gong_search_callsSearch calls by keyword or phrase (e.g., 'pricing', 'objection', 'budget'). Returns matching calls ranked by relevance.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | Keyword to search for in calls |
| 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 search results ranked by relevance to keyword query",
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "keyword": "pricing"
+ },
+ {
+ "keyword": "budget objection"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. Description adds that results are ranked by relevance, but no further behavioral context.
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?
Single sentence that is front-loaded with the action and includes an example. No wasted words.
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?
Simple tool with one parameter; description covers input and output (returns calls ranked by relevance). Output schema handles details.
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 high, but description adds examples of keywords ('pricing', 'objection', 'budget'), which aids understanding beyond the schema's parameter 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?
Clearly states the tool searches calls by keyword or phrase, with specific examples. Distinguishes from sibling Gong tools like 'gong_get_call' and 'gong_list_calls'.
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?
Implies use for keyword search but provides no explicit guidance on when to use versus alternatives or when not to use.
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.