fps-cv-mcp

summarize_cv

Read-onlyIdempotent

Summarize a CV into a tailored summary for hiring managers, academic panels, or investors. Adjust tone, style, depth, and emphasis to fit the target audience.

Instructions

Fallback CV summarization for clients without Agent Skills support.

When the cv-analyst skill is available, prefer invoking that skill instead — it provides richer orchestration, preset profiles, and artifact delivery. This tool exists for MCP clients that cannot load skills.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tone`	No	Tone of the summary. Examples: 'professional and objective', 'enthusiastic and promotional', 'analytical and critical', 'conversational and accessible', 'formal and academic'	professional and objective
`style`	No	Style of the output. Examples: 'structured paragraphs', 'bullet points', 'executive summary', 'technical brief', 'comparison table'	structured paragraphs
`context`	No	The context for the summary. Examples: 'academic research position', 'industry R&D role', 'startup technical leadership', 'consulting engagement', 'investment evaluation', 'collaboration assessment'	industry R&D role
`depth_level`	No	Level of detail for the summary. Examples: 'brief' (100-200 words), 'moderate' (200-400 words), 'comprehensive' (400-600 words), 'deep-dive' (600+ words)	comprehensive
`output_format`	No	Output format for the summary. Examples: 'markdown' (default), 'raw_text'	markdown
`target_audience`	No	Intended audience for the summary. Examples: 'technical hiring manager', 'academic search committee', 'executive leadership', 'peer researchers', 'investment team', 'collaboration partners'	technical hiring manager
`include_citations`	No	Whether to include citations and publication analysis from Google Scholar profile
`length_constraint`	No	Desired length of the summary. Examples: '1-2 paragraphs' (100-200 words), 'half-page summary' (200-400 words), 'full-page overview' (400-600 words), 'detailed report' (600+ words), 'presentation slide content' (50-100 words)	half-page summary
`emphasis_distribution`	No	Where to place emphasis in the summary. Examples: 'equal weight', 'research-heavy', 'industry-focused', 'technical-first', 'leadership-oriented'	technical-first
`additional_instructions`	No	Any specific instructions for the summary. Examples: 'Focus on AI/ML experience in healthcare applications', 'Highlight open-source contributions and community engagement', 'Compare with industry benchmarks for similar roles'

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.9Sep 16, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds the fallback context but discloses little about the tool's internal behavior, such as how the CV is selected or how include_citations affects processing.

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?

Two short sentences deliver the core purpose and usage guidance with zero filler. The key fallback message is front-loadedaine; the alternative routing appears immediately after.

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?

The description covers the main decision an agent needs to make: whether to use this tool or the skill. An output schema existsched seasons it doesn't need to explain return values, and 100% schema coverage handles parameters. The main gap is that it never specifies which CV is being summarized, but the fallback framing and routing guidance make the tool usable.

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% and every parameter has a description with defaults and examples slash variants. The description adds no parameter-specific detail, but with complete schema documentation the baseline 3 is appropriate.

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 opens with 'Fallback CV summarization' — a specific verb, resource, and clear role. It distinguishes the tool from the cv-analyst skill and indicates why it exists, making its purpose unmistakable.

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?

It explicitly states when to use the tool ('clients without Agent Skills support') and when not to ('prefer invoking that skill instead'), and names the alternative with concrete reasons. This is a model example of routing guidance.

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

Deploy Server