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Photon

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 are minimal (all hints false), so the description carries the burden and fully delivers. It discloses the claim_token workflow, rate limit of 5 per identifier per day, free/no quota impact, and daily digest handling—details far beyond what annotations provide. No contradiction with annotations.

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 front-loaded with the core purpose followed by usage, exclusion, workflow, and constraints. Every sentence contributes value, though it is slightly dense and could be tightened without losing information.

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?

Covers all essential aspects: purpose, scope boundaries, how to frame feedback, what to expect (claim_token), operational constraints, and cost. Despite no output schema, the description explains the main return artifact and resolution status sufficiently for an agent to invoke and follow up correctly.

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% and schema descriptions are already detailed, so baseline is 3. The description adds workflow semantics: filing without an account returns a claim_token, passing it back later with no other args reads status, and feedback should reference Pipeworx tools/packs, which clarifies the context and message fields. This is a meaningful supplement.

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 action ('Tell the Pipeworx team...') and clearly defines the resource (Pipeworx tooling). It enumerates feedback types (bug, feature/data_gap, praise) and explicitly distinguishes this from other MCP servers' feedback mechanisms, making it easy to select among siblings.

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 when-to-use conditions tied to feedback categories and a hard exclusion: only tools served by this Pipeworx connection; if the tool is from another MCP server, file it there. It also gives practical framing guidance (describe in terms of Pipeworx tools, don't paste the end-user prompt), which is especially helpful.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, even within clusters like Pipeworx queries (ask_pipeworx vs ask_pipeworx_grounded vs deep_research) and Polymarket tools (bet_research, arbitrage, edges, etc.). Overlap is minimal and explicitly addressed in descriptions.

Naming Consistency5/5

All tool names use snake_case, and most follow a consistent verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity). Exceptions like remember, reverse are still single words in the same style.

Tool Count4/5

32 tools is on the high side but justified by the wide scope: data querying, betting, memory, subscriptions, and utilities. Each tool earns its place, and the count is not excessive given the breadth.

Completeness4/5

The tool set covers the core workflows of querying structured data, comparing entities, validating claims, scanning dependencies, and monitoring, with few gaps (e.g., no direct data visualization). Minor gaps exist but are manageable.

Resources