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Read learning suggestions

gate_learn_suggestions
Read-only

Read normalized routes and a suggested per-minute limit from an admin-enabled, time-boxed learning session. Records only method, normalized path templates and counts per minute; no query values, raw ids, headers or bodies. Never enables learning or applies rules.

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": {
      "project_id": "<project_id>",
      "admin_key": "<admin_key>",
      "app": "<app>"
    },
    "name": "gate_learn_suggestions"
  }
}

Input Schema

NameRequiredDescriptionDefault
appYesThe app id
admin_keyYesThe project admin key (ga_...). Read it from the .env; never print it.
project_idYesThe project id from gate_create_project, e.g. p1a2b3...

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, and the description usefully adds that it never enables learning or applies rules, reinforcing the no-side-effects profile. It also discloses what data is and isn't captured (method, path templates, counts per minute; no query values, raw ids, headers or bodies), which is real context beyond annotations, though it omits behavior when no session exists or when the session has expired.

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 dense sentences with the core return value front-loaded and the privacy/side-effect constraints trailing as clarification. No filler, no restatement of the tool name or title.

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?

With no output schema, the description correctly describes what comes back (normalized routes and a suggested per-minute limit) and what is excluded. It is nearly complete for a 3-param read tool, lacking only failure/empty-session behavior and any hint that results are scoped to the time-boxed window.

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%, so app, admin_key and project_id are already documented with format and source hints in the schema itself. The description adds only the implicit 'admin-enabled' framing for admin_key and nothing about parameter syntax beyond the schema, so the 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 (read) and resource (normalized routes + a suggested per-minute limit) scoped to an admin-enabled learning session, which is a distinct purpose from the audit/listing siblings. However, it never names or contrasts against the closest read siblings (gate_audit, gate_list_actions), so differentiation is inferred rather than explicit.

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?

The phrase 'from an admin-enabled, time-boxed learning session' establishes the precondition for use, and 'Never enables learning or applies rules' draws a clear negative boundary. It stops short of naming an alternative tool to use when no learning session is active or when the agent wants raw traffic instead of normalized routes.

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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