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Biosamples

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?

Annotations are all false hints, providing no positive safety/behavior profile, so the description carries the full burden. It discloses rate limiting (5/day), quota exemption, return of a claim_token, the follow-up mechanism with the token, and the team's daily review process—substantial behavioral context beyond 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 adds operational value (usage, exclusions, token workflow, rate limits). It is front-loaded with purpose and usage, then proceeds to behavioral details. Slightly dense but appropriately so for a tool with a multi-step follow-up workflow.

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 the return behavior (claim_token) and the subsequent read flow, which is essential for using the tool correctly. It also covers prerequisites (n/a), constraints, and limitations, making it complete for the tool's complexity.

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 baseline is 3. The description adds value beyond schema by explaining the type categories with concrete examples, clarifying message content ("Don't paste the end-user's prompt"), and detailing how claim_token should be used as a read-the-status follow-up. It doesn't cover the context sub-object, but schema already does that.

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+resource: "Tell the Pipeworx team something is broken, missing, or needs to exist." It cleanly delineates the tool's scope and explicitly distinguishes it from siblings by stating it is ONLY for tools served by this Pipeworx connection, not other MCP servers.

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: bug, feature/data_gap, or praise. It also gives a clear exclusion (other MCP servers) and a disambiguation heuristic ("Pipeworx tool names are the ones this connection lists"), making it easy to decide between this and alternatives.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical to ask_pipeworx right now), and ask_pipeworx_grounded are three variants of the same router, while six polymarket_* tools plus bet_research all target prediction-market edge discovery. ai_visibility_check and scan_competitor_ai_presence further overlap. Only a minority of the 33 tools have clearly distinct purposes.

Naming Consistency3/5

snake_case is used throughout, and the polymarket_/pipeworx_ prefixes are internally consistent, but the naming convention mixes verb_noun (ask_pipeworx, search_samples, resolve_entity, validate_claim) with noun-first names (entity_profile, recent_changes, bet_research) and ad-hoc names like discover_tools or generate_llms_txt. Readable, but no single predictable pattern.

Tool Count1/5

33 tools for a server named 'Biosamples' is an extreme scope mismatch: only 2 of 33 tools (get_sample, search_samples) relate to biological samples, with the remaining 31 forming an unrelated kitchen sink of Pipeworx data routing, Polymarket betting, npm dependency checks, AI visibility audits, memory storage, and subscription management. The count is far beyond anything the stated purpose justifies.

Completeness2/5

For the actual Biosamples domain, search + get covers read-only access but no submission, annotation, or batch workflows, and the server's stated purpose is drowned out by unrelated domains that are only partially covered. The surface is simultaneously bloated with 31 irrelevant tools and thin on the one domain the server name promises, making coherent lifecycle coverage impossible to assess.

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