observatory_grade_distributionGrade distribution across every site the MDN HTTP Observatory has scanned — how many sites hold each grade from A+ to F. Use it to say where one site's grade sits relative to the web (e.g. "a B beats X% of scanned sites"). Keyless, no arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive and open-world behavior. The description adds two things beyond them: the dataset scope (every site MDN HTTP Observatory has scanned) and the absence of auth ('Keyless'), which is meaningful for calling it. Nothing here contradicts the annotations.
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?
Two tight sentences: the first defines the resource and its output, the second defines the use case. Front-loaded, 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 zero-arg, read-only aggregate with no output schema, the description adequately conveys what is returned (per-grade counts) and why it matters. It does not describe freshness or update cadence of the scan corpus, a minor gap.
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?
Zero parameters, so the baseline is 4. The description reinforces this with 'no arguments', confirming an agent can call it with an empty object and needs no input.
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?
States a specific verb (returns/computes distribution) and resource (grade distribution across all scanned sites), including the exact output shape (counts per grade A+ to F). It is clearly distinguishable from observatory_scan and observatory_results in the sibling list.
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?
Explicitly names the use case: benchmarking one site's grade against the whole web, with a concrete example phrasing. It does not name a sibling alternative to use instead for other purposes, so it stops short of full when/when-not routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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