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resume_boost

Tailor your resume to a specific job description — boost ATS keyword match, strengthen bullet points, and highlight the right experience. Returns a rewritten resume with ATS score, keyword gaps filled, and bullet points punched up with impact metrics. Use when user says 'improve my resume', 'tailor resume to this job', 'will this resume pass ATS', 'help me apply for'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoOutput style: full (complete rewrite), bullets_only (just fix bullet points), gaps_only (just highlight keyword gaps). Default: full.
resume_textYesYour current resume text. Paste the full thing (max ~4000 chars).
target_roleNoRole you're applying for if no JD available. E.g. 'Senior Product Manager at a fintech startup'.
job_descriptionNoThe job description you're targeting. Paste it in (max ~2000 chars).

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 states returns a rewritten resume with ATS score and keyword gaps, implying a non-destructive generation/analysis tool. However, it does not disclose whether it stores data, requires authentication, or has rate limits. Moderate transparency.

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 three sentences: first states purpose, second lists outputs, third gives usage examples. It is front-loaded with the key action and provides immediate value without fluff. 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?

Given the tool has 4 parameters and no output schema, the description explains the tool's purpose, typical use cases, and return values. It does not cover edge cases or error handling, but for a resume tailoring tool, it provides sufficient context for an AI agent to select and use it 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%, providing baseline descriptions for all four parameters. The tool description does not add significant extra meaning beyond the schema; it mentions 'Paste the full thing' which echoes the schema. The style parameter's enum values are already documented. No additional parameter context is provided.

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 tailors a resume to a job description, specifying actions like boosting ATS keyword match and strengthening bullet points. It lists return values (rewritten resume, ATS score, keyword gaps) and distinguishes itself by focusing on resume optimization, with no siblings in the same domain.

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 explicit example user queries ('improve my resume', 'tailor to this job') indicating when to use the tool. It does not explicitly state when not to use it, but the examples give clear context. No alternative tools are mentioned, but the sibling list shows no close competitors.

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