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Connecticut Open Data

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?

Beyond the sparse annotations (all false), the description discloses the claim_token return behavior, daily digest review, roadmap influence, rate limit of 5 per identifier per day, and that it's free. These are valuable behavioral traits not visible from annotations or schema.

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?

Description is long but every sentence adds purposeful information: what, when, exclusions, claim_token workflow, rate limits. It is well-structured with a logical flow, though slightly dense; still earns its length.

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?

The tool has no output schema, but the description compensates by explaining the claim_token response pattern and how to read it later. It also covers prerequisite knowledge (which tools are Pipeworx's), making the description fully self-sufficient for a novice agent.

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 semantics by explaining when to use claim_token (pass it back later with no other arguments to read status), and instructs not to paste end-user prompts in the message, which clarifies the intent beyond the schema's field descriptions.

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?

Description states exactly what the tool does: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It uses a specific verb (tell/send) and resource (Pipeworx feedback), and distinguishes itself from sibling tools by explicitly excluding feedback about other MCP servers and naming the connection's 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?

Provides explicit when-to-use guidance for bugs, feature/data_gap, and praise, plus a clear when-not-to-use: reporting issues for tools from other MCP servers. It also explains the claim_token flow for follow-up, giving complete usage context.

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
Disambiguation1/5

Multiple tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) have overlapping research purposes. Additionally, many Polymarket and Pipeworx-specific tools are unrelated to the Connecticut Open Data server name, causing confusion.

Naming Consistency2/5

Naming is highly inconsistent: some use snake_case (ask_pipeworx, resolve_entity), others use longer descriptive phrases (polymarket_fill_risk, scan_competitor_ai_presence), with no clear pattern.

Tool Count1/5

The server claims to be about Connecticut Open Data but includes 34 tools, only 3 of which (datasets, metadata, query) are relevant. The vast majority are unrelated Pipeworx/Prediction Market tools, making the size inappropriate.

Completeness1/5

For Connecticut Open Data coverage, only basic dataset search, metadata, and query tools exist. Missing common operations like data upload, schema modification, or API key management for the open data portal.

Resources