Fetch a docs markdown page by path (e.g. getting-started/installation, livewire, ai).
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Docs path without leading docs/, e.g. getting-started/installation or design-tokens |
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. It states 'Fetch' implying a read-only operation, but provides no additional details such as output format, error handling, or any side effects. For a simple read operation, this is adequate but not enriched.
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 is front-loaded with the core action and includes illustrative examples. Every part is relevant with no filler.
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 tool with one parameter and no output schema, the description sufficiently explains the path format and provides examples. It is complete enough for the agent to understand the tool's purpose and usage, though it could benefit from mentioning the markdown return format explicitly.
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 input schema already provides comprehensive descriptions and examples for the 'path' parameter (100% coverage). The description adds a few extra examples but does not offer substantial new meaning beyond the schema, so baseline 3 applies.
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 the tool fetches a docs markdown page by path, with specific examples (getting-started/installation, livewire, ai). The verb 'Fetch' and resource 'docs markdown page' are unambiguous. It distinguishes from siblings like get_component_docs, which focus on component-specific docs, by emphasizing general docs pages.
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 implies usage for general docs pages but does not explicitly state when to use this tool over siblings or exclude cases. No alternatives are mentioned, so while context hints at differentiation, explicit guidance is missing.
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.
Tools are mostly distinct: get_component_docs, get_component_source, get_docs_page, get_llms_index, list_blocks, and list_components each have a clear intent. The only mild overlap is between get_component_docs and get_docs_page, since component docs are also docs pages, but the descriptions resolve the boundary well enough.
All tool names follow a clean verb_noun snake_case pattern: get_* fetches a specific artifact and list_* returns a collection. The naming is predictable and easy to extend, making the set feel coherent.
Six tools is a well-scoped number for a documentation-oriented server. Each tool covers a distinct retrieval or listing need without adding redundancy or unnecessary surface area.
The set covers core workflows well: component docs, component source, general docs pages, an AI index, and listings for components and blocks. A minor gap is the lack of a direct block detail/source fetcher, but agents can likely work around it using the docs page and list tools.