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Pollen — Managed Agent-Readiness Layer

Fix opportunities

get_fix_opportunities
Read-only

Analyze the catalog's agent-readiness gaps and what can be done about each: points lost per score dimension, counts fixable automatically by Pollen (empty SEO meta, missing image alt text, thin tags), the 'needs you' checklist of physical facts only the merchant knows (SKU, barcode, weight, brand, photos) with product names, and per-product description candidates awaiting merchant approval. Start here when the user asks 'what's wrong?' or 'how do I improve my score?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds meaningful behavioral context by detailing the nature of the output (points, counts, checklist, candidates) without contradicting annotations. This enriches the agent's understanding beyond the bare annotations.

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 description is a single dense sentence that packs a lot of information, followed by a short trigger guidance. It's front-loaded with the primary purpose before the 'start here' instruction. Slightly long but every clause adds value; no fluff.

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?

Since there's no output schema, the description must convey what the tool returns. It lists all major output categories (points lost, fixable counts, checklist, candidates) in enough detail for an agent to know what to expect. Format and structure are not specified, but for a read-only analysis tool this is adequate. Could add a note about interactivity (e.g., awaiting approval), but it's covered implicitly.

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?

The tool has zero parameters, so schema coverage is trivially 100%. Per the rubric, 0 params = baseline 4. The description correctly contains no parameter details because none exist.

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 explicitly states the tool analyzes agent-readiness gaps and enumerates the specific outputs (points lost, fixable counts, 'needs you' checklist, description candidates). It also provides a distinct usage trigger ('what's wrong?', 'how do I improve my score?'), which clearly separates it from siblings like get_deep_audit or get_pipeline_status.

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 explicit when-to-use guidance with two example user queries. It doesn't mention when not to use it or name alternatives, but the trigger phrase makes the intended scenario unambiguous. Could be improved by noting it's a starting point vs. more granular tools, but not essential.

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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