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Obis

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?

The description adds substantial behavioral context beyond the sparse annotations: rate limiting ('Rate-limited to 5 per identifier per day'), free usage, the no-account claim_token flow, and the team's daily digest/roadmap signal. It also clarifies the scope boundary, so the agent knows what Pipeworx can and cannot fix.

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 long but every sentence earns its place: core purpose, then scope exclusions, then follow-up mechanics, then rate limits and cost. It is front-loaded with the primary action and structured logically, with no filler.

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?

With no output schema and minimal annotations, the description carries the full burden — and succeeds. It covers when to use, when not to use, how to structure feedback, what the artifact of filing is, how to retrieve resolution status, and rate-limit/cost constraints. The agent can invoke and follow up confidently.

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%, establishing a baseline of 3. The description adds meaningful param-level value by explaining the claim_token lifecycle ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})'), the fact that filing without an account returns the token, and by reinforcing which type enum value maps to which situation.

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 enumerates feedback categories (bug, feature, data_gap, praise) and clearly distinguishes this tool from sibling query/research tools.

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?

It explicitly states when to use ('wrong/stale data', missing tool, praise) and when not to use: 'ONLY for tools served by this Pipeworx connection' — directing other-server issues elsewhere. It even explains the claim_token follow-up flow, giving clear operational guidance.

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
Disambiguation3/5

Most tools have distinct purposes and the descriptions are unusually thorough, but ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and several discovery/prediction-market tools (polymarket_edges vs polymarket_arbitrage, discover_tools vs suggest_questions) occupy overlapping territory. An agent could reasonably route to the wrong variant despite the documentation.

Naming Consistency4/5

The dominant convention is lowercase snake_case with a leading verb (ask_pipeworx, list_subscriptions, validate_claim, resolve_entity), which makes the set predictable. A few noun-first outliers like pipeworx_trending, recent_alerts, and polymarket_edge_tracker are minor deviations rather than a broken pattern.

Tool Count2/5

34 tools is well above the coherence sweet spot for an MCP server, and much of that count is made up of meta-wrappers and convenience variants around the same Pipeworx router. The broad scope explains some of the count, but the surface still feels heavy for a single named server.

Completeness3/5

The Pipeworx side is very complete: lookups, grounded verification, research, entity profiles, comparisons, prediction-market fill checks, subscriptions, and memory all have lifecycle coverage. However, the server is named Obis and only find_occurrences, get_statistics, and get_taxon serve that domain, leaving obvious marine-biodiversity operations like occurrence detail, dataset listing, and download paths missing.

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