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Glama

Nsf Awards

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), description discloses fans-out to multiple sources, fallback logic, soft-fail for USPTO, and return structure (changes grouped by source, total_changes, citations). 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.

Conciseness4/5

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

Description is moderately long but front-loaded with query examples. Each sentence adds value (usage, sources, parameter guidance, alternative tool). Slightly verbose but well-structured.

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?

Despite no output schema, description completely explains return format (changes grouped by source, total_changes, citations) and covers edge cases (USPTO soft-fail). For a multi-source tool with 3 required params, this is exhaustive.

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?

Schema coverage is 100%, but description adds significant value: explains 'since' formats (ISO date, relative shorthand) with examples, recommends '30d' or '1m' for monitoring, clarifies 'value' accepts ticker or CIK. No ambiguity.

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 is a 'change feed for a company' and distinguishes from sibling 'entity_profile' by contrasting static profile vs. change feed. It also maps to user queries like 'what's new with X', making purpose explicit.

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 examples (e.g., 'latest on Y', 'updates on Acme') and when-not-to-use ('Use entity_profile instead for static profile'). Also details fallback behavior (GDELT→GNews) and window parameter interpretation.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, especially the various ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) which are confusingly similar. Also, memory tools (remember, recall, forget) and subscription management tools (subscribe, unsubscribe, list_subscriptions, recent_alerts) add to the ambiguity.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check, ask_pipeworx), descriptive phrases (scan_competitor_ai_presence), and single verbs (forget, recall). No clear pattern emerges.

Tool Count2/5

33 tools is excessive for a server named 'Nsf Awards'. The majority of tools are unrelated to NSF awards and belong to a general data platform. The scope is far too broad.

Completeness1/5

For the domain of NSF awards, there are only two tools (search_awards and get_award), lacking any CRUD operations or other common functionality. The server is severely incomplete for its stated purpose.

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