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ask_hirepulse_advisor

Read-only

Free advisor for AI agents: describe your goal (B2B leads, hiring signals, buying intent, tech stack lookup, Google Ads, VC due diligence) and get the right HirePulse tool, the real cost, your free-call balance and a ready-to-run request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_goalYes
budget_usdNo
domain_countNo
sample_domainNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyYes
toolsNo
free_quotaYes
guaranteesYes
cached_hintYesToday only, status metadata; no result data or new engine calls.
alternativesYes
recommended_toolYesRecommended data tool, or null when the goal is unclear.
cost_estimate_usdYesList-price range before free quota, actual data availability and optional follow-up calls; includes request splitting and scan limits.
efficiency_analysisYes
recommended_execution_sequenceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, so safety is partly covered. The description adds useful context (it's free, and it surfaces 'the real cost' and 'your free-call balance', implying a rate/budget dimension), but it doesn't state whether the call itself consumes the free balance or whether the 'ready-to-run request' is executable.

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?

One dense, front-loaded sentence that leads with the value proposition ('Free advisor for AI agents'). Efficient, though the long parenthetical list of goal types slightly burdens readability.

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?

An output schema exists, so return values needn't be spelled out, and the description does summarize the output. The remaining gap is the unexplained non-required parameters, which leaves an agent unsure how to tune cost or scope estimates.

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?

Schema description coverage is 0%, so the description must carry the parameter burden, yet it only loosely explains agent_goal via the parenthetical goal list. budget_usd, domain_count, and sample_domain are never mentioned or given meaning, leaving three of four parameters undocumented.

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?

States a specific verb+resource (advisor that takes a goal and returns the right HirePulse tool, cost, balance, and a ready-to-run request) and enumerates the goal domains it handles. This clearly distinguishes it from the sibling data tools it routes to.

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?

Strongly implies the use case – describe a goal when you don't yet know which sibling tool to call – and lists concrete goal categories. It does not, however, explicitly say when NOT to use it (e.g., when the correct tool is already known).

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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