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Glama

Chembl

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?

Goes beyond annotations by describing fan-out behavior, GDELT→GNews fallback logic, and USPTO soft-fail due to PatentsView sunset. Also discloses return structure (changes[], total_changes, citation URIs). No contradiction with annotations.

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?

Dense but efficient: starts with concrete user intents, defines the tool, lists sources, explains `since`, then describes output and alternative. Every sentence contributes unique information without redundancy.

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 multi-source complexity, the description covers all essential aspects: what sources are queried, fallback/soft-fail behavior, the return format, and when to use a different tool. No output schema exists, so this description fully compensates.

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 a 3 is baseline. The description adds value by explaining `since` formats (ISO vs relative) and recommending '30d' or '1m' for typical monitoring. It also confirms `value` can be ticker or CIK with examples.

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 time window, listing specific sources (SEC EDGAR, GDELT/GNews, USPTO). It distinguishes itself from sibling entity_profile by explicitly naming when to use that alternative.

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 context: 'What's new with X' / 'latest on Y' and notes it does this in ONE parallel call. It also gives an explicit exclusion: 'Use entity_profile instead when you want the static profile... regardless of window.'

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

B3.4/5.0
Disambiguation2/5

The tool list mixes a small ChEMBL dataset with a large Pipeworx toolkit, and several Pipeworx tools are near-duplicates (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). An agent would struggle to pick the right tool among overlapping prediction-market and research tools, and the ChEMBL tools are buried under irrelevant functionality.

Naming Consistency2/5

Naming conventions are mixed: ChEMBL tools use bare nouns (molecule, target, activities) while Pipeworx tools use inconsistent verb_noun phrases (ask_pipeworx, validate_claim) and noun phrases (entity_profile, recent_changes). There is no predictable pattern across the set.

Tool Count2/5

37 tools is far too many for a server named 'Chembl', especially since the majority are unrelated Pipeworx features. The count is justified neither by the apparent ChEMBL scope nor by a coherent overall purpose, making the server feel bloated and unfocused.

Completeness3/5

The ChEMBL subset is reasonably complete (search, molecule, target, activities, mechanism, drug_indications), and the Pipeworx side includes broad research/data tools, but the set lacks a unified purpose. Gaps include no direct assay/detail retrieval and no coherent lifecycle across the mixed domains.

Resources