Entyrax
Server Details
PC hardware decision engine for Brazil (pt-BR): compatibility, GPU recommendations, build analysis.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
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.
Tool Definition Quality
Average 3.9/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: full build analysis, pairwise compatibility check, and GPU recommendation. There is no overlap or ambiguity in their intended use cases.
All tools follow a consistent snake_case verb_noun pattern (analyze_build, check_compatibility, recommend_gpu_by_context), making them predictable and easy to differentiate.
With 3 tools, the set is well-scoped for the domain of PC build assistance. Each tool serves a distinct and necessary function without redundancy or unnecessary bloat.
While the core workflows (full build analysis, compatibility, GPU recommendation) are covered, there are notable gaps such as missing CPU or motherboard recommendations, and no tools for memory or storage. Some operations like PSU selection are hinted at but not fully exposed.
Available Tools
3 toolsanalyze_buildAnalisar build completoARead-onlyIdempotentInspect
Analisa um build CPU+Mobo+GPU. Retorna overall_quality, reasoning (why_this_mobo/why_this_gpu/budget_note), PSU recomendada e preços.
| Name | Required | Description | Default |
|---|---|---|---|
| cpu_slug | Yes | Slug da CPU (ex: ryzen-7-7800x3d) | |
| gpu_slug | Yes | Slug da GPU (ex: geforce-rtx-5070-ti) | |
| mobo_slug | Yes | Slug da placa-mãe (ex: gigabyte-b650-aorus-pro-ax) |
Output Schema
| Name | Required | Description |
|---|---|---|
| psu | No | PSU recomendada para o conjunto |
| reasoning | No | why_this_mobo, why_this_gpu, budget_note |
| overall_quality | No | Qualidade geral do build (ex: excellent, unbalanced) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavioral context by listing return fields (overall_quality, reasoning, PSU, prices), but does not disclose deeper behaviors (e.g., how compatibility is handled, whether it checks existence of slugs, or any side effects).
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 two sentences long: the first states the input, the second lists outputs. No extraneous information, front-loaded, and every word contributes value.
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 and annotations, the description adequately covers input and expected returns. However, it could be more complete by explicitly stating that compatibility is checked (implied by 'analisa') or mentioning any prerequisites (e.g., valid slug format). Still, it meets the essential needs for this simple tool.
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 100%; the input schema already describes each parameter with examples (e.g., 'ryzen-7-7800x3d'). The description adds no further meaning about the parameters beyond stating they are CPU, Mobo, and GPU slugs, which is redundant with the schema.
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 analyzes a CPU+Mobo+GPU build and returns specific outputs (quality, reasoning, PSU, prices). This distinguishes it from siblings like check_compatibility and recommend_gpu_by_context, which focus on compatibility and GPU recommendation respectively.
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 provides no explicit guidance on when to use this tool versus its siblings (check_compatibility, recommend_gpu_by_context). It does not mention when-not to use it or alternatives, leaving the agent to infer usage solely from the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_compatibilityVerificar compatibilidadeARead-onlyIdempotentInspect
Verifica compatibilidade e qualidade de pairing entre dois componentes (CPU+Mobo, CPU+GPU, GPU+Monitor). Retorna compatibility_status, pairing_quality, confidence e reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_a | Yes | Slug do primeiro componente (ex: ryzen-7-7800x3d) | |
| entity_b | Yes | Slug do segundo componente (ex: gigabyte-b650-aorus-pro-ax) | |
| pair_type | No | Tipo do par. Opcional; se omitido, tenta os três. |
Output Schema
| Name | Required | Description |
|---|---|---|
| reason | No | Justificativa em pt-BR |
| pair_type | No | cpu_mobo | cpu_gpu | gpu_monitor |
| compatible | No | Se o par funciona junto |
| confidence | No | 0.0–1.0 |
| pairing_quality | No | Ex: excellent, balanced, bottleneck_risk |
| compatibility_status | No | Ex: native_supported, needs_bios_update |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnlyHint=true and destructiveHint=false, so the description correctly focuses on output fields (compatibility_status, pairing_quality, confidence, reasoning). This adds useful context beyond annotations, but it does not elaborate on how results are computed or any edge cases, hence not a 5.
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 consists of two sentences: first sentence states function and scope, second sentence lists return fields. No redundant words, and the key information is front-loaded. Perfectly concise.
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 three parameters (two required), annotations covering safety, and an output schema (not shown but present), the description is adequate. It covers purpose, parameters implicitly via schema, and return fields. Could mention what happens for invalid inputs or unsupported pair types, but completeness is high overall.
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 description coverage is 100%, so the schema already fully documents all parameters. The description only reiterates the pair types and the optional behavior of pair_type. It adds no new semantic meaning beyond what the schema provides, so baseline 3 is appropriate.
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 starts with a clear verb 'Verifica' (checks) and specifies the resource 'compatibilidade e qualidade de pairing entre dois componentes'. It lists concrete examples (CPU+Mobo, CPU+GPU, GPU+Monitor), which clearly distinguishes it from siblings like 'analyze_build' (which likely analyzes entire builds) and 'recommend_gpu_by_context' (which recommends GPUs).
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 states the tool checks compatibility for specific pair types and lists them. It implies usage when an agent needs to verify compatibility between two components. However, it does not explicitly state when not to use it or compare with sibling tools, though the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_gpu_by_contextRecomendar GPU por contextoARead-onlyIdempotentInspect
Recomenda top GPUs dado contexto: resolução (1080p/1440p/4k), uso (gaming-aaa/gaming-competitive/produtividade) e orçamento em BRL.
| Name | Required | Description | Default |
|---|---|---|---|
| use_case | Yes | Uso principal da máquina | |
| budget_brl | Yes | Orçamento máximo em BRL (ex: 3500) | |
| resolution | Yes | Resolução alvo do monitor |
Output Schema
| Name | Required | Description |
|---|---|---|
| picks | No | GPUs recomendadas em ordem de adequação |
| reasoning | No | Resumo do porquê do ranking |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so no contradictions. Description adds input context but no additional behavioral traits beyond what is implied by annotations.
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?
Single sentence with no waste; front-loaded with purpose and key parameters.
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 that output schema exists, description need not explain return values. All required parameters are covered, and annotations are complete. Slightly incomplete regarding edge cases or fallback behavior, but adequate.
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 description coverage is 100%, so baseline is 3. Description adds minor context (e.g., BRL for budget) but does not substantially enhance meaning beyond schema.
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?
Description clearly states the tool recommends top GPUs given resolution, use case, and budget. It distinguishes itself from sibling tools (analyze_build, check_compatibility) by focusing on GPU recommendation.
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?
Description implies usage context by listing inputs, but does not explicitly state when to use or avoid this tool versus siblings. No alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!