Cross-domain structural analog: given a knowledge entry id, find its closest structural twin in each OTHER domain ("same structure, different domain") via embedding-cluster similarity. Deterministic. Use coldstate_resolve to turn a name into an id first.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Stable knowledge entry id, e.g. eng_... or soc_... | |
| limit | No | Max cross-domain analogs (one per domain) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description need not repeat safety basics. It adds valuable behavior beyond annotations: the algorithm ('embedding-cluster similarity'), determinism, and the specific cross-domain twin concept. 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?
The description is three sentences, all information-dense and front-loaded with the core purpose. It includes the algorithm, determinism, and a critical prerequisite without any wasted words.
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?
Given the tool's moderate complexity and lack of output schema, the description sufficiently explains what the tool does, how it works, and how to prepare input. It could mention the return shape more explicitly, but it is already complete enough for an agent to select and invoke it correctly.
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% with descriptions for both 'id' and 'limit', so the schema does much of the work. The description adds semantic value by explaining that the id is a 'knowledge entry id' and pointing users to coldstate_resolve for name-to-id resolution, which enriches understanding of the id parameter beyond schema text.
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 a specific action ('find its closest structural twin') on a specific resource ('knowledge entry id') and clearly differentiates it from sibling tools by targeting 'each OTHER domain' rather than same-domain search or general lookup. It names the exact purpose and scope.
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?
The description gives clear usage context: it is for cross-domain structural analogs when you have a knowledge entry id, and it explicitly instructs to use coldstate_resolve first if you have a name. It does not enumerate exclusions versus siblings like coldstate_related, but the 'OTHER domain' distinction and prerequisite instruction provide clear guidance.
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 purposes, but the three search tools (search, search_global, batch_search) and the two relation tools (related, isomorph) could cause initial confusion despite clear descriptions. Overall boundaries are clear enough that agents should select correctly with careful reading.
Every tool uses the consistent coldstate_ prefix followed by a descriptive verb_noun pattern (e.g., batch_search, browse_documents, list_indexes). The naming is uniform and predictable, making the toolset easy to navigate.
At 16 tools, the server sits at the upper edge of what feels reasonable, and the calibration guidance flags 16-25 as heavy. However, each tool serves a distinct function in the knowledge-search lifecycle, so the count is defensible but still slightly high for a single server.
The toolset covers the full read-only knowledge workflow: searching, browsing, fetching, citing, verifying, explaining, resolving, and exploring relations. It also includes meta-tools for capabilities, domains, snapshots, and statistics, leaving no obvious gaps for its stated purpose.