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Filipe-Soares-de-Almeida

MemAI

by Filipe-Soares-de-Almeida
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Python
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optimize_runs

Read-onlyIdempotent

List optimization runs with their review progress to see whether users applied or rejected suggestions, including counts and backup paths.

Instructions

List optimization runs with their review progress.

Read-only companion to optimize_stage: after staging, use this to see whether the user has applied/rejected your suggestions in the admin dashboard. Each run carries total/pending/applied/rejected counts, its note, and the safety-backup path once the first apply happened. Applying/rejecting stays in the dashboard by design -- the agent proposes, the human disposes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNowhere the page starts; `next_offset`, when present, starts the next.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.16.1
    • addedInput schema / properties / offset
      Added value: +{
+  "default": 0,
+  "description": "where the page starts; `next_offset`, when present, starts the next.",
+  "type": "integer"
+}
    • removedInput schema / title
      Removed value: -"optimize_runsArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "items": {
-        "additionalProperties": true,
-        "type": "object"
-      },
-      "title": "Result",
-      "type": "array"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "optimize_runsOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description still adds real context: what each run carries (total/pending/applied/rejected counts, note, safety-backup path once the first apply happened) and the human-in-the-loop design ('the agent proposes, the human disposes').

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?

Purpose is front-loaded in the first sentence, followed by the workflow context and return-content summary. Every sentence carries information; nothing is filler or restated from the schema.

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?

With no output schema, the description compensates by enumerating the fields each run returns (counts, note, backup path) and by clarifying the workflow position and human-approval boundary. Nothing an agent needs to call this correctly is missing.

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?

Only one optional parameter (offset) with 100% schema description coverage, so the schema already documents its meaning and the next_offset paging contract. The description adds no parameter-level detail beyond that, making the baseline 3 correct.

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+resource ('List optimization runs with their review progress') and immediately differentiates from the sibling by calling itself the 'Read-only companion to optimize_stage'. An agent can distinguish it from optimize_scan/optimize_stage/optimize_status without opening schemas.

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?

Gives the explicit triggering condition ('after staging, use this to see whether the user has applied/rejected your suggestions') and names the sibling (optimize_stage) whose output it follows up on. It also states the boundary that applying/rejecting stays in the dashboard, so the agent knows what NOT to attempt.

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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