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Glama

Pubmed

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. Added

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses rate-limit fallback (GDELT → GNews), soft-failure of the PatentsView API due to sunset, output structure (changes[], total_changes, citation URIs), and parameter semantics. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence is informative: query examples, source behavior, fallback details, API sunset note, output summary, and alternative tool. It is front-loaded with use cases and uses a readable, structured flow despite being a single paragraph.

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—multi-source fan-out, fallback logic, parameter nuances, and no output schema—the description fully covers what the agent needs to know: return shape, source-specific behaviors, time-window parsing, and when to prefer another tool. No significant gaps remain.

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?

The description explains `since` accepts ISO dates or relative shorthand ('7d', '3m') and recommends typical monitoring values ('30d' or '1m'). It also clarifies `value` can be a ticker or zero-padded CIK. This adds significant meaning beyond the schema, which already covers all parameters.

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 it provides a change feed for a company over a time window, using multiple query phrasings. It explicitly distinguishes itself from entity_profile, which focuses on static profiles, making it easy to know when to choose this tool.

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 is explicit about when to use this tool (recent changes over N days/weeks/months) and gives an exclusion: use entity_profile instead when wanting a static profile. It also explains the source fan-out and fallback behavior, helping the agent decide appropriately.

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 tool groups overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions against the same data sources, and the Polymarket suite (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) has unclear boundaries. An agent would struggle to pick the right one.

Naming Consistency2/5

Names mix verb phrases (search_pubmed, get_abstract, validate_claim) with noun phrases (entity_profile, polymarket_arbitrage, bet_research) and bare verbs (remember, forget). The ask_pipeworx family uses a non-standard prefix, and there's no consistent verb_noun pattern across the set.

Tool Count2/5

37 tools is far too many for a server that presents as a PubMed tool. The majority are unrelated to biomedical literature (memory, subscriptions, prediction markets, real estate, etc.), making the surface feel bloated and unfocused.

Completeness4/5

The PubMed-specific tools form a complete lifecycle: search, citation metadata, abstract, full text, forward citations, and related articles. However, the server's broader domain is unclear and unevenly covered — many non-PubMed areas have partial coverage.

Resources