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Glama

Server Details

Merchant verification for AI shopping agents.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
warwickwood-cell/gengeo-agent-registry
GitHub Stars
3
Server Listing
GenGEO

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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.

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Tool DescriptionsB

Average 3.4/5 across 1 of 1 tools scored.

Server CoherenceA
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion or overlap with other tools.

Naming Consistency5/5

With a single tool, naming is trivially consistent. The name 'verify_store' follows a clear verb_noun pattern.

Tool Count3/5

A single tool feels thin for a registry service, even if it's focused on verification. The scope is limited, but the tool count is borderline rather than extreme.

Completeness2/5

The server only provides a verification tool, lacking essential CRUD operations (create, update, delete, list) expected for a registry, leaving significant gaps.

Available Tools

1 tool
verify_storeVerify StoreBInspect

Verify whether a merchant has an active GenGEO verification record. Results should be treated as one signal within a broader agent decision framework.

ParametersJSON Schema
NameRequiredDescriptionDefault
domainYes
Behavior2/5

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

No annotations provided, so description carries full burden. It does not disclose behavioral traits such as idempotency, error handling, or whether it is read-only. The description merely states the verification, leaving the agent uninformed about side effects or limits.

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?

Two sentences, no fluff. First sentence states purpose, second adds usage context. Every word earns its place.

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?

Tool is simple with one required param and no output schema. Description covers the verification purpose but omits details on return value format or error conditions, which would be expected given no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not mention the 'domain' parameter at all. It adds no meaning beyond the schema, failing to compensate for the lack of parameter documentation.

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?

Description clearly states the tool verifies whether a merchant has an active GenGEO verification record, with a specific verb and resource. No siblings exist, so distinction is not needed.

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?

Explicitly states results should be treated as one signal within a broader framework, guiding the agent on how to use the output. No mention of when not to use, but the context is clear.

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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