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benediktgirz

StoryLenses MCP Server

storylenses_generate_letter

Generate story-driven cover letters from job match data using narrative archetypes. Creates tailored applications with custom tone, length, and language options.

Instructions

Generate a story-driven cover letter using matched data and a narrative archetype. Supports en/de/es/pt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_analysisYesJob analysis from storylenses_analyze_job
match_dataYesMatch data from storylenses_match_profile
candidate_nameYesCandidate's full name
archetypeNoNarrative archetype ID (use storylenses_list_archetypes to see options)golden-fleece
toneNoWriting toneprofessional
lengthNoLetter length: short (150-200 words), medium (250-350), full (400-500)medium
localeNoOutput languageen
Behavior2/5

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

No annotations provided, so description carries full disclosure burden. It mentions locale support (en/de/es/pt) but fails to disclose output format (text? HTML? JSON?), potential side effects, rate limits, or that this generates text content. Minimal behavioral context given the complexity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence of 15 words with zero redundancy. However, given the tool's complexity (7 params, nested objects, workflow dependencies), this brevity may be insufficient rather than optimally concise—front-loads function but sacrifices necessary context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers core function and locale support, but omits critical context: workflow position in the 4-step process, what the output contains (the letter text), and behavioral traits. With no output schema and 100% input coverage, description should compensate for return value documentation but doesn't.

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 has 100% coverage with detailed descriptions, establishing baseline 3. Description adds minimal semantic value beyond schema—it references 'matched data' and 'narrative archetype' which map to parameter names but doesn't explain formats or constraints beyond what's in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verb (Generate) and resource (story-driven cover letter) clearly. Mentions key inputs (matched data, narrative archetype) implicitly distinguishing it from sibling analysis/listing tools. However, it could explicitly clarify this is the final composition step in the workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

References required inputs ('matched data', 'narrative archetype') which imply prerequisites from sibling tools (storylenses_match_profile, storylenses_list_archetypes). However, lacks explicit 'when to use' guidance, sequencing instructions, or alternative tool mentions.

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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