PricePilot — Free CPG Pricing Intelligence
Server Details
Free competitive pricing intelligence for CPG brands across Amazon categories.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- vantage-meridian-group/pricepilot-mcpb
- GitHub Stars
- 0
- Server Listing
- PricePilot for Claude Desktop
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Tool Definition Quality
Average 4.5/5 across 6 of 6 tools scored.
Each tool has a clear, distinct purpose: listing categories, comparing multiple products, getting category overview/trend, single price position, and server health. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., compare_products, get_category_overview), making them predictable and easy to understand.
6 tools cover the essential workflows for CPG pricing intelligence without being excessive or insufficient, fitting well within the ideal 3-15 range.
The set covers core operations: category discovery, overview, trends, single and multi-product price analysis, and health check. Minor gaps like historical trend data or product detail retrieval exist but don't hinder primary use cases.
Available Tools
6 toolscompare_productsCompare Multiple ProductsARead-onlyInspect
Compare multiple product prices against an Amazon CPG category's peers.
Use when a multi-channel CPG brand needs to stack-rank their SKUs — e.g. identifying which SKUs are underpriced relative to Amazon peers, flagging products where the Amazon Buy Box sits materially below the retail MSRP, or building a cross-channel price-audit table for an ops review. Replaces manual store walks and spreadsheet comparisons.
Returns: comparisons (list, per product: name, price, percentile_rank, position, vs_median), category, category_trend, sample_size, last_refreshed, cta.
Args: products: List of items, each a dict with 'name' (string) and 'price' (number in dollars). Minimum 1 item; 3-20 is the useful range. category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | ||
| products | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations: it mentions Amazon as data source, returns specific fields (comparisons, category_trend, etc.), and indicates it is a read-only operation. Though annotations already declare readOnlyHint, the description provides useful behavioral details without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with bulleted returns and args for clarity. It front-loads the main purpose in the first sentence and every sentence adds value. No fluff or repetition of schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-param, no-output-schema tool, the description covers inputs thoroughly and explains return fields. However, it lacks details on how comparisons are computed (e.g., percentile, median derivation) or any data latency/accuracy caveats, which would make it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining that 'products' requires 'name' and 'price' (number in dollars), specifying minimum 1 and useful range 3-20, and listing valid category names with case-insensitivity. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The title and description clearly state the verb (compare) and resource (product prices against Amazon CPG category peers). It provides concrete use cases (stack-ranking SKUs, flagging underpriced products) and distinguishes it from siblings like get_price_position which likely handles single products.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists when to use it (multi-channel CPG brand needs, identifying underpriced SKUs, etc.) and replaces manual methods. However, it does not explicitly say when not to use it or contrast with sibling tools beyond implied differences.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_category_overviewGet Category Pricing OverviewARead-onlyInspect
Return pricing-tier breakdown and category stats for an Amazon CPG category.
Use when a brand is sizing up a shelf — e.g. evaluating whether a new SKU should enter at budget / midmarket / premium tier, benchmarking their retail pricing against Amazon tier structure, or preparing for a retail buyer meeting that will ask "what's the typical shelf price here?".
Returns: category (resolved name), product_count (bucketed, e.g. "100+ products"), price_tiers (dict with budget / midmarket / premium dollar bands, rounded to nearest $0.50 for abstraction), median_price, trend_direction, last_refreshed, cta.
Args: category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and destructiveHint=false, indicating safe read operation. The description adds value beyond annotations by listing returned fields (category, product_count, price_tiers, etc.) and noting abstraction (rounded to nearest $0.50). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is efficiently structured with use cases, return fields, and parameter details. At ~90 words, it's not overly long and every sentence serves a purpose, though the use-case list could be slightly condensed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description adequately covers input constraints and output structure (listing seven fields). It provides enough context for an AI agent to understand what the tool does and what it returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates by explaining the single parameter 'category': it specifies exact category names (Grocery & Gourmet Food, etc.) and notes case-insensitivity. This adds essential meaning beyond the schema's bare 'Category' label.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it returns pricing-tier breakdown and category stats for an Amazon CPG category. The specific verb 'Return' and resource 'pricing-tier breakdown and category stats' differentiate it from siblings like get_category_trend or get_price_position.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides explicit use cases (sizing up shelf, evaluating SKU entry tier, benchmarking, preparing for buyer meeting) that guide when to use the tool. Though it doesn't mention when not to use or list alternatives, the examples are clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_category_trendSee Category Pricing TrendARead-onlyInspect
Report the 30-day Amazon price-trend direction for a CPG category.
Use when a pricing ops lead asks whether category pricing is rising, stable, or falling — e.g. setting retail promo calendar against an Amazon backdrop, deciding whether to raise wholesale prices during inflationary windows, or catching a price war before it spills into their channel.
Returns: trend_direction (Rising / Stable / Falling / Insufficient Data), trend_window ("30 days"), confidence (note with product count), category (resolved name), last_refreshed, cta.
Args: category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and safe operation. The description adds return field details (trend_direction, confidence, etc.), but doesn't disclose behavioral nuance beyond what annotations imply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise, well-structured with a lead sentence, usage guidance, return fields, and args. No superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool (1 param, no output schema), the description covers return fields, usage context, and parameter details adequately. Missing output schema is mitigated by listing fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by specifying exact valid category names and casing hint, adding meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports the 30-day Amazon price-trend direction for a CPG category, with specific verb and resource. It distinguishes from siblings like get_category_overview and get_price_position by focusing on trend direction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly gives use cases (e.g., pricing ops lead asking about trend direction, promo calendar, price wars). While it doesn't say when not to use or name alternatives, the context is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_price_positionCheck Competitive Price PositionARead-onlyInspect
Percentile-rank a single product price against tracked Amazon competitors in a CPG category.
Use when a multi-channel CPG brand asks where their Amazon listing price sits against 100+ tracked products — e.g. checking whether a $4.99 granola is competitively positioned on Amazon, auditing whether a retail MSRP is reasonable against Amazon reality before a buyer meeting, or sanity-checking a wholesale-to-retail markup.
Returns: percentile_rank (string, e.g. "72nd percentile"), price_index_label (ratio vs. category median), position (Value / Parity / Premium), category (resolved name), last_refreshed (ISO timestamp), cta (provenance note).
Args: price: Product price in dollars (e.g. 4.99). Must be > 0 and <= 10000. category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive. Call list_categories first to confirm available names.
| Name | Required | Description | Default |
|---|---|---|---|
| price | Yes | ||
| category | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and description adds return fields, price constraints, and category requirements. Could detail error responses or processing limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Approximately 6 sentences covering function, use cases, returns, and args with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, description lists all return fields and explains args fully for a simple two-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, description adds thorough explanations: price must be >0 and <=10000, category with exact names and case-insensitive, and reference to list_categories.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it percentile-ranks a product price against Amazon competitors in CPG categories, with specific verb and resource. It distinguishes from siblings like compare_products and list_categories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use when...' with concrete scenarios, and advises to call list_categories first for valid category names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesList Available CategoriesARead-onlyInspect
List Amazon CPG categories with current product counts and trend direction.
Use as the first call in any pricing-analysis workflow — returns the exact category names expected by other tools, plus product count and trend for each. Lightweight; safe to call before any category-specific query.
Returns: categories (list of {name, product_count, trend_direction, last_refreshed}), note (summary of coverage), cta.
Covers Grocery & Gourmet Food, Health & Beauty, Household, and Pet Supplies.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral details like 'Lightweight; safe to call' and describes the expected output structure, which provides useful context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, well-structured, and front-loaded with the main purpose. It includes a usage note and expected output, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and good annotations, the description is complete. It explicitly lists the return fields and coverage scope, which compensates for the lack of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so schema coverage is 100%. The description does not need to explain parameters, and the baseline score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it 'List Amazon CPG categories with current product counts and trend direction.' The verb 'list' and resource 'categories' are specific. It distinguishes itself from siblings by noting it's the first call in a pricing-analysis workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states 'Use as the first call in any pricing-analysis workflow' and 'safe to call before any category-specific query,' providing clear context. However, it does not explicitly mention when not to use it or contrast with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
server_statusCheck Server StatusARead-onlyInspect
Report PricePilot server health, data freshness, and degraded-state reason.
Use to check whether category seeding is current (staleness threshold is 10 days) before trusting downstream tool output. Returns degraded status with reason if data is overdue; healthy otherwise.
Returns: server (name), version, status (healthy / degraded), categories_available, data_freshness (ISO timestamp of last seed), degraded_reason (null if healthy).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false. The description adds value by detailing the return values and the condition for degraded state (data overdue with reason). It also provides the staleness threshold. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with a brief paragraph followed by a clear list of return fields. Every sentence adds value: purpose, usage case, and output details. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and adequate annotations, the description fully covers the tool's behavior. It explains what the tool returns, how to interpret status, and the staleness threshold. This is complete for a health check tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has zero parameters, so baseline score is 4 per guidelines. Description appropriately omits parameter details since none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool reports PricePilot server health, data freshness, and degraded-state reason. The verb 'report' and resource are explicit. It distinguishes itself from sibling tools by emphasizing its role as a prerequisite for trusting downstream outputs, implying it's a health check tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: 'Use to check whether category seeding is current... before trusting downstream tool output.' This tells the agent when to use it, including a specific staleness threshold of 10 days. It could be improved by explicitly stating when not to use it or mentioning alternatives.
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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