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Glama

Openfema

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details multi-source fan-out, fallback mechanisms, soft-fails, and return format, giving full behavioral insight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is well-structured and informative, though slightly verbose; each sentence contributes meaning, but could be tightened slightly.

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 no output schema, the description adequately explains return format and tool behavior, covering all essential aspects for an agent to use it correctly.

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 adds significant value beyond the already high-coverage schema, explaining relative shorthand for 'since', ticker/CIK options for 'value', and constraints for 'type'.

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 in a recent window, with specific verb phrases like 'What's new with X' and explicitly distinguishes from the sibling tool entity_profile.

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 example query phrasings and clearly states when to use entity_profile instead, offering precise usage context.

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

A3.6/5.0
Disambiguation2/5

The tool set has several overlapping families: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, the discovery tools (list_datasets, discover_tools, suggest_questions) all serve a 'what can I do here' purpose, and ai_visibility_check is wrapped by scan_competitor_ai_presence. The polymarket_* tools are well-differentiated, but the heavy overlap in the meta-tools makes selection error-prone.

Naming Consistency2/5

Naming is a mix of conventions with no unifying pattern: family prefixes appear as ask_pipeworx_*, pipeworx_*, and polymarket_*, while unrelated tools use bare nouns (entity_profile, recent_changes), verb-first names (validate_claim, search_within), and inconsistent styles. The three actual FEMA tools (disaster_declarations, list_datasets, query_dataset) share no prefix that ties them to the server's stated name.

Tool Count2/5

34 tools exceeds the 'too many' threshold, and the count is unjustified by the server's apparent scope: only 3 of 34 tools relate to OpenFEMA data, with the remaining 31 being a grab-bag of Pipeworx routing, Polymarket betting, memory, subscription, and AI-visibility utilities. The bulk is either redundant with the meta-routers or off-domain for a server named 'Openfema'.

Completeness2/5

For FEMA specifically, list_datasets + query_dataset covers generic read-only access and disaster_declarations adds a convenience wrapper, but the domain is extremely thin and lacks FEMA-specific conveniences (e.g., geographic aggregation, multi-dataset joins, incident summaries). For the broader Pipeworx universe the routing coverage is actually decent, but that makes the FEMA-named server's surface feel incoherent — an agent expecting a FEMA toolset finds most of its value in unrelated prediction-market and brand-visibility tools.

Resources