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Glama

Openweather

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?

Despite minimal annotations, the description discloses the claim_token return mechanism, follow-up usage, rate limiting ('Rate-limited to 5 per identifier per day'), cost ('Free; doesn't count against your tool-call quota'), and human-read cadence ('team reads digests daily'). 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.

Conciseness5/5

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

Dense but front-loaded single paragraph; the opening sentence states the core purpose, and every subsequent sentence covers a distinct necessary fact (scope, usage, follow-up, limits). No filler or repetition.

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 the full workflow: when to file, what to include, how to follow up on an existing report, rate limits, and cost. No output schema, but the token-based response behavior is fully described, so an agent knows what to expect.

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?

Input schema already describes all 4 params with 100% coverage, so baseline 3. Description adds message guidance ('don't paste the end-user's prompt') and clarifies claim_token usage and exclusivity, earning a 4.

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 'Tell the Pipeworx team something is broken, missing, or needs to exist,' a specific verb+resource+intent. It defines the four feedback types and explicitly distinguishes this tool from other Pipeworx tools, even addressing MCP server boundaries.

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?

Explicitly states when to use ('Use when a tool returns wrong/stale data... feature/data_gap... praise') and when not to ('if the tool came from a different MCP server... file it with that server instead'). Even includes 'Not sure?' fallback.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-duplicates or heavily overlapping: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and bet_research all route natural-language queries to structured data, with only subtle differences; ai_visibility_check and scan_competitor_ai_presence also overlap significantly. An agent could easily pick the wrong one.

Naming Consistency2/5

No consistent naming pattern: most tools use lowercase snake_case, but some are noun-first (current_weather, entity_profile), some use brand prefixes (ask_pipeworx, polymarket_*), and 'generate_llms_txt' and 'scan_competitor_ai_presence' are verbose and stylistically inconsistent.

Tool Count3/5

30 tools is above the well-scoped range and feels heavy, though not absurd for a broad multi-domain data platform. The count could be reduced by consolidating near-duplicate query/research tools.

Completeness4/5

The set covers a wide range of domains: weather, SEC/financials, drugs, real estate, prediction markets, news, patents, memory, and subscriptions. Some minor gaps exist (e.g., no list/get subscription tool, no weather alerts), but the main functionality is well covered.

Resources