signals.recommendations.get
Get one recommended company with sources and persona fit
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| recommendation_id | Yes | Recommendation id |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| meta | No |
Get one recommended company with sources and persona fit
| Name | Required | Description | Default |
|---|---|---|---|
| recommendation_id | Yes | Recommendation id |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| meta | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds that the response includes 'sources and persona fit', which is useful but likely also reflected in the output schema. No contradiction with annotations; the added value beyond structured data is minimal.
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, front-loaded sentence with zero unnecessary words. It states exactly what is returned and the key differentiator ('one'). This is exemplary conciseness.
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?
Given the tool's simplicity (one parameter, output schema present, annotations covering safety), the description is complete. It names the resource, the scope (one), and the contents (sources, persona fit). No critical information is missing for an agent to decide and invoke it correctly.
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 parameter, so the schema already conveys everything about recommendation_id. The tool description does not add extra meaning about parameter formats, constraints, or usage beyond implying it identifies a specific recommendation. Baseline 3 is appropriate.
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 the verb 'Get' and the specific resource 'one recommended company' with its key attributes (sources and persona fit). It distinguishes this from listing many recommendations (signals.recommendations.list) and from mutating actions like dismiss or block. The word 'one' makes the intent unambiguous.
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 explicit when-to-use guidance is given. However, the requirement of a recommendation_id and the contrast with list imply it is for fetching a single recommendation's details. The description does not name alternatives or exclusion conditions, leaving that to inference from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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