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elephant-xyz

Elephant MCP Server

by elephant-xyz

Get permit coverage by source

getPermitCoverage

Check permit data coverage by source system for a county, including permit counts and completion date ranges, to qualify aggregate permit answers when data lags or dates are missing.

Instructions

Returns per-source-system permit coverage for a county from the 'permits' query table: each source_system with its permit_count and completion_date range (earliest/latest), plus the overall total. The donphan agent uses this to QUALIFY aggregate permit answers (permit data lags appraisals and some sources may have NULL dates).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countyYesCounty to report permit coverage for (case-insensitive), e.g. 'Lee'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.12.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden, and it does well by disclosing the underlying data source, the aggregation behavior, and important caveats about data lag and NULL dates. It does not mention permissions, response shape, or ordering, but for a simple read-only coverage query the key behavioral traits are transparent.

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 sentences with no wasted words. The first sentence front-loads what the tool returns and the second provides valuable usage context. Every sentence earns its place.

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-parameter tool with no output schema, the description is largely complete: it states the input, the output fields, the source table, and the motivating use case. It could be slightly stronger by specifying the exact response structure or how NULL dates are represented, but nothing critical is missing for correct selection and invocation.

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?

The schema already covers the single 'county' parameter at 100% with a clear example and case-insensitivity note. The description adds no additional parameter-level meaning, so the baseline score of 3 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 uses a specific verb ('Returns') and names the exact resource: per-source-system permit coverage for a county from the 'permits' query table. It enumerates the outputs (source_system, permit_count, completion_date range, overall total), which clearly distinguishes it from sibling tools like queryPermits or getPropertyPermits that focus on individual permit records rather than aggregate coverage.

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 gives clear context for when to use this tool: when the agent needs to QUALIFY aggregate permit answers, because permit data lags appraisals and some sources may have NULL dates. It does not explicitly name alternatives or state when not to use it, so it falls short of a 5, but the usage context is clear enough for an agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.