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Glama

Gong

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations indicate idempotent and non-destructive. Description adds scoping by identifier, persistence for authenticated users, 24-hour retention for anonymous. No contradictions. Could mention overwrite behavior but still strong.

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?

Four sentences efficiently packed: purpose, usage scenario, scoping/persistence, pairing. No fluff, front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple write-only tool with two params, description covers all necessary context: purpose, when, scoping, retention, and companion tools. No output schema needed.

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?

Schema covers both params with descriptions (100% coverage). Description adds naming conventions and examples (e.g., 'subject_property'). Adds value beyond schema. Baseline 3, plus 1 for 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?

Description uses clear verb 'Save' and resource 'data as key-value pairs'. Distinguishes from siblings recall and forget, and specifies scope across sessions.

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?

Explicitly states when to use: when discovering something worth carrying forward to avoid repeated lookups. Notes pairing with recall/forget. No alternative tools given but sufficient for single-purpose tool.

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

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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