PAID ($0.01 via x402). Full-text search across the CURRENT state of every sellable dataset at once, returning matching rows with their dataset name. Use it when you do not know which dataset holds a value. Do NOT use it to resolve a name to an identifier — anansi_search does that free — and note it searches current state only, not history. Input: q is a phrase match.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | ||
| limit | No | ||
| payment_header | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that it is a paid operation ($0.01 via x402), searches only current state (not history), and returns rows with dataset names. While it implies read-only behavior via 'search', it does not explicitly state side effects, but given the context this is sufficient.
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 moderately concise and well-structured, covering cost, action, scope, usage guidance, and parameter note in separate sentences. It is not overly verbose, though it could be slightly tightened without losing critical information.
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 provides enough context for an agent to decide when to use it, including cost and scope. It lacks details on limit and payment_header, but these are likely standard parameters. The absence of an output schema is noted, but the description explains the return type (rows with dataset names).
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 description explains that 'q is a phrase match', adding value beyond the schema. However, it does not elaborate on 'limit' or 'payment_header', which remain unexplained in both schema and description. With three parameters, only one is described, so coverage is partial.
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 performs a full-text search across all sellable datasets, returning matching rows with dataset names. It also distinguishes itself from the sibling anansi_search by explicitly noting it is not for name-to-identifier resolution.
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?
It provides explicit usage guidance: 'Use it when you do not know which dataset holds a value' and 'Do NOT use it to resolve a name to an identifier — anansi_search does that free'. This clearly defines when to use this tool versus an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.