ingest_listing
Ingest a listing and score uniqueness inside one pond. Needs a write key.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| name | Yes | ||
| pond | No | ||
| org_id | No | ||
| summary | No | ||
| one_liner | No |
Ingest a listing and score uniqueness inside one pond. Needs a write key.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| name | Yes | ||
| pond | No | ||
| org_id | No | ||
| summary | No | ||
| one_liner | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It does disclose an important behavioral condition—'Needs a write key'—and indicates the operation is scoped to 'one pond.' However, it does not explain side effects, what 'scoring uniqueness' entails, or the response/failure behavior, leaving significant gaps.
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 a single efficient sentence that front-loads the action and includes a necessary access prerequisite. There is no redundancy or filler. The brevity hurts completeness elsewhere but the text as written is well-structured and concise.
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 6-parameter tool with no annotations and no output schema, the description is far too sparse. It omits parameter semantics, return values, side effects, and usage conditions. An agent would not be able to invoke this tool correctly based only on this definition.
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 0%, and the description adds almost no parameter meaning. Terms like 'listing' and 'pond' loosely map to name/url and pond, but org_id, summary, and one_liner are entirely unexplained. The description fails to compensate for the undocumented schema.
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 and resource: 'Ingest a listing and score uniqueness inside one pond.' This clearly conveys the action and context, and is distinct from sibling tools like log_click or commit_recommend. It lacks explicit sibling differentiation, but the action is concrete enough to identify the tool's purpose.
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?
No guidance is given about when to use this tool instead of sibling tools such as log_intent or request_approve. The only usage-related signal is 'Needs a write key,' which is a prerequisite rather than a usage condition. There are no exclusions, alternatives, or selection criteria.
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.
Several tools cluster around pre-recommendation and human approval, so boundaries are blurry: commit_recommend, log_intent, whats_good_for, and trust_check all happen 'before recommending,' while request_approve and decide_approve differ mainly by who initiates. Descriptions help, but an agent could easily pick the wrong tool.
Most tools follow an imperative verb_noun snake_case pattern—log_click, spend_check, trust_check, ingest_listing—making the set predictable. nutrition_label and whats_good_for break that pattern, but the overall style is still consistent enough to navigate.
Ten tools fits the ideal 3-15 range and maps well to the server's trust-check, approval, logging, and listing-ingestion lifecycle. Each tool has a distinct role even if a few overlap conceptually.
The core workflow is well covered: policy checks, candidate lookup, logging, human approval, listing ingestion, and a nutrition stamp are all present. Missing observability and management endpoints like approval status/history or listing update/delete are workable gaps rather than dead ends.