list_researchList the research themes published by The Revenue AI Report. Each theme is a sourced argument about AI inside B2B revenue teams, with charts, findings, named sources, and sample sizes.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Keyword filter on title, dek, and findings. |
| Name | Required | Description | Default |
|---|---|---|---|
| total | Yes | ||
| themes | Yes | ||
| returned | Yes | ||
| researchRules | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "researchRules": {},
+ "returned": {
+ "type": "number"
+ },
+ "themes": {
+ "items": {
+ "additionalProperties": {},
+ "properties": {
+ "dek": {
+ "type": "string"
+ },
+ "number": {
+ "type": "number"
+ },
+ "slug": {
+ "type": "string"
+ },
+ "sourceCount": {
+ "type": "number"
+ },
+ "takeaways": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "slug",
+ "number",
+ "title",
+ "dek",
+ "takeaways",
+ "sourceCount",
+ "url"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total": {
+ "type": "number"
+ }
+ },
+ "required": [
+ "total",
+ "returned",
+ "themes",
+ "researchRules"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds return-content context beyond the annotations by stating that each theme is a sourced argument with charts, findings, named sources, and sample sizes. It does not discuss pagination or ordering, but that is a minor gap given the 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?
Two sentences with no fluff: the first states the action and scope, the second defines the returned item. Every sentence earns its place.
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 tool with one optional parameter, full annotations, and an output schema, the description provides enough context to call and interpret the result. It defines the domain, the item type, and the item contents, leaving only structured details to 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 100% for the single optional 'query' parameter, which is already described as a keyword filter on title, dek, and findings. The description adds no new parameter semantics, but the schema fully covers it, so the baseline of 3 applies.
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 resource ('research themes published by The Revenue AI Report'), and further defines what a theme is with content details. This clearly distinguishes the tool from the many sibling list/get/search tools.
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 when-to-use or when-not-to-use guidance is provided. The description does not mention alternatives like search_publications, get_research, or list_reversals, leaving the agent to infer selection from the name and context.
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.