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Glama

Biorxiv

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. The description adds significant detail: data sources (SEC EDGAR, GDELT→GNews fallback, USPTO), fallback logic, soft-fail behavior, and return format (changes grouped by source, total_changes count, citation URIs). No contradictions.

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 but front-loaded with common use cases. It efficiently packs multiple pieces of information into one paragraph, though slightly less structured than ideal.

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?

Given the tool's complexity (multiple data sources, fallback, soft-fail, no output schema), the description is complete. It explains the return structure, parameter details, and when to use alternatives.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds context beyond schema: explains `since` with examples like '7d', '30d', '3m', '1y' and suggests typical monitoring value; clarifies `value` accepts ticker or CIK; notes `type` currently only supports 'company'.

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 clearly states the tool provides a change feed for a company over a recent window, with examples like 'What's new with X'. It explicitly distinguishes from sibling 'entity_profile', which gives static profiles.

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 provides explicit when-to-use guidance with example queries and explicitly contrasts with 'entity_profile' for static needs, stating 'Use entity_profile instead when you want the static profile'.

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

B3.1/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve querying/research, while entity_profile, compare_entities, and recent_changes overlap on company information. The bioRxiv-specific tools are distinct but are swamped by generic Pipeworx tools, making selection ambiguous.

Naming Consistency2/5

Though all names are snake_case, there is no consistent verb-noun pattern. Some tools are verbs (remember, recall, forget), some are nouns (details, summary, publisher), and the ask_pipeworx family and meta-tools like discover_tools, suggest_questions mix styles. The naming feels ad hoc rather than following a clear convention.

Tool Count2/5

35 tools is excessive for a server named 'Biorxiv' — only 4-5 tools relate to bioRxiv directly, while the rest are general-purpose Pipeworx data and monitoring tools. This is a severe mismatch between server name and scope, bloating the tool surface unnecessarily.

Completeness2/5

For the stated bioRxiv purpose, the surface is incomplete: there is no search-by-topic, author, or abstract, and no way to retrieve full preprint text. The server compensates with many unrelated tools, but the core bioRxiv domain lacks basic coverage like searching preprints.

Resources