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Collective intelligence for AI shopping agents — product intel, deals, and more

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
dan24ou-cpu/agent-signal
GitHub Stars
0
Server Listing
agent-signal

See and control every tool call

Log every tool call with full inputs and outputs
Control which tools are enabled per connector
Manage credentials once, use from any MCP client
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Available Tools

12 tools
check_merchant_reliabilityInspect

Check a merchant's reliability based on data from other AI agents' shopping sessions. Returns selection rate, stock reliability, match scores, and purchase outcomes. Use this to decide whether to trust a merchant's listings before recommending their products.

ParametersJSON Schema
NameRequiredDescriptionDefault
merchant_idYesMerchant identifier to look up, e.g. 'amazon', 'bestbuy'
detect_dealInspect

Compare a product's current price against historical price data from all agents. Returns a verdict (best_price_ever, great_deal, good_deal, fair_price, above_average), savings vs average, and which merchants typically have the best prices. Use this before recommending a purchase to flag deals or overpricing.

ParametersJSON Schema
NameRequiredDescriptionDefault
product_idYesProduct identifier, e.g. 'sony-wh1000xm5'
current_priceYesThe price you're seeing right now
get_category_recommendationsInspect

Get intelligence about a product category from other AI agents' shopping sessions. Returns which products agents are selecting most, what decision factors matter, common requirements, and average budgets. Use this when starting a shopping task to understand what's working well in a category.

ParametersJSON Schema
NameRequiredDescriptionDefault
categoryYesProduct category, e.g. 'footwear/running', 'electronics/headphones'
budget_maxNoOptional budget ceiling to filter recommendations
get_constraint_matchInspect

Decision shortcut: find what products worked for agents with your EXACT constraints. Matches on specific requirements (e.g. 'wide fit + cushioned + under $150') and returns what those agents selected, why, and where to buy. Also returns broader recommendations from sessions with overlapping constraints. Use this to skip the search when a proven answer already exists.

ParametersJSON Schema
NameRequiredDescriptionDefault
categoryYesProduct category, e.g. 'footwear/running'
budget_maxNoMaximum budget
constraintsYesRequired attributes, e.g. ['wide fit', 'cushioned']
get_product_intelligenceInspect

Get crowdsourced intelligence about a product from other AI agents' shopping sessions. Returns selection rate, common rejection reasons, which competitors beat it and why, price ranges seen, and outcome data. Use this before recommending a product to understand how other agents have evaluated it.

ParametersJSON Schema
NameRequiredDescriptionDefault
product_idYesProduct identifier to look up, e.g. 'sony-wh1000xm5'
get_session_summaryInspect

Retrieve a full summary of a shopping session including all product evaluations, comparisons, and the final outcome. Useful for reviewing what happened during a session.

ParametersJSON Schema
NameRequiredDescriptionDefault
session_idYesSession ID to retrieve
get_similar_session_outcomesInspect

Cross-agent learning: see what other AI agents chose when shopping for similar items with similar constraints. Returns which products were selected most often, what deciding factors mattered, and how sessions ended (purchased vs abandoned). Use this at the START of a shopping task to leverage collective agent intelligence.

ParametersJSON Schema
NameRequiredDescriptionDefault
categoryYesProduct category, e.g. 'footwear/running'
budget_maxNoMaximum budget to filter similar sessions
constraintsYesShopping constraints, e.g. ['lightweight', 'cushioned', 'wide fit']
get_warningsInspect

Check for recent problems with a product or merchant before recommending them. Surfaces stock issues, high rejection rates, and abandonment signals from other agents. Returns warnings with severity levels (critical, warning, info) or all_clear if safe. Use this as a safety check before finalizing a recommendation.

ParametersJSON Schema
NameRequiredDescriptionDefault
product_idNoProduct to check for warnings
merchant_idNoMerchant to check for warnings
log_comparisonInspect

Log a comparison between two or more products during a shopping session. Call this when the agent explicitly compares products to decide between them. Record which product won and what the deciding factor was.

ParametersJSON Schema
NameRequiredDescriptionDefault
session_idYes
deciding_factorYesThe primary factor that decided the winner
products_comparedYesProduct IDs being compared
winner_product_idYesThe product that won the comparison
dimensions_comparedNoDimensions compared, e.g. ['price', 'reviews', 'durability']
log_outcomeInspect

Log the final outcome of a shopping session. Call this when the shopping task ends — whether the user purchased, received a recommendation, abandoned, or deferred the decision.

ParametersJSON Schema
NameRequiredDescriptionDefault
reasonNoWhy this outcome occurred
session_idYes
outcome_typeYes
product_chosen_idNoProduct ID if purchased or recommended
log_product_evaluationInspect

Log that a product was evaluated during a shopping session. Call this for each product the agent considers, whether it's selected, rejected, or shortlisted. Include the rejection reason if the product was rejected.

ParametersJSON Schema
NameRequiredDescriptionDefault
in_stockNo
product_idYesProduct identifier
session_idYesSession ID from log_shopping_session
dispositionYesWhether the product was selected, rejected, or shortlisted
match_scoreNoHow well the product matches intent (0-1)
merchant_idNoMerchant/retailer identifier
match_reasonsNoWhy this product was a match
price_at_timeNoPrice at time of evaluation
rejection_reasonNoWhy the product was rejected
log_shopping_sessionInspect

Start a new shopping session by logging the user's shopping intent. Call this at the beginning of any shopping task to capture what the user wants. Returns a session_id to use with subsequent log calls.

ParametersJSON Schema
NameRequiredDescriptionDefault
giftNoWhether this is a gift purchase
urgencyNostandard
categoryNoProduct category, e.g. 'footwear/running'
raw_queryYesThe user's original shopping request
budget_maxNoMaximum budget amount
exclusionsNoExcluded brands or features, e.g. ['Nike']
constraintsNoRequired attributes, e.g. ['wide fit', 'cushioned']
agent_platformNoAgent platform identifierunknown
budget_currencyNoBudget currency codeUSD

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{
  "$schema": "https://glama.ai/mcp/schemas/connector.json",
  "maintainers": [
    {
      "email": "your-email@example.com"
    }
  ]
}

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