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kishanraj41

Polygraph MCP Server

by kishanraj41

Get Integrity Score

get_integrity_score

Calculate a job's lineage integrity score by comparing declared and observed edges, returning precision and recall to pinpoint stale claims versus missing sources.

Instructions

Get the Lineage Integrity Score for a job, with its components.

The score is the Jaccard index of declared vs observed edges. Precision and recall are returned separately because a low score from stale claims and a low score from missing sources need different fixes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_urnYesA DataHub dataJob URN.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses what the score means (Jaccard index of declared vs observed edges) and that precision and recall are returned separately, but says nothing about side effects, permissions, or what happens for a job with no observed edges.

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 short sentences, purpose front-loaded, and the second sentence establishes the diagnostic framing rather than restating the first. No filler.

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?

An output schema exists, so return values need not be spelled out, and for a one-parameter read tool the description covers the essentials plus useful metric semantics. It stops just short of full completeness by omitting any relationship to the sibling tools the agent must choose between.

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 100% for the single job_urn parameter, so the schema already documents it fully. The description adds nothing about the parameter (e.g., URN format or where to obtain it), so baseline 3 applies.

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: 'Get the Lineage Integrity Score for a job, with its components.' It is clear what the tool returns, but it never distinguishes itself from the topically similar sibling can_i_trust or explain_verdict_semantics, so an agent must infer the boundary itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use/when-not guidance and no alternatives are named, despite five siblings that plausibly overlap (can_i_trust, explain_verdict_semantics). The second sentence explains how to interpret precision vs recall, which is output interpretation, not tool selection.

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