SolidPing

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Read-onlyIdempotent

Query monitoring results (raw or aggregated) with filtering by check, type, status, region, period type, and time range. Use this for trend analysis or to inspect a specific window. For investigating a single check's current state, use diagnose_check instead. Returns {data, pagination, window, effectiveFilter}: data holds the result rows, window the time bounds actually queried, and effectiveFilter the period filter that ran after defaulting. Defaults: if periodType is omitted it falls back to "hour"; if periodStartAfter is omitted it falls back to a window matched to the finest periodType requested (raw=1h, hour=24h, day=30d, month=365d). Read-only: works with mcp:read tokens.

Input Schema

Name	Required	Description
`size`	No	Max results (1-100, default 20).
`with`	No	Comma-separated extra fields: durationMs — response time in ms durationMinMs — min response time in the bucket (aggregated rows only) durationMaxMs — max response time in the bucket (aggregated rows only) region — region the check ran in metrics — per-execution metrics output — full check output incl. error messages availabilityPct — uptime % (aggregated rows only) checkSlug — slug of the check checkName — human name of the check Example: "durationMs,output".
`cursor`	No	Opaque pagination cursor returned by a previous response. Omit on the first page.
`region`	No	Comma-separated region slugs, e.g. "eu-west-1,us-east-1".
`status`	No	Comma-separated result statuses. Allowed: up, down, created, running, abandoned. "down" covers every genuine failure (down, timeout, error); "abandoned" is separate on purpose — it marks an attempt nothing was ever reported for, which is excluded from availability and is NOT downtime. Example: "down" or "down,abandoned".
`checkUid`	No	Comma-separated check UIDs or slugs to filter by, e.g. "api-prod,db-prod".
`checkType`	No	Comma-separated check types. Allowed: http, tcp, icmp, dns, heartbeat, email, domain, ssl, smtp, udp, ssh, pop3, imap, websocket, postgresql, ftp, sftp, js, mysql, redis, mongodb, mssql, oracle, clickhouse, grpc, kafka, mqtt, a2s, minecraft, rabbitmq, snmp, docker, browser, freebox_line, dnsbl, sip, kubernetes, ntp, rdp, vnc, prometheus, private-location, crawl, health, vendor-status. Example: "http,dns".
`periodType`	No	Comma-separated period types. Allowed: raw (single executions), hour, day, month (rollups). Defaults to "hour" when omitted. Example: "raw".
`periodEndBefore`	No	RFC3339 timestamp (exclusive upper bound), e.g. "2026-05-03T11:00:00Z".
`periodStartAfter`	No	RFC3339 timestamp (inclusive lower bound), e.g. "2026-05-03T10:14:22Z". Defaults to a window matched to periodType when omitted (raw=1h, hour=24h, day=30d, month=365d).

Output Schema

Name	Required	Description
`data`	No	Result rows on this page: raw executions or aggregated rollups.
`window`	No	Time window the server actually queried.
`pagination`	No	Cursor pagination for this filter.
`effectiveFilter`	No	Period filter that actually ran, after the tool's defaulting.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 2, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Goes beyond the annotations by describing the return envelope (data/pagination/window/effectiveFilter), the defaulting rules for periodType and periodStartAfter, and the token scope required ('works with mcp:read tokens'). The 'read-only' restatement is redundant with readOnlyHint=true, but the default-window logic and effectiveFilter behavior are genuinely new information.

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?

Three sentences, front-loaded with purpose and the sibling route, then the return shape, then defaults. The defaults sentence largely duplicates schema text and could be trimmed, but nothing is padding in the harmful sense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter, all-optional query tool with full schema coverage and an output schema, the description covers purpose, alternative routing, default semantics, and auth scope. An agent has everything needed to call it correctly on the first attempt.

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 coverage is 100% and the schema already documents each parameter, including the same periodType and periodStartAfter defaults the description repeats. The description adds little beyond the schema here, so the baseline 3 applies.

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?

Opens with a concrete verb plus resource ('Query monitoring results (raw or aggregated)') and enumerates the filter dimensions. It explicitly names the sibling it is not (diagnose_check) and the scenario that selects that sibling, so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

States positive use cases ('trend analysis or to inspect a specific window') and an explicit exclusion with the alternative ('For investigating a single check's current state, use diagnose_check instead'). Both sides of the when/when-not decision are covered.

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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