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Foodish

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

A4.9/5.0
Behavior5/5

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

With all annotations false, the description carries full responsibility for behavioral disclosure and exceeds expectations. It discloses rate limiting ('Rate-limited to 5 per identifier per day'), the claim_token workflow ('Filing without an account returns a claim_token; pass it back later... to read whether it was fixed'), quota independence ('Free; doesn't count against your tool-call quota'), and a content caution ('don't paste the end-user's prompt').

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: purpose, use cases, exclusion, guidance, workflow, and constraints. It front-loads the core action and then layers structured detail without redundancy.

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 and all-false annotations, the description covers all essential context: what triggers feedback, what distinguishes this tool from siblings, what information to include, how the claim_token follow-up works, and operational limits. No critical gaps remain.

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%, earning a baseline of 3. The description adds meaningful semantics beyond the schema by explaining the claim_token lifecycle and the expected referential context ('Describe the issue in terms of Pipeworx tools/packs'), which reinforces how to populate the context and message parameters.

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 verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes this feedback tool from siblings by scoping it to 'ONLY for tools served by this Pipeworx connection' and contrasting with other MCP servers' connectors.

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 cases: '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).' It also gives a clear exclusion ('if the tool came from a different MCP server... file it with that server instead') and a disambiguation rule ('Not sure? 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.

All Foodish toolsInspect
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TDQS

A3.8/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose, with detailed descriptions that differentiate between similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. The Polymarket-related tools each focus on a specific aspect (arbitrage, edges, tracking, fill risk, cross-venue spread), and memory/subscription tools are neatly separated.

Naming Consistency4/5

Most tool names follow a verb_noun or noun_verb pattern with underscores (e.g., ask_pipeworx, validate_claim, resolve_entity). However, there is some inconsistency: single-word names like 'forget' and 'random' mix with multi-word names, and a few names use different structures (e.g., bet_research as noun_noun, random_by_category as adjective_preposition).

Tool Count3/5

32 tools is on the high side for a single server, covering a broad range of functionalities from data queries to betting and memory. While the number might be justified by the platform's scope, it feels heavy, and the server name 'Foodish' suggests a narrower food-focused purpose, creating a mismatch.

Completeness4/5

As a general data platform, the tool set is comprehensive, covering queries, research, entity resolution, memory, subscriptions, and various analytical tools. Minor gaps exist (e.g., no direct editing or upload capabilities), but the core workflows are well-supported. However, the server name 'Foodish' implies food-related tools, which are minimal, so completeness relative to the name is poor.

Resources