Zambo AI Tools

salary_script

Generate a complete salary negotiation script — opening move, rebuttals to every common pushback, BATNA, and closing. Returns a step-by-step negotiation playbook with exact words to say. Use when user says 'salary negotiation', 'how do I ask for a raise', 'they offered me X', 'I have a competing offer'.

Input Schema

TableJSON Schema

Name	Required	Description
`role`	No	Job title or role being negotiated. E.g. 'Senior Software Engineer', 'Marketing Manager'.
`context`	No	Any extra context: company stage, remote/onsite, urgency, relationship with hiring manager.
`target_salary`	No	What you want to walk away with. E.g. '$130,000'.
`current_salary`	No	Your current comp. E.g. '$95,000', '$95k + 10% bonus'.
`offer_received`	No	The offer on the table (if any). E.g. '$110,000 + standard benefits'.
`competing_offer`	No	Competing offer you have (powerful leverage). Optional.
`experience_years`	No	Years of relevant experience.

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains what the tool generates but does not disclose any behavioral traits such as auth requirements, rate limits, or side effects. The description is neutral and adequate but lacks depth.

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 two sentences, front-loading the outputs and then stating usage triggers. Every word serves a purpose, with 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?

There is no output schema, but the description lists the components of the generated script. It could improve by specifying the output format (e.g., plain text, JSON), but it is largely complete for a generation tool.

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 description coverage is 100%, with all parameters documented. The description adds minimal extra meaning beyond the schema, reiterating the context of use. Baseline 3 is appropriate as the schema already does the heavy lifting.

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 generates a complete salary negotiation script, listing specific components (opening move, rebuttals, BATNA, closing). It distinctly identifies the resource and action, setting it apart from a large set of sibling tools.

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 explicitly lists user utterances that trigger use, such as 'salary negotiation', 'how do I ask for a raise', 'they offered me X', and 'I have a competing offer'. While it doesn't mention when not to use, the positive cues are clear and actionable.

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

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