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Zambo AI Tools

by dev.zambo

git_changelog

Turn raw git commits or a diff into a polished, professional changelog — formatted for GitHub releases, a CHANGELOG.md, or a product update. Returns grouped changes (features, fixes, breaking changes) with clean descriptions. Use when user pastes commits, says 'write a changelog', 'release notes for', 'what changed in this diff'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoOutput format: keepachangelog (standard), github_release, slack (casual announcement), product_update (user-facing). Default: keepachangelog.
commitsYesRaw git log output or commit messages. Paste the output of `git log --oneline` or similar. Max ~3000 chars.
versionNoVersion number for this release. E.g. 'v2.1.0', '1.4.3'. Optional.
repo_urlNoGitHub repo URL to generate compare links. Optional.

TDQS

A4.2/5.0
Behavior4/5

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

No annotations exist, so description carries full burden. It discloses output structure ('grouped changes: features, fixes, breaking changes') and mentions formatting styles. However, it omits potential side effects or limits (e.g., character truncation beyond 3000 chars, error handling for invalid input). Overall, good disclosure for a read-like tool.

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?

Description is two sentences plus a usage phrase, no fluff. Key information is front-loaded. Every sentence adds value. Excellent conciseness.

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 no output schema, the description explains the return format (grouped changelog entries). It covers the tool's scope and usage scenarios. Minor missing details: error handling and behavior for malformed input. But overall sufficiently complete for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for all four parameters (style, commits, version, repo_url). The description adds no new semantics beyond the schema, but it does not detract. Baseline 3 is appropriate since schema already documents parameters well.

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 clearly states the tool's purpose: converting raw git commits/diff into a professional changelog formatted for various outputs (GitHub releases, CHANGELOG.md, product update). The verb 'turn' and specific resource/output make it unambiguous. No sibling tools share this exact purpose.

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?

Description provides explicit when-to-use triggers: 'when user pastes commits, says write a changelog, release notes for, what changed in this diff.' While it implies not using for raw git operations, it does not explicitly list alternatives or when not to use. Still clear context for AI agent.

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

A3.5/5.0
Disambiguation3/5

Many tools have distinct purposes, but there are several overlapping or redundant tools (e.g., leadsignal vs leadsignal_generate, multiple code audit tools, multiple trading proposal/journal tools, and several 'universal' entry points like zambo_help, zambo_ask, zambo_universal). Descriptions help, but the volume creates ambiguity.

Naming Consistency3/5

Naming conventions vary across prefixes (zambo_, zambot_, axis_, presence_, trading_, etc.), with some tools using single words (weather, translate) and others using verb_noun patterns. Aliases like leadsignal_generate for leadsignal break consistency. While prefixes provide some grouping, the overall pattern is mixed.

Tool Count2/5

125 tools is excessive for a single MCP server, even if the server aims to be a universal stack. This makes it overwhelming for agents to navigate and increases the likelihood of misselection. Many tools could be split into domain-specific servers.

Completeness5/5

The tool surface is extraordinarily comprehensive, covering agent identity, cross-layer orchestration, code analysis, content generation, legal scanning, lead generation, trading, on-chain data, and more. Nearly any common agent task is supported with multiple tools, leaving few obvious gaps.

Resources