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Glama

universities

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

A4.8/5.0
Behavior5/5

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

Annotations provide no safety profile (all hints false), so the description carries the full burden. It compensates comprehensively by disclosing rate limiting (5 per identifier per day), cost (free, doesn't count against quota), return behavior (claim_token), and post-submission workflow (digests read daily, signal affects roadmap). 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.

Conciseness4/5

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

The description is longer than average but every sentence contributes operational insight (scope limits, filing rules, claim flow, rate limits, cost). It is front-loaded with the core purpose and organized by usage conditions, making it efficient despite 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?

Given the tool's nuanced purpose, nested parameters, and lack of an output schema, the description is thorough. It covers the complete workflow (submit, receive token, check status), including edge cases like anonymous filing, and provides enough context for the agent to invoke correctly without consulting external documentation.

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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining how to use claim_token ('pass it back later to read whether it was fixed') and clarifying message expectations ('don't paste the end-user's prompt'). It enhances parameter understanding without needing to compensate for gaps.

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 clearly identifies the actions of the tool: sending feedback to the Pipeworx team (bug, feature, praise). It specifies the exact target (Pipeworx connection tools) and differentiates from sibling tools like ask_pipeworx and discover_tools, stating 'ONLY for tools served by this Pipeworx connection.'

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?

The description explicitly states when to use the tool (bug, feature/data_gap, praise), when NOT to use it (different MCP server), and provides a clear decision heuristic ('Not sure? Pipeworx tool names are the ones this connection lists'). It also explains how to check status later using the claim_token, which is actionable guidance beyond basic triggers.

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
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers, and the five polymarket_* tools all deal with prediction-market edge detection and filling risk. Memory and subscription tools are clear, but the data-access and research tools require careful reading to avoid selecting the wrong entry point.

Naming Consistency3/5

All names use snake_case and many follow a verb_noun pattern such as search_universities and resolve_entity, but the set mixes product-prefixed names (polymarket_*, pipeworx_*), bare verbs (remember, recall, forget), and noun phrases (entity_profile, deep_research). The ask_pipeworx_beta suffix also introduces a naming convention not used elsewhere.

Tool Count2/5

At 32 tools, the server exceeds the heavy threshold, and almost all tools are unrelated to the apparent 'universities' domain—only search_universities matches the server name. The count might suit a broad data-research platform, but it is poorly scoped for this server's stated identity.

Completeness1/5

For the domain implied by the server name, the surface is severely incomplete: only a name/country university search exists, with no university detail, ranking, program, admissions, or comparison coverage. Agents would dead-end immediately after finding a list of universities.

Resources