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Glama

Acled

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, etc.), the description discloses caching behavior (5min-1h), data source (CF analytics-engine), and privacy (no PII). 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?

Three well-structured sentences: first states core functionality, second lists use cases, third explains data source and caching. Front-loaded, no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, use cases, data source, caching, and privacy. It does not include an example output, but the mention of 'top tools, top packs, and total call volume' is sufficient given the straightforward nature of the tool and no output schema.

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?

The only parameter 'window' is fully described in the schema. The description adds context on how shorter vs longer windows affect results, providing extra meaning beyond the enum.

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 returns top tools, packs, and call volume over a window. It lists three specific use cases, distinguishing it from sibling tools like ask_pipeworx and discover_tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides three explicit use cases for when to use this tool (discovering hot data, confirming canonical tool, aligning use case). However, it does not explicitly state when not to use it or directly compare to specific siblings.

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

A3.7/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) which all serve similar data retrieval needs. Additionally, entity_profile, compare_entities, and recent_changes overlap in providing company information. Polymarket tools also have overlapping analysis functions. This can cause confusion for agents.

Naming Consistency2/5

Tool names are inconsistent in style and convention. Some use underscores (ai_visibility_check, ask_pipeworx), others are single words (forget, recall), and many lack a clear verb_noun pattern (pipeworx_feedback, polymarket_edges). This mixture of naming conventions reduces predictability.

Tool Count3/5

With 32 tools, the count is on the higher side but appropriate given the broad scope covering multiple domains (Pipeworx data, Polymarket betting, ACLED events, npm scanning, memory, etc.). However, some areas have only one or two tools, which feels sparse, and the overall set could be trimmed or better organized.

Completeness3/5

The tool set covers many domains but has notable gaps. For ACLED, only search and count tools exist without any update/delete capabilities. For Pipeworx, the tools are heavily read-focused with no apparent write operations. The broad scope makes completeness hard to assess, but some obvious lifecycle operations are missing.

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