Rolli MCP

get_keyword_search_posts

Retrieve raw social media posts from a keyword search, with pagination, platform filtering, and lean field options for reading large result sets.

Instructions

Get raw posts from a keyword search. The first line says how many of the search's posts this response shows and the offset of the next page; total, returned, offset and platform_counts (posts per platform across the whole search) are in the JSON. Read a large search in pages with offset and limit, one platform at a time, and fields=lean when the text matters more than media and profile URLs. A response over the size cap is marked truncated: true.

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": {
      "search_id": "<search_id>"
    },
    "name": "get_keyword_search_posts"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Posts per page, 1 to 1000 (default: every remaining post)
`fields`	No	lean leaves out media_url, profile_url and sentiment_reason, so roughly twice as many posts fit in one response (default: full)
`offset`	No	Posts to skip (default 0). To read the next page, use the offset the previous response's first line gives.
`platform`	No	Filter by platform (default: all)
`search_id`	Yes	Keyword search ID

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv1.5.0Nov 4, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / fields

Added value: +{
+  "description": "lean leaves out media_url, profile_url and sentiment_reason, so roughly twice as many posts fit in one response (default: full)",
+  "enum": [
+    "full",
+    "lean"
+  ],
+  "type": "string"
+}

addedInput schema / properties / limit

Added value: +{
+  "description": "Posts per page, 1 to 1000 (default: every remaining post)",
+  "maximum": 1000,
+  "minimum": 1,
+  "type": "integer"
+}

addedInput schema / properties / offset

Added value: +{
+  "description": "Posts to skip (default 0). To read the next page, use the offset the previous response's first line gives.",
+  "maximum": 9007199254740991,
+  "minimum": 0,
+  "type": "integer"
+}

addedInput schema / properties / search_id / maximum
```
Added value: +9007199254740991
```

Changed1 schema field changedv1.4.0Aug 13, 2026

changedInput schema / properties / platform / enum

Previous value: -[
-  "all",
-  "twitter",
-  "reddit",
-  "bluesky",
-  "youtube",
-  "instagram",
-  "facebook",
-  "weibo",
-  "linkedin"
-]New value: +[
+  "all",
+  "twitter",
+  "reddit",
+  "bluesky",
+  "youtube",
+  "instagram",
+  "facebook",
+  "threads"
+]

First observedv1.1.7Mar 18, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the whole burden and does much of it well: it discloses the response envelope (a first line with count and next offset, plus total/returned/offset/platform_counts in JSON) and the truncation behavior when the size cap is exceeded. It does not cover ordering guarantees, auth requirements, or whether results are a cached snapshot, which keeps it short of 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.

Conciseness4/5

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

Four sentences, front-loaded with purpose then response shape then paging strategy, with essentially no filler. The middle sentence bundling total/returned/offset/platform_counts is dense but each item earns its place by telling the agent what fields to read.

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

Completeness5/5

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

There is no output schema, and the description compensates by describing the return envelope and the truncation flag, which is exactly the missing structured information. For a 5-parameter read tool with full schema coverage, nothing an agent needs in order to page correctly is absent.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real operational meaning: use the offset reported in the previous response's first line rather than computing it, filter per platform rather than fetching all, and note that lean roughly doubles posts per response because it omits media_url, profile_url and sentiment_reason.

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

Purpose5/5

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

The description opens with a specific verb and resource, 'Get raw posts from a keyword search', which pins down both the action and the scope. 'Raw posts' distinguishes it from the sibling get_keyword_search, which returns the search record itself, and from get_user_search_posts, so an agent can pick without opening a schema.

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

Usage Guidelines4/5

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

It explains how to consume a large result set: page with offset and limit, narrow to one platform at a time, and use fields=lean when text matters more than media and profile URLs. It never states explicit exclusions or names a sibling alternative, so it gives clear operating context rather than full when/when-not routing.

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