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Cgisclair29

Rapid7 Bulk Export MCP

by Cgisclair29

Query Rapid7 Data

query_rapid7
Read-onlyIdempotent

Run SQL queries to analyze Rapid7 vulnerability, asset, policy, and remediation data for security insights.

Instructions

Execute a SQL query against the Rapid7 database.

The database contains the following tables loaded from Rapid7 InsightVM Bulk Export API Parquet files:

assets — Asset inventory data: Key fields: orgId, assetId, agentId, hostName, ip, mac, osFamily, osProduct, osVersion, osDescription, riskScore, sites, assetGroups, tags, awsInstanceId, azureResourceId, gcpObjectId

vulnerabilities — Combined asset + vulnerability data: Key fields: orgId, assetId, vulnId, checkId, port, protocol, title, description, severity, severityRank, cvssScore, cvssV3Score, cvssV3Severity, hasExploits, epssscore, epsspercentile, riskScoreV2_0, cves, firstFoundTimestamp, reintroducedTimestamp, dateAdded, dateModified, datePublished, pciCompliant, pciSeverity

policies — Policy compliance results (agent and scan based): Key fields: orgId, assetId, benchmarkNaturalId, profileNaturalId, benchmarkVersion, ruleNaturalId, ruleTitle, finalStatus, proof, lastAssessmentTimestamp, benchmarkTitle, profileTitle, publisher, fixTexts, rationales, source ('agent' or 'scan')

vulnerability_remediation — Vulnerability remediation tracking: Key fields: orgId, assetId, cveId, vulnId, proof, firstFoundTimestamp, reintroducedTimestamp, lastDetected, lastRemoved, title, description, cvssV2Score, cvssV3Score, cvssV2Severity, cvssV3Severity, cvssV2AttackVector, cvssV3AttackVector, riskScoreV2_0, datePublished, dateAdded, dateModified, epssscore, epsspercentile

Use this tool to query any of the above tables. You can filter, aggregate, join across tables, or perform any SQL-based analysis supported by DuckDB.

Examples:

  • SELECT * FROM vulnerabilities WHERE severity = 'Critical' LIMIT 10

  • SELECT severity, COUNT(*) FROM vulnerabilities GROUP BY severity

  • SELECT * FROM policies WHERE finalStatus = 'fail' LIMIT 10

  • SELECT cveId, COUNT(*) FROM vulnerability_remediation GROUP BY cveId

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesSQL query to execute against the database

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.5.2

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context: the data comes from Rapid7 InsightVM Bulk Export Parquet files and SQL is executed with DuckDB support. This gives the agent expectations about the underlying engine and data source without contradicting the annotations.

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?

The opening sentence is direct, and the table-by-table field lists are useful rather than filler for a SQL tool. The examples are compact and illustrative. It is long, but the length is mostly justified by the need to document the data model for query authoring.

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 complex SQL-query tool, the description covers the available tables, representative fields, dialect, and example queries. An output schema exists, so return-value documentation is not the description's job. It does not mention result limits, timeouts, or error behavior, but those are minor given the annotations and the tool's read-only nature.

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?

Schema coverage is 100% for the single sql parameter, so the baseline is 3. The description adds real value by specifying DuckDB as the SQL dialect, listing queryable tables with key columns, and providing representative examples. This materially reduces ambiguity about what kinds of queries are valid.

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 and resource ('Execute a SQL query against the Rapid7 database') and goes beyond the name by enumerating the four available tables and their key fields. This clearly distinguishes it from sibling lifecycle tools like start_rapid7_export, download_rapid7_export, or purge_rapid7_data.

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?

Explicitly says 'Use this tool to query any of the above tables' and gives concrete SQL examples, establishing clear context for when it applies. It does not name exclusions or alternatives, but the sibling set is self-evident enough that an agent can see this is the data-querying tool versus export/schema/stats helpers.

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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