Find knowledge entries deterministically related to a given entry id via ColdState's concept-cluster graph. Returns same-cluster and neighboring-cluster entries, each tagged with its relation and a cross_domain flag. Same id ⇒ same neighbors.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Stable knowledge entry id, e.g. eng_... or soc_... | |
| limit | No | Max related entries |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals behavioral traits beyond the readOnly/idempotent annotations: it explains the deterministic nature ('Same id ⇒ same neighbors') and describes the output structure (same-cluster and neighboring-cluster entries tagged with relation and cross_domain flag). This adds useful context that annotations alone don't provide.
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, both information-dense. The first states purpose and mechanism; the second states output and determinism. No filler 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?
The tool is simple (2 params, no output schema) and the description covers the output structure, determinism, and key semantics. The readOnly/idempotent annotations cover safety. There is no significant missing information for an agent to 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?
The input schema already provides full descriptions for both parameters (id with example prefixes, limit with range and default). The description adds only a minor reinforcement that id is the entry key, so it doesn't meaningfully improve on the schema's own 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 uses a specific verb 'Find' and identifies a unique resource ('knowledge entries deterministically related to a given entry id') via a specific method ('ColdState's concept-cluster graph'). This clearly distinguishes it from siblings like coldstate_search or coldstate_fetch, which serve 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?
It states the use case explicitly: finding entries related to a given id, with determinism noted. However, it does not name sibling tools as alternatives or state when not to use this tool, so it lacks explicit exclusionary 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.