parse_searchFind brands, organic AI prompts, citation sources, and market niches for marketer research. Use this first when the user names a brand, category, source, or AI visibility question.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| types | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, and the description adds usage context but not much else. It does not describe return formats, how 'types' affects results, or whether results are aggregated, leaving the agent to infer behavior from the schema.
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 with no fluff. The main purpose is front-loaded, and the usage guidance follows naturally. Every word adds value.
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 covers purpose and usage well, but given 0% schema description coverage and no output schema, it leaves out important details about the 'types' filter and 'limit.' It provides the essential starting point but is not fully complete for a parameterized search 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%, so the description must compensate. It only implicitly references 'query' via 'when the user names a brand, category, source, or AI visibility question.' The 'limit' and 'types' parameters are not mentioned at all, leaving them semantically undefined.
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 uses specific verbs and lists concrete resources: 'Find brands, organic AI prompts, citation sources, and market niches.' It also establishes its role as the primary entry point with 'Use this first,' clearly distinguishing it from siblings like general 'search' or specific 'get_brand' tools.
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?
'Use this first when the user names a brand, category, source, or AI visibility question' gives clear context on when to invoke the tool. However, it does not explicitly mention when not to use it or name alternative tools, keeping it one notch below a fully explicit guideline.
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 distinct purposes: parse_get_brand fetches brand briefs, parse_get_prompt fetches prompts by slug, parse_get_stats explains the index, and parse_search does searching. However, the 'fetch' alias overlaps with parse_get_brand and parse_get_prompt by resolving IDs, causing potential ambiguity.
The four parse_* tools consistently follow a verb_noun pattern (parse_get_*). However, the two compatibility aliases 'fetch' and 'search' break this pattern, introducing minor inconsistency.
Six tools is a well-scoped set for a read-only research server focused on parsing and searching. Each tool serves a clear purpose without unnecessary bloat.
The server covers core read operations: searching, getting brand details, getting prompt details, and retrieving index statistics. Minor gaps exist (e.g., no direct tool for listing all brands or prompts), but the search tool handles discovery.