Apply a refinement directly as one logged revision (append evidence pointers, nudge confidence). Authorized by OAuth (own model) or an editToken. Prefer propose_refinement unless the owner asked for direct writes.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | ||
| modelId | No | ||
| editToken | No | The model's edit key, if not connected via OAuth. |
| Name | Required | Description | Default |
|---|---|---|---|
| ok | Yes | ||
| note | Yes | ||
| urls | Yes | ||
| applied | Yes | ||
| updatedAt | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, so the write nature is clear. The description adds useful behavioral context by explaining that the operation is 'one logged revision', mentions authorization requirements (OAuth or editToken), and states that it appends evidence pointers and nudges confidence. It does not contradict the annotations and provides meaningful details beyond the structured fields.
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 sentences and gets straight to the point. It front-loads the core action, then adds necessary usage guidance without any 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 has an output schema, so return values are already specified. The description covers the key operational context (auth, direct-write preference, logged revision) and is consistent with the moderately complex nested input schema. It is sufficiently complete for an agent to use correctly, though it could benefit from a note about max/min items, but those are already in the schema.
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 description coverage is low at 33%, with only editToken described in the schema and nested item properties having descriptions. The description adds only minimal parameter context (mentions OAuth and editToken as authorization methods) but does not explain modelId or the items structure. Given the low coverage, the description should compensate but falls short.
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: 'Apply a refinement directly as one logged revision' with specifics on what it does ('append evidence pointers, nudge confidence'). It also distinguishes itself from the sibling tool propose_refinement by explicitly noting a directive to prefer that alternative unless direct writes are requested, making the purpose unambiguous.
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 provides explicit guidance on when to use this tool versus the alternative: 'Prefer propose_refinement unless the owner asked for direct writes.' This clearly delineates the appropriate context and names the alternative, making it easy for an agent to decide between the two.
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 are clearly distinct: add_hunch and suggest_question differ by confirmed vs. proposed; propose_refinement and apply_refinement differ by queued vs. direct. The only potential confusion is find_complementary_thinkers vs. find_matches, but their descriptions (famous thinkers vs. actual people) mitigate overlap.
All tool names follow a consistent verb_noun pattern (add, apply, find, propose, read, suggest), with no mixed conventions or vague verbs. The names are descriptive and predictable.
Seven tools is well within the ideal range for a focused domain. Each tool serves a distinct core operation—reading, adding, suggesting, refining, and matching—so no tool feels redundant or excessive.
The tool surface covers the primary workflows: reading a model, adding confirmed hunches, suggesting questions, proposing/applying refinements, and finding complementary thinkers or people. Minor gaps exist—no explicit update/delete hunch tool, and pending proposals are not listable—but these may be intentionally delegated to the human hub.