Explain why a specific document ranked for a query. Returns a per-term relevance breakdown and an overall match summary — deterministic and reproducible.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to explain against | |
| doc_id | Yes | Document ID, e.g. "doc_42" or "42" | |
| index_id | Yes | Index ID, e.g. idx_... |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds the deterministic and reproducible nature of the results and outlines the return composition, which goes beyond the structured fields without contradicting them.
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 two concise sentences, action-first, with no redundant information. Each sentence adds value: the first states the purpose, the second describes the output and a key behavioral trait.
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 read-only explain tool, the description adequately covers purpose, output, and determinism. There is no output schema, but the description summarizes the return value. It lacks explicit prerequisites/error behavior, but these are minor given the annotation safety profile.
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 input schema covers 100% of the parameters with descriptions and patterns (e.g., doc_id example, index_id pattern). The description adds no additional parameter-specific details, so it earns the baseline 3 for high schema coverage.
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 action ('Explain why a specific document ranked for a query') and specifies the output (per-term relevance breakdown, overall match summary). This distinguishes it from sibling tools like search, fetch, or browse, which have different purposes.
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 context on when to use the tool (to understand document ranking for a query), but it does not explicitly mention alternatives or exclusions. For instance, it doesn't compare with search or browse, but the intent is obvious enough for an agent to choose it for explainability tasks.
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.