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Zambo AI Tools

by dev.zambo

sql_from_english

Convert plain-English database questions into working SQL queries — with explanation and optimization notes. Describe what you want to pull from your database and get production-ready SQL. Handles JOINs, aggregations, subqueries, window functions. Use when user says 'write a query to', 'get me all X where Y', 'SQL for', 'how do I query'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaNoYour table definitions or schema description (helps accuracy enormously). E.g. 'users(id, email, plan, created_at), orders(id, user_id, amount, created_at)'.
dialectNoSQL dialect: postgresql, mysql, sqlite, mssql, bigquery, snowflake. Default: postgresql.
requestYesWhat you want to query in plain English. E.g. 'Get the top 10 customers by total spend in the last 30 days, excluding free tier accounts'.
optimizeNoInclude index suggestions and query optimization notes. Default: true.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool returns SQL queries with explanation and optimization notes, and it handles JOINs, aggregations, etc. It does not explicitly state that the tool does not execute the SQL, which could be considered a minor gap, but the context implies a read-only generation task.

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?

Extremely concise: two sentences plus a list of use-case examples. Front-loads the core purpose and key capabilities, with no redundant information. Every sentence earns its place.

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?

For a tool with 4 parameters and no output schema, the description covers the main aspects: what it does, what it returns (SQL with explanation and optimization), and how to use it. It could mention that it does not execute queries, but overall it is sufficient for an agent to invoke correctly.

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 descriptions for all 4 parameters. The description adds little beyond the schema: it restates that the user should describe what they want (mapping to 'request') and implies schema helps accuracy. This meets the baseline but does not significantly enhance understanding.

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 converts English questions into SQL queries with explanation and optimization notes. It lists supported SQL features and example user phrases (e.g., 'write a query to', 'get me all X where Y'), making the purpose unambiguous and distinct from sibling tools, none of which are SQL-focused.

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?

Explicitly provides trigger phrases ('write a query to', etc.) and a clear directive 'Use when user says...', helping the agent select this tool over alternatives. However, it does not mention when not to use or suggest alternative tools for non-SQL queries.

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