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oahelper-mcp

oahelper-mcp

by SirjanSingh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

oahelper_interviews

Retrieve company interview experiences, save each full write-up to your local library, and get a short list for offline reading.

Instructions

Interview experiences for a company from OAHelper (12 per page, most relevant first). Saves each full write-up to library//interviews/ and returns a short list; read the saved files for the full text.

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": {
      "company": "<company>"
    },
    "name": "oahelper_interviews"
  }
}

Input Schema

NameRequiredDescriptionDefault
pagesNo
companyYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses a side effect (saving full write-ups to library/<company>/interviews/), the pagination rate (12 per page, most relevant first), and that the return is only a short list. It omits overwrite behavior, auth/permission needs, and rate limits, which keeps it below a 5.

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?

Two tight sentences with the core resource front-loaded and the side effect plus the required follow-up action stated next. Every clause carries information; only minor polish is missing.

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?

For a two-parameter tool with an output schema present, the description covers the essentials an agent needs: what is returned (a short list), where the full text lives, and the page size. Remaining gaps (whether re-running overwrites saved files, how `pages` interacts with results) are secondary.

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% for both parameters, so the description must compensate. '12 per page' gives the agent a way to infer what `pages` means (pages × 12 results, default 2), and `company` is implicitly the lookup key, but neither is stated explicitly.

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?

The description states a specific resource ('Interview experiences for a company') and its source (OAHelper), which is clear enough for an agent to distinguish it from oahelper_question or oahelper_solution. It does not explicitly contrast itself with oahelper_search, so sibling differentiation is only partial.

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?

Usage is implied: fetch interview write-ups for a company, then 'read the saved files for the full text,' which is a useful follow-up instruction. There is no guidance on when to prefer this over oahelper_search or how to size the `pages` parameter, so no explicit when/when-not reasoning is given.

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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