list_threads
Live event threads: wire items clustered into developing stories (paid, $0.002/req or pass).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Live event threads: wire items clustered into developing stories (paid, $0.002/req or pass).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It adds useful context about the data type ('live event threads', 'developing stories') and mentions a pricing/access policy ('paid, $0.002/req or pass'). However, it does not specify return format, pagination, ordering, authentication needs, or any side effects, leaving significant gaps.
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, compressed sentence that packs the core idea and a critical caveat into few words. It is fully front-loaded and every phrase earns its place. However, it is slightly cryptic—especially 'or pass'—which reduces clarity, so it is not a perfect 5.
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, parameterless list tool, the description conveys the essential topic but leaves ambiguity around 'or pass' and does not describe the return value or any limits. Since there is no output schema, these omissions are noticeable, and the description does not fully stand alone.
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?
The input schema has zero parameters with 100% 'coverage' (trivially). Per guidelines, a 0-parameter tool gets a baseline of 4 because there is nothing to document. The description adds no parameter-related detail, but none is needed.
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 identifies the resource being listed ('Live event threads') and explains their nature ('wire items clustered into developing stories'), which distinguishes this from sibling tools like get_thread or search_wire. The verb 'list' is not stated in the description, but the tool name supplies it, making the purpose clear enough.
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 implies this tool is used for accessing live event threads, but provides no explicit guidance on when to choose this over alternatives such as get_thread or search_wire. It does mention a cost/availability caveat ('paid, $0.002/req or pass'), which hints at conditions of use, but there are no clear exclusions or comparisons.
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.
Tools are grouped by domain (funding, deals, transcripts, etc.) and each has a specific focus: basic list, latest, search, or summary. While some pairs like deals/deals_search and funding/funding_latest could be confused, the descriptions clearly differentiate them. The boundaries are mostly clear, but the sheer number of tools requires careful reading.
Naming is inconsistent across the set. Some tools use bare nouns (funding, deals, catalogues), some use verb prefixes (get_article, list_threads, search_wire), and many use suffixes (_latest, _search, _summary). The position and style of modifiers vary between domains, making it difficult to predict tool names.
With 27 tools, the server is on the heavy end, which aligns with its terminal-style scope covering many distinct data domains (news, transcripts, funding, retail, model watch). The count is justified by the breadth, but it feels dense and could be split into smaller, more focused servers.
The server provides comprehensive coverage for most domains: listing, retrieving details, searching, and domain-specific variants (latest, hot, sentiment). Minor gaps exist, such as no way to fetch a specific funding event by ID or a latest deals tool, but these are easy workarounds.