Description | Default |
|---|---|---|---|
| callId | Yes | The Gong call ID |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Response from Gong API with call transcript containing speaker dialogue and timestamps",
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "callId": "8145046661234567890"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint as false, indicating a safe, idempotent read operation. The description adds value by specifying the content of the transcript (speaker names, timestamps, dialogue), which complements the annotations. No contradictions.
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 consists of two sentences: one defining the function and result, the other providing usage guidance. No extraneous information. Every sentence is purposeful and earns its place.
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 simplicity (one required parameter, full schema coverage, rich annotations, and presence of an output schema), the description sufficiently covers what an agent needs: what the tool does, what it returns, and when to use it. The mention of using after gong_get_call provides workflow 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?
Schema coverage is 100%; the single parameter callId has a schema description 'The Gong call ID'. The tool description does not add any additional semantic meaning to the parameter beyond what is already in the schema, so baseline score of 3 is appropriate.
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 purpose: retrieve the full conversation transcript with speaker names, timestamps, and dialogue. It differentiates from sibling tools like gong_get_call and gong_list_calls by specifying the exact resource and the context of use after gong_get_call.
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 explicit usage context: 'Use after gong_get_call to analyze specific conversations.' This guides the agent on the step-by-step workflow. It doesn't explicitly state when not to use it or list alternatives, but the given context is sufficient for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes due to detailed descriptions, but there is potential confusion among the many Polymarket and pipeworx-related tools. The Gong-specific tools are clearly separated.
Naming conventions are mixed: some use snake_case, others camelCase, and there is inconsistency between groups (e.g., gong_* vs. polymarket_*). However, within each subgroup, naming is consistent.
35 tools is high for coherence. The server covers multiple domains (Gong calls, data research, betting), leading to an overloaded toolset that could be streamlined into fewer, more general tools.
The toolset is comprehensive for its intended use cases, covering Gong call management, a wide array of data lookups, and Polymarket betting analysis. Minor gaps exist, such as limited CRM features beyond calls.