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Gaming Feeds

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?

Without helpful annotations (all false), the description carries full behavioral disclosure. It reveals the claim_token return mechanism, the two-mode usage (filing vs. checking status), rate limiting (5/day), and that it does not count against tool-call quota. This is far beyond what annotations offer.

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 long but every sentence earns its place: purpose, use cases, exclusion, tool identification, content rules, claim token behavior, and operational limits. The logical flow makes it efficient despite its length. Could be slightly trimmed, but density is high.

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 no output schema, the description fully covers the lifecycle: when to file, what to include, how to check follow-up status, and operational constraints. The claim_token mechanics are explained clearly, so no output schema is needed. Nothing essential is missing.

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?

Since schema coverage is 100%, the baseline is 3. The description elevates this by providing concrete usage guidance for message ('describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt') and for claim_token with the exact call format pipeworx_feedback({claim_token:'pwfb_…'}).

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 immediately states 'Tell the Pipeworx team something is broken, missing, or needs to exist' and then enumerates specific feedback categories (bug, feature, data_gap, praise). It clearly distinguishes itself from all sibling tools by scoping itself to the Pipeworx connection's own tools and explicitly excluding other MCP servers.

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?

Provides explicit 'Use when' triggers for each feedback type and a strong 'ONLY for tools served by this Pipeworx connection' exclusion, telling the agent to file elsewhere for other servers. It even offers a practical check: 'Pipeworx tool names are the ones this connection lists.'

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

A3.8/5.0
Disambiguation2/5

Several clusters of near-duplicates force careful reading: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded overlap heavily; the five polymarket tools share edge-detection and arbitrage territory; and ai_visibility_check vs scan_competitor_ai_presence are easy to confuse. discover_tools, suggest_questions, and pipeworx_trending also compete as discovery/onboarding entry points.

Naming Consistency3/5

All names are lowercase snake_case, so there is surface consistency, but the structural pattern is mixed: verb_noun (list_feeds, read_feed, resolve_entity), noun_verb (ai_visibility_check), noun_noun (entity_profile, polymarket_fill_risk), and bare verbs (remember, forget). Prefixes like ask_pipeworx and polymarket create local order, but no server-wide naming convention holds.

Tool Count2/5

34 tools for a server named 'Gaming Feeds' is heavily over-scoped; only list_feeds, read_feed, and fetch_feed actually serve that purpose. The remaining ~25 tools constitute an unrelated Pipeworx data platform covering queries, prediction markets, memory, subscriptions, and feedback, making the count feel like a bundled mega-server rather than a focused toolset.

Completeness3/5

For the nominal gaming-feed domain, list/read/fetch plus keyword filtering covers basic consumption, but there is no cross-feed search, feed management, or feed-specific subscription support (subscribe only handles SEC, Polymarket, and FRED streams). The broader data/research surface is comprehensive internally, but that completeness belongs to a different server's purpose.

Resources