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Openfema

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations provide idempotentHint and destructiveHint. Description adds context beyond: scope by agent identifier, persistence difference between authenticated and anonymous sessions (24hr), and that it stores key-value pairs. This is valuable for the agent.

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?

Highly concise and well-structured. Four sentences front-load the main purpose, then usage, then retention details. No wasted words.

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 simplicity (2 required string params, no output schema), the description is complete: explains what it does, when to use, how storage works, and links to sibling tools. No gaps.

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% with clear descriptions for key and value. The description adds usage examples but does not provide additional semantic meaning beyond what the schema already offers. Adequate but no extra value.

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 verb 'Save data' and the resource 'the agent will need to reuse later'. It provides concrete examples (resolved ticker, target address, user preference) and distinguishes from sibling tools 'recall' and 'forget' by focusing on storing.

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?

Explicit guidance on when to use: 'when you discover something worth carrying forward... so you don't have to look it up again'. Also mentions pairing with recall and forget. Lacks explicit when-not to use, but context is sufficient.

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 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.

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