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career-mcp

career-mcp

by sindhujaIBM
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

query_stories

Retrieve saved career stories by tag to find relevant examples for job requirements, interview questions, or LinkedIn post ideas.

Instructions

Retrieve saved career stories, optionally filtered by tag (e.g. leadership, conflict, failure-recovery, technical-deep-dive). Use to find a story relevant to a JD requirement, an interview-prep question, or a LinkedIn post idea.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "query_stories"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNoOptional tag filter

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden, and it only implies read-only behavior through the word 'Retrieve' on 'saved' content. It does not disclose match semantics for multiple tags (AND vs OR), what happens on no match (empty list vs error), or whether results are paginated or ordered.

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 sentences, zero filler, with the action and its optional modifier front-loaded before the usage sentence. Every clause carries information the agent needs.

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 one-optional-parameter read tool with no output schema and no nested structures, the description covers purpose, filter examples, and invocation contexts adequately. The remaining gap is result shape and multi-tag behavior, which matters less here since no output schema exists to cover it.

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

Parameters4/5

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

Schema coverage is 100% with a single, well-documented optional array, so the baseline is 3, and the description earns above baseline by supplying example tag vocabulary (leadership, conflict, failure-recovery, technical-deep-dive) that an agent could not infer from 'Optional tag filter'. It stops short of stating how multiple tags combine.

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 opens with a specific verb and resource ('Retrieve saved career stories') plus the scoping modifier ('optionally filtered by tag'), which cleanly separates it from sibling mutation tools like save_story and from the generative start_story_interview. An agent can tell immediately that this is the read path over the existing story corpus.

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

Usage Guidelines4/5

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

The second sentence gives three concrete use cases (JD requirement matching, interview-prep question, LinkedIn post idea), which is strong positive routing. However, it names no alternative or exclusion — e.g. it never says to use start_story_interview instead when no saved story exists.

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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