Skip to main content
Glama
benediktgirz

StoryLenses MCP Server

storylenses_quality_check

:

Instructions

Score and evaluate a cover letter for relevance, narrative strength, and completeness. Returns score 0-100 with actionable feedback.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
letter_textYesThe cover letter text to evaluate (min 200 characters)
job_analysisYesJob analysis from storylenses_analyze_job
localeNoFeedback languageen
Behavior3/5

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

With no annotations provided, the description must carry the full behavioral disclosure burden. It successfully discloses the return format ('score 0-100 with actionable feedback'), but omits safety-critical context like whether the operation is read-only, if results are cached/persisted, or potential rate limiting. The mention of 'actionable feedback' adds some behavioral context beyond pure typing.

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 efficient sentences with zero waste. The first sentence front-loads the action and scope; the second clarifies the return value. Every word serves a specific purpose in communicating tool capability.

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 absence of an output schema, the description appropriately compensates by detailing the return value format (0-100 score + feedback). The 100% schema coverage for inputs handles the nested job_analysis object sufficiently. Minor gap: no mention of workflow integration with the job analysis/generation siblings.

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?

Input schema has 100% description coverage, establishing a baseline of 3. The description does not add parameter-specific semantics beyond the schema (e.g., it does not explain that the job_analysis object must be sourced from the sibling analyze tool, or provide guidance on optimal letter_text length beyond the schema's minLength constraint).

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 uses specific verbs ('Score and evaluate') and clearly identifies the resource (cover letter) and evaluation dimensions ('relevance, narrative strength, completeness'). It effectively distinguishes itself from sibling storylenses_generate_letter (which creates content) by stating this tool evaluates existing content.

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?

While the description implies the tool is for quality assurance of existing letters, it lacks explicit guidance on when to invoke it relative to siblings (e.g., 'use after generating a letter with storylenses_generate_letter'). The schema references storylenses_analyze_job in the job_analysis parameter description, but the description text itself provides no explicit workflow guidance or alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/benediktgirz/storylenses-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server