Search Shipshape Data's library of 201 practical guides on data and AI. Each result includes the questions the guide answers and its URL (every page also serves a markdown twin at URL + index.md).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, default 5 | |
| query | Yes | Search terms, e.g. "data lineage" or "RAG architecture" | |
| category | No | Optional category filter; one of the names from resource categories |
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, closed-world behavior, so the bar is lower, and the description still adds real value by describing the result shape (questions each guide answers plus URL) and the markdown-twin convention at URL + index.md. It does not disclose ranking behavior, whether the match is keyword or semantic, or what an empty result looks like.
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 sentences, front-loaded with the identification of the corpus, then the result shape. Every clause carries information the agent can act on and nothing is redundant.
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?
With no output schema, the description correctly picks up the burden of describing return values, including the markdown-twin detail. For a three-parameter, fully documented search tool this is close to complete; only match semantics and result-count behavior are unstated.
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 coverage is 100%, so query, limit, and category are all documented in the schema; that sets the baseline at 3. The description adds no parameter guidance of its own — nothing on how query terms combine, what the default limit means in practice, or where category values come from.
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 (Search) and a precisely scoped resource (Shipshape Data's library of 201 practical guides on data and AI), which is distinguishable from siblings like list_services and get_case_studies. It stops short of explicitly naming those alternatives, so an agent must infer that this is the content-lookup path rather than the structured-data path.
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 is only implied: an agent can infer this is the tool for finding guides on a topic, but there is no explicit 'use this when…' or exclusion versus siblings such as get_ai_readiness_questions. No mention of when a category filter is preferable to a query.
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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