aware_search
AWARE — free text or code.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | No |
AWARE — free text or code.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavior such as side effects, output, authentication, rate limits, or matching semantics. The description discloses none of these; it only hints at accepted input format.
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 under-specified rather than concisely complete. It front-loads an input hint but lacks a clear subject-verb-object statement of purpose, and the brevity omits information necessary for correct use.
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?
An agent given this definition cannot determine what AWARE is, what the tool returns, or how search behavior differs from aware_get and the sibling search tools. With no annotations and no output schema, the description must supply this context and does not.
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?
The schema provides only a 'q' string with 0% description coverage. The phrase 'free text or code' gives a minimal hint that q accepts natural language or source code, but it does not explain query syntax, optionality, default behavior, or what kind of matching is performed.
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 names the resource ('AWARE') and hints at an input type ('free text or code'), but it contains no verb or explicit statement that this tool performs searches. The function is largely inferred from the tool name rather than stated, and it does not distinguish aware_search from aware_get or other sibling search tools.
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?
There is no guidance about when to use aware_search versus aware_get or any of the many sibling _search and _get tools. No alternatives, exclusions, or context conditions are mentioned, leaving the agent to guess the appropriate invocation scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are cleanly separated by entity prefix and action (_get vs _search), so AWARE/Box/etc are not easily confused. The main ambiguity is among spl_lab_auth, spl_agent_key, and spl_signup, which all relate to account/key creation and could cause misselection.
The 24 entity tools follow a consistent <entity>_get/<entity>_search pattern, which is predictable and readable. However, the five spl_* tools mix noun-style names like spl_catalog with verb-style names like spl_discover and spl_signup, creating a noticeable second convention.
At 29 tools, the server exceeds the comfortable 3-15 range and even the heavier 16-25 range. The 12 get/search pairs are systematic, but combined with the five spl_* tools the overall surface feels bloated and harder to scan.
For a retrieval-oriented knowledge server, every entity has both point lookup and free-text search, plus lab signup, agent key minting, and discovery/catalog resources. Minor gaps like no key revocation or list-all operations are not critical for the stated purpose.