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interview-prep-mcp

interview-prep-mcp

by shenmali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

parse_job_posting_tool

Extract structured job data from URLs, HTML, or text. Returns parsed fields with a unique identifier for interview prep.

Instructions

Parse a job posting from a URL, raw HTML, or pasted text. Returns id + parsed fields.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "source_or_text": "<source_or_text>"
    },
    "name": "parse_job_posting_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
source_or_textYes
is_htmlNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only states what the tool does and its return value, but omits behavioral traits such as network requests, error handling, or side effects, leaving significant gaps for an agent.

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 a single sentence that is front-loaded with the main verb and resource, containing no redundant words. Every word earns its place.

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?

With an output schema existing, the description appropriately summarizes return values. However, it lacks details on input validation, error scenarios, or handling of different input types, making it barely adequate for a tool with zero schema descriptions and no annotations.

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 0%, so the description must add meaning. It explains that 'source_or_text' can be a URL or text and hints at 'is_html' via mention of raw HTML, providing moderate value beyond the bare schema.

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 parses job postings from multiple input types (URL, raw HTML, pasted text) and returns parsed fields, distinguishing it from sibling tools like 'parse_cv_tool' which handles CVs.

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?

The description implies usage when a job posting is available, but does not explicitly state when not to use it or mention alternatives like 'parse_cv_tool' for CVs. The context is clear but lacks exclusions.

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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