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AisleNode Local Commerce (Meta Muse & Claude MCP)

Server Details

Connect Meta Muse and Claude agents to live local retail store shelves and direct checkout.

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Status
Healthy
Last Tested
Transport
Streamable HTTP · MCP 2024-11-05
URL
Repository
marvo-bd/muse-commerce-mcp
GitHub Stars
0
Server Listing
muse-commerce-mcp

TDQS

A4/5.0

Scored across 4 tools

Disambiguation5/5

Each tool targets a clearly distinct action within the local commerce flow: catalog search, order creation, order status checking, and store reporting. There is no overlap in purpose, and the descriptions reinforce the boundaries between discovery, purchasing, fulfillment tracking, and trust-and-safety.

Naming Consistency5/5

All four tools use a consistent snake_case verb_noun pattern (check_order_status, create_order, report_store, search_catalog). The slightly longer check_order_status still follows the same convention and remains readable.

Tool Count4/5

Four tools is on the lean side but well-scoped for the core agent workflow of finding local inventory, placing an order, checking status, and reporting bad stores. It is not bloated, though a slightly broader surface might better support common adjacent tasks.

Completeness4/5

The primary commerce lifecycle is covered: search inventory, create an order, and check fulfillment status, plus a trust-and-safety reporting path. Minor gaps exist around order cancellation, order history/listing, and richer store detail retrieval, but agents can work around these for the stated purpose.

Available Tools

4 tools
check_order_statusAInspect

Check live fulfillment, payment, and courier dispatch status for an existing local retail order.

NameRequiredDescriptionDefault
order_idYesUUID of the order to inspect

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that this is a live status check across three operational domains, implying a read-only diagnostic operation, but it does not state permissions, rate limits, caching behavior, or whether any state is changed.

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?

A single front-loaded sentence with zero wasted words. The action and status domains are stated immediately.

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?

For a simple one-parameter read tool, the description is nearly complete: it identifies the input context and the status domains inspected. It does not describe the response shape, but with no output schema the description could optionally say more; the remaining gap is minor.

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?

The single order_id parameter is fully documented in the schema as a UUID. The description adds contextual scope ('existing local retail order') but no additional format, source, or example details beyond what the schema provides, so the baseline of 3 is appropriate.

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 uses a specific verb, 'Check,' and a specific resource, order status, then enumerates the status domains: fulfillment, payment, and courier dispatch. It clearly distinguishes this read-only status lookup from sibling tools like create_order, search_catalog, and report_store.

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 'for an existing local retail order' establishes the context and prerequisite that the order must already exist. It does not explicitly name alternatives or when-not-to-use cases, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_orderAInspect

Reserve in-stock items at a local physical store and generate a zero-PII, non-custodial checkout URL. The shopper completes payment and delivery details directly on the verified merchant checkout page.

NameRequiredDescriptionDefault
itemsYesArray of products and quantities to reserve
store_idYesUUID of the verified physical store
agent_sourceNoIdentifier of the calling AI agent (e.g. "meta_muse", "claude_desktop")meta_muse

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It helpfully reveals that the checkout URL is zero-PII and non-custodial, and that payment and delivery details are handled on the merchant page. However, it omits important operational details such as what happens if items are out of stock, reservation expiration, idempotency, or authentication requirements.

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?

The description is two sentences, front-loaded with the core action and result, with no wasted words. Every sentence earns its place by clarifying the tool's purpose and the checkout flow.

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?

Given the absence of an output schema and annotations, the description does well to explain that a checkout URL is generated and that the shopper completes payment elsewhere. It covers the essential flow but could be slightly more complete by mentioning expected return format or error conditions.

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 description coverage is 100%, so all parameters (store_id, items, agent_source) are already documented in the schema. The description adds no parameter-specific meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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 specific verbs and resources: 'Reserve in-stock items at a local physical store' and 'generate a zero-PII, non-custodial checkout URL.' This clearly distinguishes the tool from siblings like search_catalog (search), check_order_status (status check), and report_store (reporting). An agent can immediately understand the tool's function without opening the schema.

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 description implies usage context ('Reserve in-stock items at a local physical store') and the flow (shopper completes payment on merchant page), but it does not explicitly state when to use this tool versus alternatives or any preconditions. However, the context is clear enough for an agent to infer appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

report_storeAInspect

Submit an autonomous trust-and-safety violation report if a store exhibits deceptive pricing, phantom stock, or invalid coordinates.

NameRequiredDescriptionDefault
detailsYesDetailed explanation of the discrepancy observed by the agent or user
categoryYesViolation category
store_idYesUUID of the store being reported
agent_sourceNoIdentifier of the reporting AI agentmeta_muse

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It says the report is 'autonomous' but never states what happens after submission—whether it is filed immediately, whether duplicates are rejected, whether the agent needs confirmation, or what permissions are required for a write operation against a store.

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?

A single front-loaded sentence with the verb first and the qualifying conditions immediately after; no filler and nothing repeated from the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with zero annotations and no output schema, the description covers selection adequately but leaves the consequence side thin—the agent cannot tell what a successful submission returns or whether the report can be amended. The rich 100%-covered schema offsets some of that gap, keeping it at minimum-viable-plus.

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 description coverage is 100%, so store_id, category, details, and agent_source are already documented in the schema. The description's examples hint at which categories apply but add no syntax, format, or guidance for the details field or the agent_source default beyond what the schema supplies, so the baseline 3 is 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?

Names a specific verb ('Submit') and resource ('trust-and-safety violation report') and enumerates the triggering conditions (deceptive pricing, phantom stock, invalid coordinates), which map onto the enum categories. The action is unmistakably distinct from the transactional siblings search_catalog, create_order, and check_order_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?

The conditional clause 'if a store exhibits...' gives an explicit trigger for when to invoke it, which is more than most descriptions offer. It stops short of stating when not to use it (e.g., prefer other categories, or avoid duplicate filings) or naming any alternative route, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_catalogAInspect

Search real-time physical retail store inventories near a shopper’s GPS location. Returns verified brick-and-mortar stores, in-stock products, unit prices, and Haversine distance in kilometers.

NameRequiredDescriptionDefault
limitNoMaximum number of matching products to return (default: 10)
queryYesProduct name, category, or keyword (e.g. "coffee", "milk", "usb cable")
customer_latNoShopper latitude coordinate for proximity ranking (optional)
customer_lngNoShopper longitude coordinate for proximity ranking (optional)
max_distance_kmNoMaximum search radius in kilometers (default: 15)

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the disclosure burden. It usefully states that results are real-time, verified brick-and-mortar stores with in-stock products, unit prices, and Haversine distance, though it omits operational details such as rate limits, auth needs, or pagination behavior.

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?

The description is two efficient sentences with the core purpose front-loaded and no wasted wording. It immediately communicates the search scope before listing return details.

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 a fully described schema but no output schema, the description adequately explains the tool's purpose and return contents. A small gap remains around how proximity ranking behaves when optional customer_lat and customer_lng are omitted.

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 description coverage is 100%, so all five parameters are already documented in the input schema. The description reinforces the GPS-proximity nature of the search but adds no syntax or format detail beyond what the schema provides, making the baseline 3 appropriate.

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: searching real-time physical retail store inventories near a shopper's GPS location. It clearly distinguishes this product/store search from sibling tools such as create_order and check_order_status.

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?

The description implies the use case through 'near a shopper's GPS location' and 'physical retail store inventories,' but it does not explicitly say when to use this tool versus alternatives or when not to use it. No sibling tool is named as a contrast.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updates
    • First observedcheck_order_status
    • First observedcreate_order
    • First observedreport_store
    • First observedsearch_catalog

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