bigfix-root-mcp

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Client Query Results

client_query_results

Fetch cumulative results for a submitted client fast query, polling by query_id until reported_count stops growing and paging rows with limit/offset.

Instructions

Fetch current results for a previously submitted client fast query.

Results are cumulative and there is no completion flag: safe and cheap to call repeatedly until reported_count stops growing or the expected number of computers have reported.

reported_count and result_row_count always describe the full result set; the returned rows are windowed by limit/offset.

Input Schema

Name	Required	Description
`limit`	No	Max rows to return (default 500). The response reports total_available and truncated.
`offset`	No	Row offset, for paging through results.
`query_id`	Yes	Query ID returned by client_query_submit.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.2.0Oct 20, 2026

addedInput schema / properties / limit

Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Max rows to return (default 500). The response reports total_available and truncated."
+}

addedInput schema / properties / offset

Added value: +{
+  "default": 0,
+  "description": "Row offset, for paging through results.",
+  "type": "integer"
+}

First observedv0.1.0Aug 4, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that results are cumulative, that there is no completion flag, that repeated calls are safe and cheap, and how windowing interacts with the totals. It omits error behavior (e.g. invalid/expired query_id) and retention limits, which keeps it below 5.

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?

Three short sentences, each earning its place: purpose, polling strategy, and pagination semantics. Front-loaded with the core action and free of 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 shape need not be restated, and the description still covers the polling and pagination model well. What is missing for a polling tool – failure/expiry behavior on a stale query_id and any refresh cadence – is minor but leaves it slightly incomplete.

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%, so the baseline is 3, but the description adds genuine meaning beyond the schema by explaining that reported_count/result_row_count reflect the full result set while rows are windowed by limit/offset – a semantic distinction the schemas do not state. That clarifying relationship lifts it above baseline.

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 precise verb and resource ('Fetch current results for a previously submitted client fast query') and clearly positions itself as the read side of the submit/read pair, distinguishing it from client_query_submit. It does not contrast itself with the other query-ish sibling (client_query) or name it, so differentiation is good but not exhaustive.

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?

Gives an explicit usage pattern: call repeatedly until reported_count stops growing or the expected number of computers report, which is real when-to-use guidance. It stops short of naming alternatives or the condition under which a different tool (e.g. client_query) should be preferred.

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