Retrieve the current status and AI-generated insights for a contact research job when it has completed.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The unique identifier of the contact research request |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that it returns 'current status and AI-generated insights' and a completion condition, but does not disclose error behavior, polling semantics, or what happens before completion. Beyond annotations, limited additional disclosure, so 3.
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?
A single, front-loaded sentence that introduces the verb and object immediately and has no redundant phrases. Efficient for a one-parameter getter, so 5.
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 simple get-by-ID with no output schema, the description provides a high-level return summary but omits important context: what happens if the job hasn't completed, whether not-found errors occur, or the structure of insights. Given the openWorldHint and lack of output schema, more detail would help, but the description is minimally adequate, so 3.
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 has one parameter with full description coverage (100%). The description's 'job' term aligns with schema's 'request' but adds no format, allowed values, or relationship details. Baseline 3 applies because schema carries the semantic weight.
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 a specific action (retrieve) and resource (contact research job), with a scoping condition ('when it has completed'). It distinguishes from write tools like contacts-create_research but does not explicitly differentiate from the similar sibling getContactResearchByExternalID, since both retrieve research data. Thus 4.
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?
Provides a timing guidance ('when it has completed') that implies it should be called after completion, but does not name alternatives such as contacts-create_research or getContactResearchByExternalID, nor state when not to use it. Minimal guidance, so 3.
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.
