Skip to main content
Glama

Ieee Standards

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

A4.9/5.0
Behavior5/5

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

Despite annotations being all false (offering little signal), the description richly discloses behavior: rate-limited to 5 per identifier per day, free/no quota impact, the claim_token workflow for tracking resolution, and that the team reads digests daily. This goes well beyond the annotations and sets accurate 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, use cases, exclusion, mechanics, claim_token flow, rate limits, and quota. It is front-loaded with the core purpose and then methodically covers the nuanced edge cases a feedback tool needs.

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 4 parameters, nested context, no output schema, and nuanced routing rules, the description is exceptionally complete. It covers what happens with/without an account, how to read back status, what is appropriate to report, and constraints like rate limits—leaving no significant gap.

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 operational value beyond the schema by explaining the claim_token round-trip ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})') and by coaching message content ('Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt').

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 clear, specific verb-resource pair: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It also immediately distinguishes this tool from sibling research/query tools by framing it as the feedback channel for the 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 enumerates when to use the tool (bug, feature/data_gap, praise) and, crucially, when not to use it: 'ONLY for tools served by this Pipeworx connection' and directs users to file elsewhere for other MCP servers. It even offers a disambiguation strategy ('Pipeworx tool names are the ones this connection lists').

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

All Ieee Standards toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tools blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer factual questions with overlapping routing behavior, while polymarket_edges, polymarket_arbitrage, and bet_research all surface prediction-market opportunities. Long descriptions help, but the boundaries between these clusters are genuinely unclear, and the three IEEE tools are buried in a sea of unrelated Pipeworx tools.

Naming Consistency4/5

The naming is predominantly snake_case with clear family prefixes like ieee_, ask_pipeworx_, and polymarket_, and most tools follow a readable verb_noun or noun_verb shape. Minor deviations exist (remember/recall/forget, bet_research, pipeworx_trending) but there is no camelCase/mixed-convention problem, so the overall pattern is predictable.

Tool Count1/5

A server named 'Ieee Standards' exposes 34 tools, yet only three of them (ieee_search, ieee_standard_search, ieee_article) actually serve that domain. The remaining 31 tools form a general Pipeworx data, prediction-market, memory, and subscription platform, which is an extreme scope mismatch for the stated server purpose.

Completeness4/5

For the IEEE lookup domain, the three IEEE tools cover the core read-only workflow: broad corpus search, standards-specific search, and full metadata retrieval by article number or DOI. Minor gaps exist (browsing by committee, revision/status history, full-text access) but those are workable or inherently restricted by IEEE's paywall.

Resources