List all AI-generated signal summaries for a domain, ordered by creation date (latest first)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results per page | |
| domain | Yes | Filter summaries by company domain (e.g., "acme.com") | |
| offset | No | Number of results to skip for pagination |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds value by revealing the ordering behavior ('latest first'), which is not in the annotations. It does not, however, describe pagination quirks or return shape, so it doesn't reach a 5.
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 sentence (14 words) that starts with the key verb 'List' and packs in the scope, resource type, and ordering. No fluff, no repetition of schema details. It is maximally efficient.
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, and the annotations cover safety, but there is no output schema. The description does not explain what the returned summaries look like (fields, structure), nor does it mention pagination behavior beyond what the schema already provides. For a list tool, this is a moderate gap, so a 3 is appropriate.
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 100%, with each parameter (limit, domain, offset) already documented in the schema. The description adds no additional parameter-level semantics (e.g., format hints, default behaviors beyond what's in the schema). Baseline of 3 is appropriate because the schema carries the full burden.
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 ('List') and identifies a clear resource ('all AI-generated signal summaries') with a scope ('for a domain'). It also adds ordering detail ('by creation date, latest first'), which distinguishes it from other signal list tools like market_signals-list_signals or company_signals-list.
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 implies usage by stating the tool lists summaries for a domain, but it does not explicitly contrast with alternative tools such as signal_summaries-generate or other signals-list tools. There is no 'when to use' or 'when not to use' guidance, so it stops at implied usage.
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.
