get_postsFetch recent posts from a Bluesky user's timeline. Returns post text, timestamps, likes, reposts, reply counts, and threaded replies.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of posts (1-100, default 20) | |
| handle | Yes | Bluesky handle |
| Name | Required | Description | Default |
|---|---|---|---|
| posts | Yes | List of posts from user's timeline |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "handle": "alice.bsky.social"
+ },
+ {
+ "handle": "jack.bsky.social",
+ "limit": 50
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "posts": {
+ "description": "List of posts from user's timeline",
+ "items": {
+ "properties": {
+ "author": {
+ "description": "Author's display name or handle",
+ "type": "string"
+ },
+ "createdAt": {
+ "description": "ISO timestamp of post creation",
+ "type": "string"
+ },
+ "handle": {
+ "description": "Author's handle",
+ "type": "string"
+ },
+ "likes": {
+ "description": "Number of likes",
+ "type": "number"
+ },
+ "replies": {
+ "description": "Number of replies",
+ "type": "number"
+ },
+ "reposts": {
+ "description": "Number of reposts",
+ "type": "number"
+ },
+ "text": {
+ "description": "Post content text",
+ "type": "string"
+ },
+ "uri": {
+ "description": "AT URI of the post",
+ "type": "string"
+ }
+ },
+ "required": [
+ "uri",
+ "author",
+ "handle",
+ "text",
+ "createdAt",
+ "likes",
+ "reposts",
+ "replies"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "posts"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {
- "limit": 10
- },
- {
- "limit": 25
- }
-]Input schema / examplesAdded value: +[
+ {
+ "limit": 10
+ },
+ {
+ "limit": 25
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds return field details but does not disclose operational behavior like pagination, ordering, time windows, or rate limits. With annotations covering the safety profile, this modest addition earns a 3.
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 two concise sentences, front-loaded with the primary action and followed by return details. Every word earns its place, with no redundancy or fluff.
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 simple read-only tool with an output schema and good annotations, the description covers the core purpose and return contents. Missing usage guidance and operational details are minor gaps given the tool's simplicity, but overall it is sufficiently complete.
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 both parameters (handle and limit), including the limit's range and default. The description adds no additional parameter meaning beyond what the schema already provides, so the baseline score 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 clearly states the verb 'fetch' and the resource 'recent posts from a Bluesky user's timeline', and lists return fields. It is specific but does not explicitly differentiate from sibling tools like get_feed or get_thread, so it stops short of a 5.
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 gives no guidance on when to use this tool versus alternatives such as search_posts or get_thread. There is no mention of prerequisites, exclusions, or preferred contexts, so the agent must infer usage from the tool name alone.
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.
Most tools have clearly distinct roles, but several overlapping pairs create ambiguity: ask_pipeworx vs ask_pipeworx_beta are explicitly identical today, discover_tools vs suggest_questions both serve discovery/onboarding, and bet_research vs polymarket_edges both address betting-edge questions. The detailed descriptions help, but an agent could still select the wrong tool in these cases.
The set uses at least four naming conventions: get_* for Bluesky reads, verb_noun for Pipeworx tools (ask_pipeworx, resolve_entity, validate_claim), polymarket_* prefixed tools, and verb-only memory tools (remember, recall, forget). Each subgroup is internally consistent, but the overall mix feels inconsistent and unpredictable.
39 tools is well beyond the typical well-scoped server, and the scope sprawls across Bluesky reads, Pipeworx data, Polymarket analysis, memory, subscriptions, and one-off utilities like generate_llms_txt and scan_dependency. The count would be more reasonable split into separate servers; as-is it feels heavy and unfocused.
Within the server's evident scope, coverage is strong: Bluesky read operations, Pipeworx query/research/verification, entity profiling, and subscription lifecycle are all represented. The main gaps are write actions for Bluesky (posting, following, liking) and a few auxiliary features that are only partially integrated, but no critical workflow dead-ends appear for the primary data-research use cases.