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RingGuard — AI Receptionist for Trade Businesses

Lead-gen status

lead_gen_status
Read-only

Health of the automated lead-gen pipeline (job postings → AI enrichment → unclaimed pool, runs every 4 hours): when it last delivered leads and how many.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and scope are covered structurally. The description adds real value beyond them by disclosing the pipeline cadence (runs every 4 hours) and roughly what is returned, but it says nothing about freshness semantics, staleness thresholds, or error/empty states.

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?

A single dense sentence with the key noun phrase front-loaded and the returned facts placed up front rather than buried. The parenthetical pipeline diagram is compact and informative, though the sentence does pack three ideas together, reducing scannability slightly.

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?

With no output schema, the description takes on the job of describing the return content, and it does so at a high level ('when it last delivered leads and how many'). For a zero-parameter, read-only diagnostic that is sufficient to call the tool correctly, though exact field names and response shape remain undocumented.

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 takes zero parameters, so there is no parameter semantics for the description to clarify; the baseline of 4 applies. Nothing in the description misdescribes inputs, and it correctly presents itself as a no-argument status check.

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?

The description names a specific resource (the automated lead-gen pipeline) and states exactly what it reports: last delivery time and lead count. It is clearly distinct from lead-oriented siblings like find_leads or add_lead, though it never explicitly contrasts itself with pipeline_report, which is the closest potential overlap.

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?

Usage is only implied by the content ('health of the pipeline'), with no statement of when to call it, when not to, or which sibling to prefer for adjacent questions such as pipeline_report or my_stats. An agent can infer a diagnostic use case but gets no routing guidance.

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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