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Location Intelligence Score

location_score
Read-onlyIdempotent

Composite hiring-location score (0-100) for a US ZIP, weighted by role profile (engineering, sales, operations, warehouse, retail, remote). Joins Census ACS demographics, FEMA flood, IECC climate, FAA airport, BEA cost-of-living, and state income tax into one tuned signal. Returns overall score, tier, verdict, and six sub-scores with notes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipYesU.S. ZIP code, for example 10001.
roleNoRole profile that tunes the sub-score weights.engineering
yearNo
filing_statusNosingle

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare the tool read-only, non-destructive, and idempotent; the description is consistent with these. The description adds valuable behavioral context by naming the joined data sources (Census, FEMA, IECC, FAA, BEA, state tax) and the exact return components. It does not cover error or edge-case behavior, but the annotations already supply the safety profile.

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 dense sentences pack purpose, inputs, data sources, and returns without redundant phrases. The key scoping constraint (US ZIP, role weighting) is front-loaded before the data-join details. No word is wasted.

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?

For a medium-complexity tool with no output schema, the description gives enough for basic invocation: required ZIP, optional role, and return shape. However, it omits the meaning of year and filing_status, possible invalid-ZIP behavior, and how the score relates to nearby sibling tools. Overall viable but with identifiable gaps.

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 50%: zip and role are described, and the description reinforces both (US ZIP, role-tuned weights). Year and filing_status remain semantically unexplained in both the schema and the description, despite their likely impact on state-income-tax calculations. The description partially compensates but does not fully close the gap.

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?

States a specific verb and resource: produces a composite hiring-location score (0-100) for a US ZIP with role-based weighting. The output details (overall score, tier, verdict, six sub-scores) make the purpose unmistakable among siblings. It does not explicitly call out a sibling alternative, so it stops short of a 5.

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?

The description implies the tool is for evaluating a location for hiring based on role profile, but it never states when to prefer it over related tools like geo_zip_lookup, hiring_signal, jobs_concentration, or remote_hire_cost. No exclusions or alternative conditions are given. This is usable but relies on inference.

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.

Naming Consistency4/5

All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).

Tool Count1/5

129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.

Completeness4/5

Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.

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