Skip to main content
Glama

Elife

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

A4.8/5.0
Behavior5/5

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

Even though annotations are all false, the description discloses substantial behavior: returns a claim_token for non-account filings, supports later status checks via pipeworx_feedback({claim_token:...}), rate limit of 5/day, free and not counted against quota, and team reading cadence with roadmap impact. It also warns against pasting prompts, which is a valuable behavioral constraint.

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 lengthy (~200 words) but each sentence contributes distinct information: purpose, use cases, scope boundaries, token flow, rate limit, and cost. It is front-loaded with the verb and immediate when-to-use. The verbosity is justified by the tool's nuanced rules, though it is not as minimal as the best two-sentence examples.

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?

With no output schema, the description adequately covers return behavior (claim_token and later status retrieval), limitations (rate limit, scope), and the human impact (team reads digests, roadmap influence). It provides all necessary context for an agent to correctly invoke the tool and understand its side effects.

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, but the description adds extra semantics beyond the schema: how to use claim_token (pass back as pipeworx_feedback({claim_token:'pwfb_…'})), and guidance to describe issues in terms of Pipeworx tools/packs rather than user prompts. These augment the parameter descriptions meaningfully.

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 phrase 'Tell the Pipeworx team something is broken, missing, or needs to exist,' clearly identifying the tool as a feedback-reporting mechanism. It distinguishes itself from sibling research/query tools by enumerating feedback types (bug, feature, data_gap, praise), making the purpose unambiguous.

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 'Use when' clauses for bug, feature, data_gap, and praise, and a clear when-not for feedback about tools from other MCP servers: 'ONLY for tools served by this Pipeworx connection... file it with that server instead.' This gives the agent precise decision rules for tool selection.

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

All Elife 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

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are differentiated by reliability mode; entity_profile vs compare_entities serve single vs multi-entity; and memorization, subscription, and search tools occupy separate operational niches. No two tools could be easily confused.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use verb_noun (ask_pipeworx, resolve_entity, validate_claim), others are noun phrases (polymarket_arbitrage, ai_visibility_check), and some mix verb+noun with underscores inconsistently (generate_llms_txt, scan_competitor_ai_presence). This lack of uniformity makes the surface harder to navigate.

Tool Count3/5

34 tools is borderline high. The server spans multiple domains (data query, prediction markets, eLife, memory, subscriptions), and each domain gets several tools, making the overall surface feel bloated. While individual tools are justified, the total count strains discoverability and hints at scope creep.

Completeness2/5

The tool set is incomplete relative to its stated breadth. For a server named 'Elife', only 3 tools actually serve eLife; the rest are dominated by Pipeworx and Polymarket. Within the query/data domain, coverage is deep but lacks write/modify tools. Prediction market analysis lacks execution tools (no order placement). This leaves clear gaps for agents that need to act on the data.

Resources