Deterministic statistics. With a query: total matches + state distribution of the top results. Without: global knowledge-base stats (entry count, domains, snapshot).
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional query to get coverage/state stats for | |
| domain | No | Optional domain filter (with query) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful context by explaining what 'stats' includes in each mode (entry count, domains, snapshot), and the word 'deterministic' reinforces the idempotent/read-only nature. 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 extremely concise, using two sentences to convey the dual-mode behavior. Every phrase adds value, and the most important distinction (query vs no query) is front-loaded. No fluff or repetition.
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 simplicity (only two optional params, no output schema), the description covers the essential behavioral modes and key output concepts. It does not detail return formatting or pagination, but for a stats tool with read-only annotations, this is sufficient. Sibling tools are numerous but the description is self-contained.
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 clear descriptions for both parameters. The description adds meaning beyond the schema by explaining the conditional effect of the optional query parameter (with vs without), and the schema already clarifies 'domain' as an optional filter. This supplemental context enhances parameter understanding.
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 identifies the tool as providing deterministic statistics and explicitly differentiates two modes: with a query (total matches + state distribution) and without (global KB stats). This distinguishes it from the search and browse siblings by specifying it's about stats, not retrieval.
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 for when to use each mode (with or without a query), implying usage in stats-gathering scenarios. It does not explicitly name alternatives or exclusions, but the mode-dependent behavior effectively guides the agent on when to invoke it.
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.