findEmailBatchGet
Poll a bulk find-email batch
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The `batch_id` returned by the submit call. |
Poll a bulk find-email batch
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The `batch_id` returned by the submit call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds little behavioral context beyond the resource name; it doesn't explain polling semantics, response format, or retry recommendations. 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 one short sentence, front-loaded with the verb and resource. It is concise but not overly terse; it conveys the core action without excess words.
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?
With no output schema and a minimal description, the tool lacks explanation of what a 'poll' returns (status vs. results), or how to interpret the response. The parameter description provides some context but the description as a whole is incomplete for a tool involved in an async workflow.
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% for the single `id` parameter, and its description explains it's a batch ID from a submit call. The tool description itself adds no parameter details, but the baseline is 3 due to 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 uses the verb 'Poll' and identifies the resource as a bulk find-email batch, distinguishing it from sibling tools like findEmailBatchSubmit and findEmail. However, it doesn't specify whether it returns status only or final results, making it slightly vague.
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 on when to use this tool vs alternatives. It doesn't mention that this should be called after findEmailBatchSubmit, or that it's for retrieving async results. The parameter schema mentions 'returned by the submit call', but that is in the schema, not the tool description.
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.
Many tools have overlapping purposes, such as signals-firmographics vs companies-enrich_firmographics, findEmail vs contacts-enrich_work_email, and monitors vs signal_subscriptions vs market_signals. While descriptions add some context, an agent could easily select the wrong tool due to the high similarity in function.
Most tools use a resource_subresource-action pattern, but there are inconsistent separators: underscores within some names, hyphens in others (e.g., scoring-assignment-bulk-create), and several camelCase exceptions (findEmail, findEmailBatchGet, getContactResearchByExternalID). This mixed convention makes the tool set feel unpredictable.
With 119 tools, this server vastly exceeds the typical well-scoped range. Even for a comprehensive B2B data platform, the sheer number creates cognitive overload and increases the risk of incorrect tool selection.
The server covers an extensive range of operations: enrichment, lists, contacts, signals, subscriptions, monitors, and scoring. Nearly every resource has create, read, update, and delete or lifecycle equivalents, leaving very few practical gaps for the intended use case.