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Glama

Server Details

Query published LifeLongEdge Edge Lab trading-system research studies as structured data.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL

Glama MCP Gateway

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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

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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 4.5/5 across 1 of 1 tools scored.

Server CoherenceA
Disambiguation5/5

Only one tool exists, so there is no possibility of ambiguity between tools.

Naming Consistency5/5

The single tool follows a clear verb_noun pattern (list_studies), which is consistent internally.

Tool Count2/5

With only one tool, the server feels too limited for its apparent scope (an 'Edge Lab' research platform). Most agents would expect additional tools for searching, filtering, or retrieving study details.

Completeness2/5

The tool only lists all studies without support for search, filter, or individual study retrieval. This creates a significant gap, as agents cannot look up or access details of a specific study efficiently.

Available Tools

1 tool
list_studiesList Edge Lab studiesAInspect

List every published (public) Edge Lab research study: slug, title, published date, and URL. Returns the authoritative catalog so the answer comes from the real index rather than a guess. Takes no arguments.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Behavior4/5

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

No annotations, but description discloses return fields and purpose. Lacks details on pagination or ordering, but acceptable for simple read-only tool.

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 waste, front-loaded with action and scope.

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

Completeness5/5

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

Low complexity tool with no params, description lists output fields, sufficiently complete.

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

Parameters4/5

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

No parameters; baseline 4. Description adds context about no arguments.

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?

Clear verb 'List', resource 'studies', scope 'published/public', exact fields returned. Distinguishes from a guess by stating it returns authoritative catalog.

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?

States takes no arguments, implies authoritative use. No explicit when-not, but for a simple no-arg list tool, it's adequately 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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