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Openfema

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.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds useful context: no PII, cached 5min-1h, self-aggregating, derived from CF analytics-engine. 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?

Description is concise, front-loaded with purpose, and each sentence adds value. No wasted words: states function, use cases, and caching.

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?

With one parameter, 100% schema coverage, and no output schema, the description fully covers return format (top tools, packs, call volume) and caching behavior. Complete for the tool's complexity.

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

Parameters3/5

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

Schema coverage is 100%, and the schema description already explains the window enum and its effect. Description adds caching detail but otherwise does not significantly enhance meaning beyond schema.

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?

Description clearly states it returns 'top tools, top packs, and total call volume' over a window. Distinguishes from siblings like 'discover_tools' by focusing on trending/usage data.

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?

Explicitly lists three use cases (discovering hot data sources, confirming canonical tools, aligning use case). Implies when not to use (e.g., for specific queries, use other tools).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Openfema toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools are near-duplicates or heavily overlapping: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, and ask_pipeworx_grounded, deep_research, and validate_claim all cover grounded-answer territory. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread, bet_research) also has fuzzy boundaries that would require careful reading to differentiate.

Naming Consistency2/5

Naming mixes several conventions: verb_noun (query_dataset, resolve_entity, validate_claim), noun phrases (entity_profile, disaster_declarations, deep_research), and branded prefixes (pipeworx_trending, pipeworx_feedback, polymarket_edges, polymarket_arbitrage). Some tools use scan_, some ask_, some list_, with no single predictable pattern across the set.

Tool Count2/5

At 34 tools, the set exceeds the 25+ 'too many' threshold and carries a lot of surface area. The server is named Openfema, yet only about three tools actually relate to FEMA data, making the count feel inflated relative to the stated name and purpose.

Completeness4/5

For its actual broad domain—a general structured-data research gateway—the surface is quite comprehensive: discovery, single lookups, grounded verification, deep multi-source research, entity resolution, comparisons, change feeds, memory, subscriptions, and prediction-market analysis are all covered. Minor gaps exist (e.g., no direct OpenFEMA dataset metadata beyond list_datasets, and some tools require accounts/paywalls), but agents can generally accomplish the intended workflows.

Resources