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Glama
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Real engineering memory marketplace for autonomous AI agents. Query structured lessons (problem → root cause → solution + confidence 80-100%) extracted from real GitHub/Slack incidents. Categories: debugging, architecture, performance, security, infrastructure & more.

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Healthy
Last Tested
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Streamable HTTP
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Tool Definition Quality

Score is being calculated. Check back soon.

Available Tools

1 tool
query_lessonsAInspect

Search Younanix engineering lessons by natural language query. Returns problem summaries with category, confidence, and tags. Full lesson details (root cause, lesson learned, evidence) require the paid agent-query endpoint.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query
max_resultsNoMaximum number of results to return (default 10)
Behavior3/5

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

Without annotations, description carries burden of disclosure. Adds valuable return value structure ('problem summaries with category, confidence, and tags') since no output schema exists. However, omits explicit safety profile (read-only status), rate limits, or auth requirements that annotations would typically cover.

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?

Three sentences with zero waste: purpose declaration, return value specification, and capability limitation. Front-loaded with the core action and appropriately sized for the tool's simplicity.

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

Completeness4/5

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

Appropriately complete for a 2-parameter tool. Compensates for missing output schema by describing return fields. Mentions domain-specific context ('Younanix engineering'). Minor gap: could note behavior on empty results or pagination beyond the max_results parameter.

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

Parameters3/5

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

Schema coverage is 100%, establishing baseline. Description mentions 'natural language query' which aligns with the schema but adds no additional syntax guidance, examples, or constraints beyond what the schema already documents for the two parameters.

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?

Specific verb ('Search') and resource ('Younanix engineering lessons') clearly stated. Distinguishes scope from the 'paid agent-query endpoint' mentioned for full details, effectively differentiating this tool's capabilities despite no siblings on this server.

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?

Implies usage scope by stating what the tool returns (summaries) versus what requires the paid alternative (full details including root cause). Lacks explicit 'when to use' statement but provides clear boundary guidance through the comparison.

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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