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100Hires MCP Server

100Hires MCP Server

Official
by 100Hires
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

hires_list_origins

List candidate origin taxonomy values to normalize sourcing data and enable attribution analytics.

Instructions

List candidate origin taxonomy values. Use for attribution analytics and source normalization.

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": "hires_list_origins"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.3

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. 'List' implies a read-only operation, but the description does not disclose details like response format, pagination, or authentication requirements. For a simple taxonomy list, it is minimally sufficient but lacks richer behavioral context.

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 two short sentences, front-loaded with the primary action ('List candidate origin taxonomy values') followed by a targeted use case. Every word earns its place with no redundancy.

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

Completeness5/5

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

For a zero-parameter list tool, the description covers what it does and why to use it. Without an output schema, it could mention return type, but the pragmatic use case ('attribution analytics and source normalization') makes it sufficiently complete for an agent to select and invoke correctly.

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?

The tool has zero parameters, so the baseline is 4. The description correctly adds no parameter-level detail beyond what the empty schema already conveys, which is appropriate.

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 'lists candidate origin taxonomy values' with a specific resource (candidate origin) and a clear verb. It distinguishes itself from sibling list tools by mentioning 'attribution analytics and source normalization', which clarifies its unique scope.

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 description provides clear context on when to use it: 'Use for attribution analytics and source normalization.' It does not explicitly list alternatives or exclusions, but the use case is specific enough to guide an agent away from similar list tools.

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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