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Glama

Gong

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: self-aggregating signal, derived from CF analytics-engine, no PII, caching behavior (5min-1h). 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, with a clear opening sentence followed by bullet-pointed use cases. Every sentence adds value; no fluff.

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 low complexity (one optional parameter, no output schema), the description covers return values, caching, and use cases adequately. It provides sufficient detail for an agent to decide when to invoke this 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 only parameter 'window' has an enum in the schema, and the description adds semantic value by explaining that shorter windows highlight current trends while longer windows show steady-state demand. Schema coverage is 100%, but description enhances meaning.

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: returning top tools, top packs, and total call volume over a recent window. It uses specific verbs and resources, and distinguishes from siblings like 'discover_tools' by focusing on what other AI agents are calling.

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 description provides three explicit use cases (e.g., discovering hot data sources, confirming canonical choice). While it doesn't explicitly state when not to use, the context and sibling tool list (e.g., 'discover_tools') offer guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Gong toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

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 Consistency3/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources