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Glama

Maryland 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / claim_token
      Added value: +{
+  "description": "Read 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.",
+  "type": "string"
+}
    • removedInput schema / required
      Removed value: -[
-  "type",
-  "message"
-]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the neutral annotations (all false), the description discloses rate limits (5 per identifier per day), pricing (free, doesn't count against quota), the claim_token mechanism for later retrieval, and the human review process (daily digests, roadmap impact). This gives the agent a clear model of side effects and expectations.

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: purpose, exclusions, framing advice, claim_token flow, rate limits, pricing, and roadmap impact. It is front-loaded with the core purpose and logically structured, with no redundant phrases or vague 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?

For a tool with no output schema and a nested context object, the description fully covers the feedback lifecycle: how to submit, what to expect (claim_token), how to check resolution, and the human-side commitment (daily digests). The agent has everything needed 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%, so the baseline is 3. The description adds meaningful guidance for `message` (be specific, 1-2 sentences, 2000 chars max) and `claim_token` (pass it back later with no other arguments), though much of the claim_token behavior is already in the schema description. It improves but doesn't drastically transcend the schema.

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') and immediately names concrete categories (bug, feature/data_gap, praise). It also scopes the tool exclusively to Pipeworx connection tools, making it unambiguous versus any sibling feedback or support tool.

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 each feedback type and an explicit when-not-to-use for tools from other MCP servers, even directing the agent to file those elsewhere. It also gives practical framing advice ('describe in terms of Pipeworx tools/packs — don't paste the end-user's prompt'), which is actionable and distinct from sibling tools.

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

A3.7/5.0
Disambiguation3/5

Tools are generally distinct in purpose, but the server name 'Maryland Open Data' conflicts with the inclusion of many unrelated Pipeworx tools (e.g., prediction market tools). This creates ambiguity about the server's actual domain, making it hard for agents to know what to expect.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others are plain (datasets, query), and some are descriptive phrases (ask_pipeworx_grounded). No consistent verb_noun pattern emerges, leading to a chaotic feel.

Tool Count2/5

33 tools is high, and the majority are unrelated to Maryland Open Data, suggesting scope creep. The server tries to be a general-purpose data platform but is named after a specific dataset, making the count feel excessive and unfocused.

Completeness3/5

The Maryland Open Data subset is minimal (3 tools: datasets, metadata, query), lacking update/delete/CRUD operations. The broader set includes many query and analysis tools, but the server's stated purpose is not fully covered.

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