get_contextThe complete weekly context document: scores, every tracked question with its verbatim answer status, mentions, and what to do next. Same content as the downloadable context.md.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes that this is a safe read operation. The description adds useful context by listing the document's contents and noting it matches context.md, but it does not describe response format, freshness, or potential caveats. No contradiction with 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 concise sentences: the first front-loads the document scope and contents, the second gives a concrete reference point via context.md. Every word earns its place with no repetition or fluff.
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-parameter, read-only tool with no output schema, the description is nearly sufficient: it names the main content areas and provides an equivalence to the downloadable file. It could additionally note when to choose this full document over a sibling tool, but that is usage guidance rather than a core completeness 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?
The input schema has zero parameters, so there is nothing for the description to document beyond what the schema already shows. The description appropriately focuses on what the returned context document contains rather than parameters.
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 identifies the resource as 'the complete weekly context document' and enumerates its contents (scores, tracked questions, mentions, next steps). It does not use an explicit verb like 'retrieves' or 'returns', and it does not explicitly distinguish itself from sibling tools, though 'complete' implies the full-document role.
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?
Usage context is only implied: the phrase 'complete weekly context document' and 'Same content as the downloadable context.md' suggest this tool is for getting everything at once, while sibling tools like get_answers and get_mentions cover narrower slices. There is no explicit when-to-use or when-not-to-use statement.
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 have clearly distinct objects: answers, trajectories, citations, mentions, traffic, scores, and briefs. A few pairs like get_citation_sources vs get_source_profile and get_rivals vs get_share_of_voice overlap thematically, but their descriptions clarify different granularity and purpose.
All 16 tools follow an identical get_ + snake_case noun phrase pattern, such as get_agent_view, get_share_of_voice, and get_question_trajectories. This makes the tool set highly predictable and easy to navigate.
At 16 tools, the set sits just above the ideal 3-15 range, but each tool addresses a distinct facet of AI visibility monitoring. The count feels justified rather than bloated, though it is slightly heavy for a read-only monitor.
The suite covers the full read-only monitoring lifecycle: visibility scores, raw answers, question histories, competitor comparisons, cited sources, mentions, traffic, benchmarks, action plans, personas, and content briefs. There are no obvious dead ends or missing core operations for the stated purpose.