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Glama

Server Details

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

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

Glama MCP Gateway

Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.

MCP client
Glama
MCP server

Full call logging

Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.

Tool access control

Enable or disable individual tools per connector, so you decide what your agents can and cannot do.

Managed credentials

Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.

Usage analytics

See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.

100% free. Your data is private.
Tool DescriptionsA

Average 4/5 across 30 of 30 tools scored. Lowest: 3.1/5.

Server CoherenceA
Disambiguation3/5

The tools cover distinct aspects of shopping intelligence, but there is significant overlap in purpose. For example, 'smart_shopping_session', 'get_category_recommendations', and 'get_constraint_match' all aim to provide product recommendations, which could confuse an agent about which to use. Descriptions help clarify, but the boundaries between tools like 'detect_deal' and 'get_todays_deals' are not entirely clear, leading to potential misselection.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (e.g., 'add_to_wishlist', 'check_price_alerts', 'get_product_intelligence'), with clear and descriptive names. However, there are minor deviations like 'smart_shopping_session' (adjective_noun_noun) and 'agent_signal_status' (noun_noun_noun), which slightly break the pattern but do not severely impact readability.

Tool Count2/5

With 30 tools, the set feels overly large and heavy for the shopping intelligence domain. Many tools have overlapping functionalities (e.g., multiple recommendation and analysis tools), suggesting redundancy. A more streamlined set of 10-15 tools could cover the same scope without overwhelming an agent, making the current count excessive.

Completeness5/5

The tool surface is highly complete for the shopping intelligence domain, covering the full lifecycle from session initiation ('smart_shopping_session') to outcome logging ('log_outcome'), with comprehensive analysis, recommendation, and monitoring tools. There are no obvious gaps; tools like 'import_completed_session' even allow retroactive data contribution, ensuring robust coverage.

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