get_llms_index
Fetch https://slate.electrik.dev/llms.txt — the curated AI index for Slate.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Fetch https://slate.electrik.dev/llms.txt — the curated AI index for Slate.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 full behavioral transparency burden. Although 'Fetch' implies a read-only GET operation, it does not explicitly state side-effect freedom, potential errors, rate limits, or the nature of the response. The description lacks details about what the tool actually does beyond the URL.
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 concise sentence that communicates the essential action and resource. There is no fluff or redundancy, making it highly efficient.
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 description is brief and mentions that the resource is a curated AI index for Slate, but it lacks context about the index's purpose, content structure, or expected output. Since there is no output schema, the description should offer more insight into what the tool returns, which it does not. This leaves some gaps in completeness.
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 tool has zero parameters, so the schema fully covers parameter semantics. The description adds no parameter-related information, but none is needed. According to the baseline rule for 0 parameters, the score is 4.
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 it fetches a specific URL and identifies it as a curated AI index for Slate. It distinguishes from sibling tools that focus on components or docs. However, it does not explain what 'Slate' is, leaving some ambiguity about the resource's context.
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 guidance on when to use this tool versus alternatives. It does not mention scenarios where fetching the index is preferred, nor does it indicate any prerequisites or fallback options. This leaves the agent without clear selection criteria.
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 clearly distinct: component docs, component source, docs pages, and index browsing each target a specific need. The only potential confusion is between get_component_docs and get_docs_page, since component docs are a subset of docs pages, but the descriptions clarify the intended usage.
All tool names follow a consistent verb_noun pattern (get_* and list_*), with clear and predictable object nouns. This makes it easy to infer what each tool does before reading its description.
Six tools is well-scoped for a documentation and component lookup server. Each tool covers a distinct need—listing, fetching docs, fetching source, and browsing pages—without unnecessary duplication.
The core workflows of exploring components, fetching component docs, and retrieving source code are well covered. A minor gap is that blocks can only be listed, with no corresponding block detail or source tool, and there is no search capability beyond the llms.txt index.