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interview-prep-mcp

interview-prep-mcp

by shenmali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

save_research_note_tool

Persist research notes for interview preparation sessions. Store content, subject, and source URL for each note.

Instructions

Persist a research note for a prep session. Markdown content is fine.

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": {
      "interview_id": "<interview_id>",
      "kind": "<kind>",
      "content": "<content>"
    },
    "name": "save_research_note_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
interview_idYes
kindYes
contentYes
subjectNo
source_urlNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are present, so the description must fully disclose behavior. It implies a write operation but does not mention whether it overwrites, appends, requires permissions, or any rate limits. The note only says 'Markdown content is fine', leaving significant gaps.

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 extremely concise with two sentences, no filler, and the key action is front-loaded. Every word serves a purpose.

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

Completeness2/5

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

Given 5 parameters (3 required), an enum, and no annotations, the description is incomplete. It does not explain parameter usage, return value (despite output schema existing), or provide enough context for an AI agent to correctly invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds minimal value beyond the schema. It hints that 'content' accepts markdown, but does not explain 'interview_id', 'kind', 'subject', or 'source_url'. The enum for 'kind' is undocumented.

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 clearly states the verb 'Persist' and the resource 'research note for a prep session', making the purpose evident. However, it does not differentiate from siblings like 'analyze_weak_areas_tool' or 'generate_questions_tool', which is acceptable as they are distinct in function.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description lacks context such as prerequisites or situations where saving a research note is appropriate.

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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