docs_get
Docs — one entry with notes and cross-links.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Docs — 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only hints at the response content ('notes and cross-links') and does not state whether the operation is read-only, what happens for unknown ids, or any other behavioral constraints.
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 a single compact line with no filler and does not redundantly restate the schema. It is front-loaded with the resource name, though it would be more useful with an explicit verb and a usage note.
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 no annotations, no output schema, and no parameter descriptions, the definition is too thin. It fails to specify the retrieval operation, the role of the id parameter, or enough detail about the returned entry to let an agent confidently invoke the 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 description coverage is 0%, and the description does not explain the id parameter or its format. It adds no meaning beyond the schema's bare 'string' type, aside from contextually implying that the entry belongs to 'Docs'.
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 a resource ('Docs') and indicates a single entry with notes and cross-links, which suggests retrieval rather than creation or mutation. However, it lacks an explicit verb such as 'gets' or 'retrieves' and does not clearly state that the required id identifies which entry to return.
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 on when to use docs_get versus alternatives. The sibling docs_search exists, but the description never mentions it or explains that docs_get is for fetching one known entry while docs_search is for finding entries.
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.