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Glama

Corporate Apology

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations provide no positive hints (all false), so the description carries the full burden. It discloses the claim_token return-and-follow-up workflow, rate limiting ('5 per identifier per day'), quota impact ('Free; doesn't count against your tool-call quota'), and processing expectations ('team reads digests daily'). This is rich behavioral context beyond what annotations convey.

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?

Though longer than typical descriptions, every sentence earns its place: purpose, usage categories, exclusion, identification heuristic, content guideline, claim_token workflow, processing expectation, rate limit, and quota note. It is dense, front-loaded with purpose, and contains no redundant or filler text for a tool with this complexity.

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 description is complete given the tool's complexity: it covers the full lifecycle (submitting feedback, receiving a claim_token, later checking resolution), constraints (rate limit, quota), and proper scope. No output schema exists, but the description compensates by explaining the claim_token return and its use. The nested context object is adequately covered by the schema.

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 parameters are already well described. The description adds extra semantic value by explaining the claim_token usage pattern ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})') and by stipulating message content ('don't paste the end-user's prompt'). These details go beyond the schema descriptions, though the schema already carries the core meaning.

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 and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly identifies this as a feedback channel for the Pipeworx tool connection, distinguishing it from sibling tools like 'ask_pipeworx' or 'deep_research' by enumerating exact feedback categories (bug, feature/data_gap, praise).

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: uses cases for bug, feature/data_gap, and praise. It also gives an exclusion rule: 'ONLY for tools served by this Pipeworx connection' and instructs to file with another MCP server if the tool is not Pipeworx. It even offers a disambiguation heuristic ('Not sure? Pipeworx tool names are the ones this connection lists.'), which is excellent for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Corporate Apology toolsInspect
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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. Some overlap exists in the Polymarket-related tools, but each has a specific focus (arbitrage detection, edge scanning, persistence tracking, fill risk, cross-venue spread). The two ask_pipeworx variants are similar but differentiated by hallucination resistance.

Naming Consistency5/5

All tool names use lowercase with underscores, following a consistent pattern of verb_noun or noun_verb. Examples include 'ai_visibility_check', 'bet_research', 'entity_profile', and 'validate_claim'. There are no mixed conventions or erratic naming.

Tool Count2/5

The server is named 'Corporate Apology' but contains 31 tools, only one of which (corporate_apology_generate) relates to apologies. The vast majority are unrelated data retrieval and analysis tools, making the count excessive and poorly scoped for the server's stated purpose.

Completeness1/5

For a server focused on corporate apologies, the only tool is corporate_apology_generate. There are no tools for analyzing apology impact, managing crisis response, or tracking apologies. The tool surface is severely incomplete relative to the server's name.

Resources