Rolli MCP

get_expert_search

Retrieve an expert search by ID to check status and get recommended experts with profiles and AI-generated match summaries; retry if results are still pending.

Instructions

Get results for an expert search by ID. Returns search status and, once complete, the array of recommended experts with their profiles and AI-generated match summaries. results_pending: true means the search succeeded but the backend is still writing results; retry in a few seconds.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "get_expert_search"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Expert search ID

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.5.0Nov 4, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```
- addedInput schema / properties / id / maximum
```
Added value: +9007199254740991
```
Addedv1.3.0Jun 3, 2026

TDQS

A3.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does substantial work: it discloses the two-phase return behavior (status first, experts plus AI match summaries once complete) and explains an ambiguous field, results_pending, with an explicit retry recommendation. It stops short of stating read-only semantics, error behavior for invalid IDs, or other status values.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences with zero redundancy; the core action is front-loaded and the return shape and retry caveat follow in the order an agent needs them.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read tool with no output schema and no annotations, the description covers what is returned and the tricky pending state well. It omits failure handling for a nonexistent search ID and the full set of possible status values, which keeps it just short of complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter and schema coverage is 100%, with the schema itself documenting 'Expert search ID'. The description only restates 'by ID' and adds no format, origin, or sourcing guidance (e.g., where the ID comes from), so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ('Get results for an expert search by ID') and the 'by ID' framing implicitly separates it from the sibling that initiates a search (expert_search) and the one that enumerates them (list_expert_searches). It does not name those siblings outright, so an agent must infer the pairing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: the polling/retry note ('results_pending: true means... retry in a few seconds') tells the agent how to call it repeatedly, but nothing says when to use this versus expert_search or list_expert_searches, nor what to do if the ID is unknown. Adequate but leaves the workflow to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.