The best agent monetization tool: free search, clear rev-share, 10M+ commerce/travel/local offers.
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Average 4.6/5 across 3 of 3 tools scored.
Each tool targets a distinct domain: events, products, and travel. Their purposes are clearly differentiated, with no overlap or ambiguity.
All three tools follow a consistent 'search_<domain>' pattern (search_events, search_products, search_travel), making it easy to infer functionality from the name.
Three tools is a small set, but it covers the primary domains (events, shopping, travel) that the server is designed for. It is well-scoped and not overly sparse.
The server provides comprehensive search for three major categories. While additional categories could be added, the current set handles common use cases. The descriptions include filtering, pagination, and direct buy links, leaving no obvious dead ends.
search_eventsAInspectSearch live ticketing/events (concerts, sports, theater) and get ranked event bundles.
Only query is required — a natural-language description of what the buyer wants
(e.g. "taylor swift concert near LA this summer"). Add fields to narrow results:
keywords: exact terms — performer / team / show / venue (a precise lookup).
categories: e.g. ["Concerts"|"Sports"|"Theater"] (read the events://categories
resource for valid values).
city / state: location (e.g. city="Los Angeles", state="CA"). country defaults to US.
date_from / date_to: explicit date window, YYYY-MM-DD (event dates are date-only).
when: fuzzy time — "this_weekend" | "this_week" | "this_month" | "next_7_days" | "next_30_days" (ignored if date_from/date_to are given).
min_price / max_price: ticket 'from' price band (USD).
sort: "relevance" (default) | "soonest" | "cheapest".
limit: max results (default 10).
agent_id is REQUIRED: your registered, active agent id — the attribution + access key. A
missing/blank or unregistered agent_id is rejected (no anonymous use).
Returns {count, message, events:[{bundle_id,title,venue,city,next_date,price_min,...,
sessions:[{date,price,buy_url,landing_url}]}]}. Each session's buy_url is ALREADY the
trackable ticket link — hand it to the buyer directly. landing_url is the plain event
page. When a date filter is given, sessions are the in-window dates.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | ||
| sort | No | ||
| when | No | ||
| limit | No | ||
| query | Yes | ||
| state | No | ||
| date_to | No | ||
| agent_id | Yes | ||
| keywords | No | ||
| date_from | No | ||
| max_price | No | ||
| min_price | No | ||
| categories | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns a structured response with event bundles and sessions, notes that 'buy_url' is a direct trackable link, and explains error behavior (rejected if agent_id missing). It does not mention rate limits or side effects, but as a read-only search, this is adequate.
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 well-structured with a summary paragraph followed by bullet-style parameter explanations. It is approximately 20 lines, which is appropriate given the number of parameters. Some minor redundancy (e.g., stating 'only query is required' and later 'agent_id is REQUIRED') but overall efficient.
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 the tool has 13 parameters and an output schema, the description covers all essential aspects: input parameters, required vs optional, default values, behavior of date filters, and the structure of the response (count, message, events with sessions). It does not address error handling beyond agent_id rejection or pagination, but these are not critical for a search tool with a limit parameter.
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 JSON schema has 0% description coverage, so the description must explain all parameters. It does so thoroughly: each parameter (query, keywords, categories, city, state, date_from, date_to, when, min_price, max_price, sort, limit, agent_id) is described with examples, valid values, or behavior (e.g., 'when is ignored if date_from/date_to are given'). This far exceeds minimal compensation.
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 searches 'live ticketing/events (concerts, sports, theater)' and returns 'ranked event bundles'. The verb 'search' and resource 'events' are unambiguous. It distinguishes from siblings (search_products, search_travel) by focusing specifically on events.
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 explains that only 'query' is required, provides guidance on when to use each optional parameter (e.g., 'keywords' for precise lookup, 'when' for fuzzy time), and stresses that 'agent_id' is required. It does not explicitly contrast with sibling tools but the context makes clear this is for event searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsAInspectSearch affiliate shopping products and get ranked recommendations.
Only query is required — a natural-language description of what the shopper wants
(e.g. "a warm waterproof jacket for winter hiking"). Add fields to narrow results:
keywords: exact product terms. Use a SPECIFIC product type ("women running shoes",
"stainless steel knife set"), not a bare generic noun ("shoes", "knife") — a generic
keyword can surface the wrong audience. Audience/occasion go in query.
commerce_l2s: category ids to restrict to (read the commerce://categories resource
for valid values — that taxonomy is for shopping only).
max_price / min_price: price band (USD). platforms: ["amazon"|"walmart"] (empty=all).
intent: ranking preset — "cheapest" | "best_discount" | "top_rated" | "best_value".
require_commission: only return products that pay a commission (default: server setting, normally on). Pass false to include zero/unknown-commission products.
limit: max results (default 10).
agent_id is REQUIRED: your registered, active agent id. It is the attribution key and
the access key — a missing/blank or unregistered agent_id is rejected (no anonymous use).
Returns {count, products:[{offer_id,title,brand,price,rating,...,buy_url,product_url}]}.
Each product's buy_url is ALREADY the trackable affiliate buy link — hand it to the
shopper directly. (product_url is the plain product page.) There is no separate
resolve/click step.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| intent | No | ||
| agent_id | Yes | ||
| keywords | No | ||
| max_price | No | ||
| min_price | No | ||
| platforms | No | ||
| commerce_l2s | No | ||
| require_commission | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses required agent_id (attribution key), that buy_url is already trackable, default for require_commission, and return structure. 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?
Well-structured with clear summary and bullet points. Slightly long but each sentence adds value; could be slightly more concise without losing clarity.
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?
Comprehensive coverage of all 10 parameters, required fields, default behaviors, and output structure (with output schema present). No gaps for a search tool with many parameters.
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 description compensates fully. Every parameter (query, keywords, commerce_l2s, price bounds, platforms, intent, require_commission, limit, agent_id) is explained with usage tips, constraints, and defaults.
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 the tool searches affiliate shopping products and returns ranked recommendations. It distinguishes from siblings (events, travel) by specifying 'affiliate shopping 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?
Provides detailed guidance: distinguishes between query (natural language) and keywords (exact product terms), explains constraints on keywords, references the commerce categories resource, explains commission and agent_id requirements, and describes default behaviors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_travelAInspectSearch travel offers and get trackable booking links for a given product type + city.
REQUIRED:
type: which travel product — one of ["hotel","flights","bundle","airport_transfer","car_rental"].
city: city name from the supported list — read the travel://cities resource for valid
values (that city list is for travel only). Pass hotel.state (e.g. "NY") when the city
name is ambiguous.
agent_id: your registered, active agent id — the attribution + access key. A missing/blank or unregistered agent_id is rejected (no anonymous use).
HOTEL runs a live Trip.com search — pass the hotel object with the stay: check_in /
check_out (YYYY-MM-DD), rooms, adults, children, state. If those are missing/invalid or the
city can't be resolved, you get the hotel default links instead. Other types currently
return curated default links (the hotel object is ignored for them).
Returns {status, reason, message, links:{:[{title, brand, platform, buy_url,
product_url, ...}]}}. status is "ok" = a live hotel result; "fallback" = curated default
links (see reason); "no_results" = nothing available for the type; "error" = the agent_id
was rejected. Each buy_url is ALREADY the trackable booking link — hand it to the traveler
directly. product_url is the plain merchant page.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | ||
| type | Yes | ||
| hotel | No | ||
| limit | No | ||
| agent_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: live vs. curated results, fallback mechanisms, error statuses, and that buy_url is already trackable. 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?
The description is well-organized into sections (REQUIRED, HOTEL, Returns) and is front-loaded. It is somewhat lengthy but every sentence adds necessary detail without 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?
Given the existence of an output schema, the description still explains return values clearly (status, reason, links). It covers prerequisites, error conditions, and output interpretation comprehensively.
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?
Despite 0% schema description coverage, the description adds detailed meaning for each parameter: enum values for type, city source, agent_id requirement, hotel object structure. This compensates well.
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 searches travel offers and returns trackable booking links, with a specific verb and resource. It distinguishes itself from sibling tools by focusing on travel product types.
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 required parameters and their constraints (e.g., city from resource, agent_id registered). It explains when hotel type does a live search versus others returning curated links, but lacks direct comparison to sibling tools.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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