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Clinicaltrials

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?

The description goes well beyond the sparse annotations. It discloses the claim_token lifecycle (filing without an account returns a token; passing it back reads status), rate limits (5 per identifier per day), free usage, and that the team reads digests daily. It also advises not to paste end-user prompts, which is a behavioral trait not captured anywhere else.

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 dense and front-loaded with the primary purpose. It is longer than a typical two-sentence description but every sentence adds relevant detail (conditions, exclusions, token flow, rate limit). A couple of sentences ('The team reads digests daily...') are motivational rather than strictly operational, but they do not detract from usefulness.

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?

Despite having no output schema, the description fully explains what the tool returns: a claim_token when filing without an account, and status/resolution when using that token. It covers all input types (bug, feature, data_gap, praise), specifies exclusions, and gives lifecycle instructions. For a feedback tool with four optional parameters, this is complete.

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 meaningful context around claim_token usage ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})') and emphasizes message specificity, but the schema already describes the parameters well. The slight extra guidance on the feedback workflow justifies 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 a specific verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes this from any generic feedback tool by restricting to 'tools served by this Pipeworx connection' and contrasting with other MCP servers. The listed uses (bug, feature, data_gap, praise) make the purpose unmistakable.

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?

Explicit when-to-use guidance is provided: '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).' It also states when NOT to use it by directing feedback for other servers elsewhere, and how to identify Pipeworx tools. This is clear and actionable.

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

All Clinicaltrials toolsInspect
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TDQS

A3.6/5.0
Disambiguation1/5

Several tools appear to do the same thing at the top level: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are near-duplicate routing entry points, and deep_research overlaps heavily with them. Among the ct_* tools, ct_count_by_condition, ct_competitive_landscape, ct_sponsor_pipeline, and ct_compare_sponsors all provide overlapping counting/landscape functionality, making correct selection genuinely ambiguous.

Naming Consistency4/5

The overwhelming majority of tools use lowercase snake_case and mostly follow a verb_noun or domain-prefixed pattern (ct_search, ct_get_study, list_subscriptions, validate_claim, resolove_entity). Some names are noun phrases rather than verbs (ct_competitive_landscape, entity_profile, polymarket_edge_tracker) but the overall style is consistent and readable, with only minor deviations.

Tool Count2/5

44 tools is far too many for a server named 'Clinicaltrials'; only 13 tools are actually clinical-trials-specific while the rest span general data lookup, prediction markets, memory, subscriptions, and npm scanning. The count is inflated by redundant entry points (ask_pipeworx/beta/grounded) and overlapping ct tools, making the set feel heavy and unfocused.

Completeness4/5

For the clinical-trials registry domain, the surface is largely complete: search, full study details, results summaries, condition counts, sponsor pipelines, location-based lookup, recent updates, and catalyst tracking are all represented. Minor gaps exist (e.g., historical versions/protocol amendments and advanced filter combinations), but most could be worked around via the universal ask_pipeworx router.

Resources