surface_get
Surface — one entry with notes and cross-links.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Surface — one entry with notes and cross-links.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
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?
With no annotations, the description carries the burden of behavioral disclosure, but it only mentions that the result is 'one entry with notes and cross-links'. It does not describe error behavior, missing ids, authentication needs, or whether this is a read-only operation.
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 very short and front-loaded, but it is closer to under-specification than efficient clarity. It includes a useful hint about the return content, yet omits essential action and usage context.
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?
The tool has no output schema and no annotations, so the description should explain both invocation semantics and return shape. It partially describes the return as an entry with notes and cross-links, but leaves the id parameter, error cases, and relation to sibling tools unexplained.
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 0%, and the description does not mention the 'id' parameter at all. It provides no meaning beyond the raw schema field name, so the agent cannot learn what id refers to or what format is expected.
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 phrase 'one entry' suggests a singular retrieval and 'Surface' names the resource, so an agent can infer this is a getter. However, there is no explicit verb like 'get' or 'retrieve', and it does not clearly distinguish itself from surface_search beyond the implicit singular/plural contrast.
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 gives no guidance on when to use this tool versus alternatives such as surface_search or other *_get tools. It does not state whether to use it after a search, for direct id lookup, or when a single surface entry is needed.
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.