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Stocktwits

by io.github.pipeworx-io

Send Pipeworx Feedback

pipeworx_feedback

Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a claim_token; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNobug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else.
contextNoOptional structured context: which tool, pack, or vertical this relates to.
messageNoYour feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max.
claim_tokenNoRead the reply to a report you filed earlier: pass the `pwfb_…` token that filing returned, with no other arguments. Returns the status and, once resolved, what actually changed.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only provide false hints, so the description carries the full burden. It adds rich behavioral context: claim_token mechanism for follow-ups, rate limit of 5 per day, no tool-call quota impact, daily digest reading, and roadmap influence. This goes well beyond what annotations convey and gives the agent a clear picture of consequences.

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?

The description is a single dense paragraph, but each sentence earns its place: purpose, use cases, exclusions, claim_token flow, rate limit, and cost. It could be slightly more scannable with bullet points, but it's appropriately sized for the tool's complexity and avoids fluff.

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?

For a feedback tool with 4 parameters (one enum, one nested object) and no output schema, the description fully covers usage context, return behavior via claim_token, constraints, and exclusions. It leaves no significant ambiguity about when or how to invoke it.

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. The description adds extra nuance beyond schema, such as the claim_token round-trip usage pattern, the instruction to describe issues in terms of Pipeworx tools/packs rather than pasting user prompts, and clarifies that the type enum maps to specific conditions. It doesn't fully detail every parameter, but meaningfully supplements the schema.

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 states a specific verb ('Tell the Pipeworx team') and an explicit resource (feedback about Pipeworx tools). It clearly distinguishes itself from sibling tools by scoping to 'tools served by this Pipeworx connection' and enumerating feedback types (bug, feature, data_gap, praise).

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

Usage Guidelines5/5

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

Explicitly states when to use (bug, feature, data_gap, praise) and when not to use (feedback about other MCP servers), with a direct alternative ('file it with that server instead'). This is exactly the when/when-not/alternatives guidance the dimension asks for.

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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TDQS

A3.5/5.0
Disambiguation2/5

Multiple tools serve overlapping functions: three ask_pipeworx variants, two deep research tools, and multiple polymarket tools. StockTwits-specific tools are distinct but the overall set has significant ambiguity between Pipeworx and Polymarket tools.

Naming Consistency2/5

Naming styles are mixed: some use lowercase (ask_pipeworx), some use underscores (ai_visibility_check), and some use prefixes (pipeworx_feedback, polymarket_arbitrage). No consistent pattern across the tool set.

Tool Count3/5

40 tools is on the higher end but not extreme. However, many tools belong to the Pipeworx ecosystem, which seems separate from StockTwits, making the set feel bloated and unfocused for a single server.

Completeness2/5

StockTwits social features are adequately covered (symbol search, streams, trending), but the inclusion of Pipeworx tools creates a sprawling surface without complete coverage in any one domain. Missing core StockTwits features like user profiles or direct messaging.

Resources