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Biostudies

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 present but all false, offering no behavioral hints. The description compensates richly by disclosing rate limits (5 per identifier per day), the claim_token return mechanism, the fact that it's free and doesn't count against quota, and that the team reads digests daily with roadmap influence. This goes far beyond what annotations provide.

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 serves a purpose: purpose, usage, exclusions, token mechanics, rate limits, and incentives. It is front-loaded with the primary action and structured logically. Slight verbosity in the exclusion clauses is justified.

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 covers the key return value behavior (claim_token) and the two-mode operation (filing vs. reading a reply). It also addresses rate limits, scope boundaries, and content guidelines. For a 4-parameter tool with a nested context object, the description 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 baseline is 3. The description adds meaningful context for claim_token (how to use it to read replies) and message (advice to describe issues in terms of Pipeworx tools/packs and not paste end-user prompts). This enhances the schema's parameter descriptions.

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.' This clearly states the tool's purpose and scope (feedback about Pipeworx tools) and distinguishes it from sibling tools that are research/visibility oriented. The resource and intent are 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 when-to-use guidance with enumerated scenarios (bug, feature/data_gap, praise), a clear exclusion (only for tools served by this Pipeworx connection, not other MCP servers), and a concrete alternative action (file with that server instead). Also explains the claim_token workflow for following up, giving complete usage context.

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

A3.8/5.0
Disambiguation2/5

Several tools occupy nearly identical roles: ask_pipeworx and ask_pipeworx_beta are described as functionally identical right now, ask_pipeworx_grounded and deep_research are overlapping query modes, and ai_visibility_check / scan_competitor_ai_presence / discover_tools / suggest_questions all blur into discovery or visibility tasks. The two actual BioStudies tools are clear, but they are buried in a server dominated by Pipeworx meta-tools.

Naming Consistency3/5

All tool names use snake_case, and many follow a verb_noun shape such as search_studies, get_study, and discover_tools. However, the convention is inconsistent across the set: noun-first names like entity_profile and polymarket_edges, brand-prefixed names like pipeworx_feedback, and verb-first product names like ask_pipeworx all coexist, making the pattern harder to predict.

Tool Count2/5

33 tools is well into the too-many range, and the count is especially inappropriate for a server named Biostudies since only search_studies and get_study actually belong to that domain. The rest form a sprawling general-purpose data-research platform that appears to have been merged into one server without a clear scope.

Completeness3/5

For the BioStudies-specific surface, search_studies and get_study provide reasonable read-only coverage for the EBI archive. But as the broader research platform the other 31 tools imply, the set is hard to evaluate for completeness because most actual data access is delegated to Pipeworx's hidden 5,718 tools rather than exposed directly, leaving notable gaps in transparency and direct source-level control.

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