AaaSagent Templates
Server Details
AaaSagent: browse agent templates (BYOK) and join the invite-only beta.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: list_templates discovers/filters templates, while get_template retrieves a specific template by path. The description explicitly frames get_template as a follow-up to list_templates, eliminating ambiguity.
Both names follow the same verb_noun convention (list_templates, get_template) with consistent snake_case. The pattern is predictable and easy to extend.
Two tools is on the thin side for a template server, even a read-only one. The pair is well-scoped and each earns its place, but the surface feels minimal rather than comfortably complete.
For a read-only template catalog, list plus get covers the core browsing and retrieval lifecycle, including license-aware behavior. Minor gaps exist around previewing restricted templates or metadata-only listing details, but agents can work around them.
Available Tools
2 toolsget_templateAInspect
Fetch one template by its path (as returned by list_templates) and get its full prompt text when the licence allows it. Use this after list_templates when the user wants to actually apply, compare or quote a specific template. Free/MIT templates return the complete text; restricted-licence ones return metadata plus the original repo link only.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | 模板路径,例如 marketing/aeo-specialist(来自 list_templates 的 path 字段) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden well by disclosing that license restrictions change the response: free/MIT templates return full text, while restricted ones return metadata plus a repo link. It does not cover error behavior for invalid paths, but the key conditional behavior is clearly stated.
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 three tightly written sentences with the core action first, usage context second, and license-dependent return behavior third. Every sentence adds useful information.
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?
For a simple one-parameter fetch tool with no annotations or output schema, the description gives enough context to call it correctly and explains the two possible return modes. No critical missing information prevents proper invocation.
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 documents the single path parameter with an example and its origin in list_templates. The description reinforces that the path comes from list_templates but does not add syntax or format details beyond 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 states a specific verb and resource: fetch one template by path. It also distinguishes this from list_templates by emphasizing 'one template' and noting that the path comes from list_templates.
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?
It explicitly says to use this after list_templates when the user wants to apply, compare, or quote a specific template. This names the sibling tool and the condition that selects this tool over listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_templatesAInspect
List open-source agent templates when the user asks for a reusable prompt, workflow or job template, or wants them filtered by department or keyword. Returns name, path and license status (free or restricted). Read-only, no authentication required.
| Name | Required | Description | Default |
|---|---|---|---|
| dept | No | 部门过滤(如 marketing / finance / customer-service) | |
| limit | No | 返回数量上限(默认 20) | |
| keyword | No | 关键词(中英文均可) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden and does so well by declaring 'Read-only, no authentication required' and enumerating the return fields (name, path, license status free/restricted). It stops short of describing result ordering or whether limit paginates beyond a simple cap, which an agent would want to know before calling with a large limit.
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?
Two sentences, front-loaded with purpose and followed by return shape and safety profile. No redundant restatement of the tool name and no filler; every clause adds information.
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?
For a simple three-parameter read tool with no output schema it covers purpose, filter triggers, return fields, and safety/auth prerequisites. The remaining gap is listing behavior at scale — whether results are sorted or capped beyond the default limit — which is minor but non-zero.
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 all three parameters are already documented, including the default of 20 for limit. The description adds only marginal value by restating department and keyword filtering and does not clarify matching semantics (e.g. whether keyword searches name, description, or tags), so the 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?
States a specific verb and resource ('List open-source agent templates') and immediately clarifies the scope: open-source, reusable prompt/workflow/job templates. There are no siblings to differentiate from, and the description also names the returned fields, so an agent knows exactly what this tool produces.
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?
Explicitly gives trigger conditions — the user asking for a reusable prompt/workflow/job template, or wanting results filtered by department or keyword. There are no alternative tools to route against, so no exclusions are possible; the only gap is the absence of any guidance on when this tool is *not* appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- Added
get_template
1 tool update
- First observed
list_templates
Related MCP Connectors
The cloud for agents. Tools for AI agents to register, build, and deploy other agents. Zero human required.
Agent-native security, trust, reliability, data and procurement tools for AI workflows.
Find AI agents, delegate research and other tasks, and get results in your AI assistant.
AI agent registry — search, discover, register, and connect agents via MCP.
Related MCP Servers
- AlicenseAqualityCmaintenanceOn-demand access to 150+ specialist AI agent templates — search, browse, and spawn agents. 150x reduction in context usage vs loading agents locally.442 npm1MIT
- AlicenseNot gradedqualityDmaintenanceAgent Vault is a local credential vault for AI agents. It enables agents to use secrets by name without ever seeing their values, with host allowlists, output scrubbing, and audit logging.4MIT
- AlicenseNot gradedqualityDmaintenanceZero-knowledge credential injection for AI agents. Your agent authenticates to websites and APIs without ever seeing a password, TOTP code, or API key.15 npm1MIT
- AlicenseAqualityDmaintenanceEnables deploying pre-built AI agent blueprints for RFx procurement and employee onboarding, with no signup or API key required.28 npmMIT
Glama MCP Gateway
Add one secure layer between your agents and this server.