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Set how many answers each check captures

set_tracked_query_passes
DestructiveIdempotent

Change how many answers each check of up to 100 tracked queries captures (nPasses; above 1 only for AI engines). Fewer passes spend less budget; use get_usage to see the effect. Passes no longer captured are never made up later. Each one that cannot be changed is reported in "failed". Safe to retry.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "organizationId": "<organizationId>",
      "projectId": "<projectId>",
      "trackedQueryIds": "<trackedQueryIds>",
      "nPasses": "<nPasses>"
    },
    "name": "set_tracked_query_passes"
  }
}

Input Schema

NameRequiredDescriptionDefault
nPassesYes
projectIdYes
organizationIdYes
trackedQueryIdsYes

Output Schema

NameRequiredDescriptionDefault
failedNoAlways present, empty list included: read it rather than inferring success from the status code.
successfulNoThe resources the operation was applied to.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=true, so the bar is lower, yet the description still adds real value: passes dropped are permanently lost ("never made up later") and partial failures surface per-item in "failed". "Safe to retry" corroborates the idempotency hint. It does not, however, say what permissions are required.

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 definition front-loads what changes, then budget impact, then irreversibility. Four short sentences, all carrying information; only the repeated "up to 100" constraint is redundant with the schema.

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 explained, and the description still flags the "failed" field for partial failures. For a destructive batch mutation it covers budget impact, irreversibility, and failure reporting; the only gap is the unaddressed organizationId/projectId semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry the load. It explains nPasses conceptually (answers per check, above 1 only for AI engines) and restates the 100-query cap already in the schema's maxItems, but organizationId and projectId get no explanation at all. Half the parameters remain undocumented anywhere.

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?

The description states a specific verb and resource: change how many answers each check of tracked queries captures. It defines the operative concept (nPasses) inline, so an agent can distinguish this from the sibling set_tracked_query_check_frequency, which alters cadence rather than depth.

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?

It gives consequence-oriented context ("Fewer passes spend less budget") and routes to get_usage to observe the effect, but never states when to prefer this tool over the frequency sibling or any precondition for calling it. Usage is implied rather than explicit.

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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