searchSearch everything Mediawork publishes — vendor directory listings, FAQ entries and blog posts — and return matching records as {id, title, url}. Pass an id to fetch to read one in full. If your client supports them, the dedicated tools (search_facilities, search_faq, …) give richer, filterable results.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What to search for |
Changes observed during successful MCP inspections.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Input schema / additionalPropertiesRemoved value: -falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return format ({id, title, url}) and the relationship to `fetch`, which is valuable behavioral context. However, it does not mention potential pagination, rate limits, or indexing freshness, which would make it more transparent.
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 core function and return shape, then providing workflow guidance. Every sentence earns its place with 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 single-parameter search tool with no output schema, the description explains what is searched, the return format, how to retrieve full records, and when to prefer other tools. This covers all essential aspects needed for correct invocation and follow-up.
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 schema already documents the only parameter (`query`) 100%, so the baseline is 3. The description adds meaning by enumerating what the query can match (directory listings, FAQ, blog posts), giving the agent a better sense of the search scope beyond 'What to search for'.
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 searches across all content types (vendor directory listings, FAQ entries, blog posts) and returns a uniform record structure. It distinguishes itself from sibling tools by explicitly positioning dedicated search tools as richer alternatives, making its scope clear.
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 guidance on when to use this tool vs. alternatives: use dedicated tools when available for richer results, and use `fetch` to read a full record by id. This exceeds basic context by naming the alternative tools and the workflow.
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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